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ASSESSING GENERAL MOTOR ABILITY
AND TESTS FOR TALENT IDENTIFICATION OF MALAYSIAN ADOLESCENTS
By
Halijah Ibrahim
0179666
This thesis is presented for the degree of
Doctor of Philosophy
School of Sport Science, Exercise and Health
Faculty of Life and Physical Sciences
The University of Western Australia
November 2009
ii
ABSTRACT
Talent Identification (TI) in sports begins by mass screening individuals’ motor abilities. du
Randt (2000) wrote that, as test items from one country might not necessarily suit another,
appropriate basic motor skill test items are important for developing a TI mass screening
instrument. Three hundred and thirty Malaysian adolescents aged from 12-15 years were
tested on three motor skill test batteries: the McCarron Assessment of Neuromuscular
Development (MAND, McCarron, 1982); the Australian Talent Identification Test (AIS,
Australian Sports Commission, 1998); and a Balance and Movement Coordination Test
which was specifically developed for this project. In the current research, the motor
performance data recorded from the adolescents underwent several types of analyses.
Principal Component analyses were conducted on the MAND, AIS and BMC motor skill
instruments to understand what the three motor skill instruments were assessing globally in
the Malaysian adolescents. Then, first-order and higher-order factor analyses were
conducted on the 13 parameters making up the AIS+BMC motor skill instrument to examine
the concept of general motor ability (GMA). After descriptive analyses of the adolescents’
motor skill performances, age and gender differences were examined using two (gender) by
four (age) ANOVAs. Finally, stepwise discriminant function analyses were conducted on a
combined AIS+BMC motor skill instrument to determine the best sub-set of motor skills that
reliably classified the Malaysian adolescents into three levels of motor performance.
Principal Component analyses on the three motor skill instruments among all participants
demonstrated that the MAND assessed three motor abilities - postural control, bi-manual
dexterity and muscle power. Only one motor ability was found to be assessed by the AIS
motor skill instrument, that of anaerobic power. Finally, the BMC assessed movement
coordination, postural control and static balance. These findings did not correspond with the
hypothesised factor structures. These instruments need to be carefully examined because
what they assess appears to change with the population under investigation.
Higher-order factor analyses were performed separately on all subjects to test for a motoric
‘g’. The motor abilities of movement coordination and postural control were found for both
girls and boys, and some balancing ability emerged. Girls exhibited static balance, whereas
boys recorded a more general balance factor named kinaesthetic integration, and explosive
power. Hence, when assessing motor skill, power appears to be relevant for adolescent boys.
The descriptive analyses indicated that the participants increased in height, weight and BMI
across gender and age. Results also demonstrated no significant interactions between gender
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and age on fine motor skills of the MAND. However, significant interactions between
gender and age were shown on the MANDs gross motor skills of grip strength, and finger-
nose-finger, with varied performances reported for the boys and girls across the age groups.
For the gross motor skills of jumping and heel-and-toe the boys outperformed the girls. A
gender-by-age interaction was also reported for the AIS motor skill of basketball throw with
the older boy and girl age groups throwing further; particularly the 14- and 15-year-old boys.
The boys also outperformed the girls for the AIS motor skills of vertical jump and 40m
sprint. Finally, a significant interaction between gender and age was reported for the BMC
motor skill of hopping speed. This revealed that although boys outperformed girls at age 12
they deteriorated with an increase in age while the girls improved hopping speed as they
became older. The two movement coordination motor skills of the shuttle run and the
shuttle-run-with-object revealed that the boys outperformed the girls. Finally, the results for
the quadrant jump indicated that the number of correct jumps for the girls increased with age
and that from age 13 the girls outperformed the boys.
Two stepwise discriminant analyses were undertaken to find the best set of motor skills for
classifying Malaysian adolescents into three motor coordination groups based on scores on
the MAND and three motor ability groups derived from scores on the motoric ‘g’. The
ability of a combined AIS+BMC motor skill instrument to classify Malaysian adolescents
into the three groups was good for those classified as Normal, not so great for those
adolescents classified as High, and poor for those adolescents classified as Low. The motor
skills consistently reported across both sets of analyses were Balance-Eyes-Open, Balance-
Eyes-Closed, Dynamic Balance, Hopping Speed, Quadrant Jump, Hopping-in-Square,
Basketball Throw and Shuttle-Run-with-Object. Hence, motor skills assessing static balance,
dynamic balance and postural control appeared to reliably discriminate the Malaysian
adolescents into three motor performance groups.
Finally, an examination of the misclassifications found in the discriminant analyses revealed
two things. Those individuals being predicted into a lower group performed a large number
of the motor skills to a lesser standard when compared with their correctly classified cohorts.
Conversely, those predicted into a higher group performed a number of motor skills to a
standard higher than their correctly classified cohorts. Thus, at a global level, certain
individuals could be overlooked for further athletic development and is a concern when
developing a rigorous TI program. Therefore, practitioners need to be cautious of any single
ability score, and how that represents an individual’s athletic potential. These results are
discussed, limitations noted, and directions for future research provided.
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ACKNOWLEDGMENTS
I would like to thank many people, especially those listed below, without whose
contributions this research would not be completed.
Firstly, thanks to my research supervisor, Dr. Dawne Larkin for the ideas and enthusiasm
shown to me. Your encouragement, guidance and passion constantly inspired me to bring
this research area to life. To all the staff and postgraduate students in the School of Sport
Science, Exercise and Health, at The University of Western Australia, thanks for all your
support and assistance. You provided me with a fantastic environment in which to work. To
Dr Paul Heard and Professor Brian Blanksby, special thanks for helping me bring this
project to fruition.
Thank you Adis for the time you spent helping me with my writing. To Tun and Jem, even
though you were far from us, your assistance was the best we had. To Sue, Debbie and the
Herdsman Neighbourhood Centre, thanks for being there for us help with the hectic work
and release the tension. Thanks too for teaching us how to live in Australia by providing
support in the environment and society when we first arrived.
Mak, bapak and family, you are my inspirations. Thank you very much to the participants,
colleagues and friends in Malaysia, and Universiti Teknologi Malaysia who supported my
study here.
Most importantly, for my own family, I want to thank my lovely husband and sons, Zainal
Fahrul, Faisal and Fauzi for making this thesis possible. Your support and faith make me
believe anything is possible.
Hopefully, this thesis is reward for all the sleepless nights.
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TABLE OF CONTENTS
ABSTRACT ....................................................................................................................... ii
ACKNOWLEDGMENTS ................................................................................................. iv
CHAPTER 1 ........................................................................................................................... 1
INTRODUCTION .............................................................................................................. 1
1.1. STATEMENT OF THE PROBLEM .......................................................................... 3
1.2. RESEARCH QUESTIONS ........................................................................................ 3
1.3. THE CONCEPTUAL FRAMEWORK OF THE STUDY ......................................... 4
1.4. DELIMITATIONS ..................................................................................................... 5
1.5. LIMITATIONS .......................................................................................................... 5
1.6. DEFINITION OF TERMS ......................................................................................... 7
CHAPTER 2 ........................................................................................................................... 9
LITERATURE REVIEW ................................................................................................... 9
2.1. TALENT IDENTIFICATION IN SPORTS ............................................................... 9
2.2. MOTOR ABILITY ................................................................................................... 24
2.3. GENERALITY VERSUS SPECIFICITY OF MOTOR ABILITY ......................... 27
2.4. MOTOR ABILITY TESTS ...................................................................................... 29
2.5. HUMAN MOVEMENT TAXONOMY ................................................................... 31
2.6. FACTORS UNDERLYING MOTOR ABILITY ..................................................... 32
2.7. DISCRIMINATING ITEMS IN MOTOR ABILITY .............................................. 36
2.8. MOTOR ABILITY STUDIES AMONG ADOLESCENTS .................................... 38
CHAPTER 3 ......................................................................................................................... 42
METHODS AND PROCEDURES .................................................................................. 42
3.1. INSTRUMENTS. ..................................................................................................... 42
3.2. TRANSLATION OF TEST INSTRUMENTS INTO MALAY. ............................. 44
3.3. PARTICIPANTS. ..................................................................................................... 45
3.4. ADMINISTRATION OF THE TESTS .................................................................... 46
3.5. DATA ANALYSIS .................................................................................................. 46
CHAPTER 4 ......................................................................................................................... 51
FACTOR ANALYSES OF THE MOTOR SKILL INSTRUMENTS ............................. 51
SECTION A - FACTORS UNDERLYING THE MAND, AIS AND BMC ................... 52
4.1. RESULTS ................................................................................................................. 52
4.2. DISCUSSION: FACTORS UNDERLYING THE MAND, AIS AND BMC.......... 56
4.3. RESULTS – BOYS’ SUB-SAMPLE ....................................................................... 63
4.4. RESULTS – GIRLS’ SUB-SAMPLE ...................................................................... 67
4.5. DISCUSSION – GMA ANALYSES ....................................................................... 70
4.6. GENERAL DISCUSSION ....................................................................................... 73
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CHAPTER 5 ......................................................................................................................... 77
RESULTS AND DISCUSSION FOR THE MOTOR SKILL PERFORMANCES ......... 77
5.1. AUSTRALIAN INSTITUTE OF SPORT (AIS) TALENT IDENTIFICATION
INSTRUMENT ................................................................................................................ 77
5.2. McCARRON ASSESSMENT OF NEUROMUSCULAR DEVELOPMENT
(MAND) ................................................................................................................... 84
5.3. BALANCE AND MOVEMENT COORDINATION (BMC) INSTRUMENT ....... 92
5.4. DISCUSSION........................................................................................................... 99
5.5. SUMMARY ........................................................................................................... 110
CHAPTER 6 ....................................................................................................................... 112
DISCRIMINANT ANALYSIS OF COMBINED AIS+BMC MOTOR SKILL SET .... 112
6.1. GROUP CLASSIFICATION BASED ON SCORES ON THE MAND ................ 112
6.2. RESULTS – ALL PARTICIPANTS ...................................................................... 114
6.3. DISCUSSION – ALL PARTICIPANTS ................................................................ 120
6.4. GROUP CLASSIFICATION BASED ON THE MOTORIC ‘g’ ........................... 122
6.5. RESULTS – ALL PARTICIPANTS ...................................................................... 124
6.6. DISCUSSION – ALL PARTICIPANTS ................................................................ 129
6.7. GENERAL DISCUSSION ..................................................................................... 131
SUMMARY, CONCLUSIONS & FUTURE STUDY RECOMMENDATIONS ......... 136
7.1. SUMMARY OF RESEARCH................................................................................ 136
7.2. LIMITATIONS ...................................................................................................... 147
7.3. STRENGTHS ......................................................................................................... 148
7.4. CONCLUSIONS .................................................................................................... 149
7.5. RECOMMENDATIONS FOR FURTHER STUDY ............................................. 150
REFERENCES ................................................................................................................... 151
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LIST OF TABLES
Table 1. Test Items with Predominant Characteristics in the Australian Talent Search
Program. ........................................................................................................... 18
Table 2. Different Sets of Items to Identify Talented Young Athletes........................... 19
Table 3. Categorised Factors Underlying Physical Qualities and Motor Educability. ... 35
Table 4. Rate of Motor Ability Improvements. .............................................................. 41
Table 5. Numbers and Percentages of Participants in the Research. .............................. 45
Table 6. Motor Skills, Scoring Method & Statistical Analyses from Chapters 4-6. ...... 50
Table 7. Correlations, Components & Loadings for the MAND for All Participants. ... 54
Table 8. Correlations, Component & Loadings for the AIS for All Participants. .......... 55
Table 9. Correlations, Components & Loadings for the BMC for All Participants. ...... 56
Table 10. Correlations, Components & Loadings of the AIS+BMC for the Boys........... 64
Table 11. Higher-order Factor Analysis of the AIS+BMC for the Boys. ........................ 65
Table 12. Correlations, Components & Loadings of the AIS+BMC for the Girls. .......... 68
Table 13. Higher-order Factor Analysis of the AIS+BMC for the Girls. ......................... 68
Table 14. The First-order Components and Higher-order Factor of the AIS+BMC
for the Boys’ and Girls’ Sub-samples. ............................................................. 73
Table 15. Means ± SDs and Reliability Coefficients for the AIS Tests. .......................... 78
Table 16. Means ± SDs for the AIS Anthropometry Measures. ...................................... 79
Table 17. ANOVA Results for the AIS Anthropometry Measures. ................................. 80
Table 18. Descriptives for the Main Effect Age on the AIS Weight Assessment. ........... 80
Table 19. Means ± SDs for the AIS Motor Skills. ........................................................... 82
Table 20. ANOVA Results for the AIS Motor Skills. ...................................................... 83
Table 21. Descriptives for the Main Effect Age on the Vertical Jump Motor Skill. ........ 84
Table 22. Means ± SDs and Reliability Coefficients for the MAND............................... 85
Table 23. Means ± SDs for the MAND Fine Motor Skills. ............................................. 87
Table 24. ANOVA Results for the MAND Fine Motor Skills. ........................................ 88
Table 25. Decriptives for the Main Effect Age on the Finger Tapping Motor Skill. ....... 88
Table 26. Means ± SDs for the MAND Gross Motor Skills. ........................................... 90
Table 27. ANOVA results for the MAND Gross Motor Skills. ....................................... 91
Table 28. Descriptives for the Main Effect Age on the Jumping Motor Skill. ................ 92
Table 29. Means ± SDs and Reliability Coefficients for the BMC. ................................. 93
Table 30. Means ± SDs for the BMC Body Balance Motor Skills. ................................. 94
Table 31. ANOVA Results for the BMC Body Balance Motor Skills. ............................ 95
Table 32. Means ± SDs for the BMC Movement Coordination Motor Skills. ................. 96
Table 33. ANOVA Results for the BMC Movement Coordination Motor Skills. ........... 97
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Table 34. Mean Heights and Weights ± SDs of the Malaysian Sports Council Data
and the Current Research. ............................................................................... 99
Table 35. Number and Percentage of Participants in the Three Motor Coordination
Groups. ........................................................................................................... 113
Table 36. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canoninical Correlations, Eigenvalues and Group Centroids for
the Three Motor Coordination Groups ........................................................ 115
Table 37. Profiling Correctly Classified and Misclassified Observations in the
Three- Group Discriminant Analysis for all Participants ............................... 118
Table 38. Participant Numbers & Percentages in the Three Motor Ability Groups. ..... 123
Table 39. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canonical Correlations, Eigenvalues and Group Centroids for
the Three Motor Ability Groups. ................................................................... 125
Table 40. Profiling Correctly Classified and Misclassified Observations in the
Three-Group Discriminant Analysis for All Participants. .............................. 127
Table 41. Summary Table of Findings. .......................................................................... 137
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LIST OF FIGURES
Figure 1. Conceptual framework of motor ability testing & TI program. ......................... 6
Figure 2. Talent identification and the development process. ......................................... 11
Figure 3. The motor skills, loadings & motor abilities for the MAND, AIS & BMC. .... 62
Figure 4. The AIS+BMC motor skills, first-order components and higher-order
factor for the boys. ........................................................................................... 67
Figure 5. The AIS+BMC motor skills, first-order components and higher-order
factor for the girls. ............................................................................................ 70
Figure 6. Significant age by gender interaction for the Basketball throw. ...................... 83
Figure 7. Significant age by gender interactions for the gross motor skills of Grip
Strength and Finger-nose-finger. ...................................................................... 91
Figure 8. Significant age by gender interaction for the Hopping speed motor skill. ....... 98
Figure 9. The percentile patterns of height among Australian adolescents
(Australian Sports Commission, 1998) and Malaysian participants in this
study (AG = Australian girls, AB = Australian Boys, MG = Malaysian
Girls, MB = Malaysian Boys). ....................................................................... 101
Figure 10. The percentile patterns of weight among Australian adolescents
(Australian Sports Commission, 1998) and Malaysian participants
in this study (AG = Australian girls, AB = Australian Boys,
MG = Malaysian Girls, MB = Malaysian Boys). ............................................ 102
Figure 11. The percentile patterns for vertical jump results among Australian
adolescents (Australian Sports Commission, 1998) and Malaysian
participants in this study (AG = Australian girls, AB = Australian Boys,
MG = Malaysian Girls, MB = Malaysian Boys). ............................................ 104
Figure 12. The percentile patterns for 40m sprint results among Australian
adolescents (Australian Sports Commission, 1998) and Malaysian
participants in this study (AG = Australian girls, AB = Australian Boys,
MG = Malaysian Girls, MB = Malaysian Boys). ............................................ 105
Figure 13. The percentile patterns for the multistage fitness test results among
Australian adolescents (Australian Sports Commission, 1998) and
Malaysian participants in this study (AG = Australian girls,
AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian Boys). ....... 106
Figure 14. The percentile patterns for the basketball throw results among
Australian adolescents (Australian Sports Commission, 1998) and
Malaysian participants in this study (AG = Australian girls,
AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian Boys). ....... 107
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LIST OF APPENDICES
APPENDIX A Summary of motor ability test instruments
APPENDIX B Human Movement Taxonomy
APPENDIX C Details for selecting the BMC motor skills
APPENDIX D Testing protocols of MAND, AIS and MC test (Malay version)
APPENDIX E Letters of permission to conduct data collection
APPENDIX F Testing protocols of MAND, AIS and MC test (English version)
APPENDIX G Conversion Tables
APPENDIX H Confirmatory Factor Analyses of The MAND
APPENDIX I Total Sample Analysis Testing Motoric ‘g’
APPENDIX J Female Discriminant Analyses
Note. The appendices are stored on the CD provided.
1
CHAPTER 1
INTRODUCTION
Talent identification (TI) is a structured process with the goal of maximising personal
potential of individuals after revealing exceptional abilities. After being awarded the 1998
Commonwealth Games, the Malaysian Sports Council (Majlis Sukan Negara - MSN)
developed a TI and development program to effectively nurture Malaysian athletes. The
program tested young participants in sports (Moreland, 1994) and non-sport participants to
identify latent sport talent through mass screening tests (Majlis Sukan Negara, 1998).
Unfortunately, the concepts and rationales for TI test selection were not documented, except
for the test procedures and resultant normative data. After hosting the Games, the program
was only continued among athletes and the mass screening of the general population ceased.
However, following poor performances in the South-East Asian (SEA) games (1999, 2001,
2003) and subsequent Commonwealth Games (2002, 2006) the MSN, has wanted to
recommence the mass screening tests.
The development of a national sport institute (Institut Sukan Negara - ISN) in Malaysia was
influenced by the Australian Institute of Sport (AIS) (Moreland, 1994) and a talent search
program conducted by the AIS motivated Malaysia to follow suit. Because the development
of TI instruments and protocols should be population specific (Reilly, Williams, Nevill &
Franks, 2000), groundwork is required prior to providing a focused TI program in Malaysia.
Talent identification programs in sport require the assessment of individual movement skills
through several stages. Firstly, one screens individual organic and motor attributes to assess
motor ability. Then follows a phase of sport-specific skills testing and talent development
(Australian Sports Commission, 1998; Brown, 2001; Durand-Bush & Salmela, 2001; Grice,
2003; Gulbin, 2001; Hoare, 1995, 1998; Jarver, 1981; Kozel, 1996; Loko, 1994; Malina,
1997; Moore, Burwitz, Collins & Jess, 1998a; Moore et al., 1998b; Regnier, Salmela &
Russell, 1993; Riordan, 1987; Wu, 1992).
Several countries use similar procedures to identify potentially talented athletes, including
Australia (Australian Sports Commission, 1998), China (Wu, 1992), Eastern Europe
(Riordan, 1987), Germany (Kozel, 1996) and Russia (Jarver, 1981). Typically, individuals
are mass screened via general motor capacity and physical skills, and then sport related skill
tests until finally selected for sport development programs (Hoare, 1995; Jarver, 1981). This
process assumes that measurable underlying features such as physical type, motor abilities
and traits do exist among talented athletes (Regnier et al., 1993). A movement taxonomy
2
was developed (Burton & Miller, 1998) and revised (Burton & Rodgerson, 2001) to point
out the importance of specific motor ability (SMA) and general motor ability (GMA) when
assessing movement skill.
The Burton and Rodgerson (2001) movement taxonomy involves assessment of movement
skills at many levels of TI. Thus, when identifying talent in sport, or assessing an
individual’s motor ability, one initially assesses various components of physical capacities
or abilities such as strength, power, endurance, agility, speed, body coordination, eye-hand
and eye-foot coordination, balance and accuracy. Therefore, investigations of the underlying
constructs of the motor skill instruments being utilised is essential when assessing TI,
because one needs to know what it is that a particular motor skill instrument is assessing.
Burton and Rodgerson (2001) also proposed that GMA was the underlying component in
movement skill assessment (MSA) at all levels of movement tasks. This includes Movement
Skill Sets, Movement Skill and Movement Skill Foundations. The term ‘Movement Skill
Foundations’ is used in the taxonomy to refer to motor abilities. Controversy has surrounded
the acceptance or rejection of the GMA concept because of inconsistency of results between
motor ability items (Burton & Rodgerson, 2001; Chaiken et al., 2000; Rivenes & Sawyer,
1999). The contradictory statistical interpretations are seen when evaluating the factor
loading in Fleishman’s studies (1954, 1956 & 1964). Analysis by Rivenes and Sawyer
(1999) on the coefficient of determination of Fleishman’s data has lead to a rejection of the
GMA concept. However, a re-evaluation of the correlation matrix of Fleishman’s studies has
supported the existence of GMA (Chaiken, Kyllonen & Tirre, 2000). In the current research,
quite different motor skill test batteries were used to assess different types of motor ability
thought to be important for TI in Malaysia. Two of these instruments underwent factor
analysis to examine the notion of GMA.
However, before developing mass screening TI tests suitable for Malaysia, appropriate test
items are needed. Hahn (1991) conducted a series of studies to isolate the most suitable TI
instrument specific for rowers. One study validated results of a field test item (arm-and-leg
ergometer) with a sophisticated laboratory instrument (rowing ergometer). The selected TI
tests were did unearth talented rowers in Australia. Grice (2003) also found some sport
specific tests that were useful when selecting talented athletes in specific sports among
Americans. However, since there are limited physical capacity and motor ability data
available for the Malaysian population (Majlis Sukan Negara, 1998), and given the
exploratory nature of the research, the AIS TI test was used in the current research. This was
in conjunction with movement coordination and balance motor ability items (BMC) derived
3
to facilitate the development of a Malaysian TI test instrument (Burton & Miller, 1998;
Burton & Rodgerson, 2001). Hoare (1994) claimed that the AIS TI test was valid and
reliable for Australians. However, whether assessment by the AIS motor skill instrument
holds up in the Malaysian context, awaits verification. Additionally, considering the
importance of exploring the TI instruments constructs for a specific population, further
investigations to identify the factors or motor abilities underlying the TI tests in the current
research is required.
Finally, an important component to any TI program is the ability to compare individual
performances with relevant performance standards. Comparing Malaysian adolescent
performances on the motor skills in these instruments with data from other countries will
also determine if separate age and gender norms are necessary for Malaysians.
1.1. STATEMENT OF THE PROBLEM
The overall purpose of this study was to examine the components of existing motor skill
instruments, in conjunction with other basic TI tests in sport, for subsequent use in Malaysia.
More specifically, this project:
• examined the underlying constructs of the motor skill instruments used here in a
sample of Malaysian adolescents;
• investigated the existence of a ‘g’ in motor ability or GMA;
• compared Malaysian performance data with existing performance data from the
West to establish if there is a need to develop norms relevant to Malaysia;
• sought to identify the most suitable motor skills from the instruments utilised
for inclusion in a TI instrument for Malaysian adolescents;
• where possible, examined gender differences
1.2. RESEARCH QUESTIONS
In order to address the above purposes, a series of research questions were asked.
1. Do the motor skill instruments developed on Western populations assess the same things
when examined on populations in Malaysia, an Eastern culture? Thus, what are the
underlying constructs of the motor skill instruments used in this research to elucidate the
nature of these instruments in a Malaysian setting?
4
2. Can statistical approaches such as higher order factor analyses provide evidence to
support the general motor ability (GMA) concept?
3. Do motor skill performances vary with age and gender among Malaysian adolescents?
4. How do Malaysian participants’ motor performances compare with those reported for
Western populations?
5. Can Malaysian adolescents reliably be discriminated and classified into Poor, Normal
and High motor coordination groups derived from scores on the MAND, and/or the
motor skills in the AIS+BMC motor skill instrument?
6. Can Malaysian adolescents reliably be discriminated and classified into Low, Normal
and High levels of motor ability derived from factor scores from the motoric ‘g’
analyses, with the motor skills in the AIS+BMC motor skill instrument?
1.3. THE CONCEPTUAL FRAMEWORK OF THE STUDY
Individuals were assessed along a motor ability continuum to identify those exhibiting
greater abilities. Assessing persons with high motor ability is an ongoing process at every
stage of TI. Fundamental factors which influence sports performance that should be included
in any TI program are morphology, motor abilities, perceptual ability, psychological,
demographic/situational and socioeconomic levels (Arnots & Gaines, 1986; Bloomfield,
1992; Bompa, 1985, 1990; Malina, 1997; Regnier et al., 1993; Reilly et al., 2000;
Woodman, 1985).
The research was carried out at the basic level of TI and focused on investigating motor skill
variables that helped to identify talented performers. The selection of adolescents aged 12-15
was deemed appropriate because they should have completed the sampling phase and be in
the specialising phase of motor skill development. The conceptual framework focused on the
underlying constructs or motor abilities assessed by the motor skill instruments employed.
The AIS motor skills, together with balance and motor coordination motor skills (i.e., the
BMC motor skill instrument), were examined along with a combined AIS+BMC motor skill
instrument. In addition, the combined AIS+BMC instrument underwent discriminant
analysis to find the best set of motor skills to discriminate and classify participants into three
groups based on motor performance ability. Figure 1 illustrates the proposed model on the
relationships of motor ability and TI, and the shaded area was the focus of this study. The
conceptual framework (Figure 1) illustrates the preliminary factors associated with TI and
the continuum of motor abilities specific to the study. The isolation of motor abilities as key
factors permitted a two-fold assessment of GMA; firstly as a concept, and then mass
screening and motor ability testing required for a population sensitive TI test battery. Thus,
5
this research attempted to find motor skills and motor abilities able to discriminate between
general motor performance ability and exceptional motor performance ability in Malaysian
adolescents.
1.4. DELIMITATIONS
1. Only two foundations of the eleven movement skill foundations were selected to be
examined (Burton & Miller, 1998); namely, balance and movement coordination. Nine
motor skills were selected to measure these two movement skill foundations. Thereafter,
these nine motor skills formed the motor skill instrument called Balance and Movement
Coordination (BMC).
2. Only 330 male and female participants aged 12-15 years participated in the current
research. This age range was selected for it was determined to be the most suitable for
examining motor performance that could be used for identifying athletic talent.
1.5. LIMITATIONS
1. The field tests could only be conducted either from 8.00-10.00AM or 4.00-6.00PM, as
these were the times when physical education classes take place in Malaysia. The
average temperature is cooler in the morning than the hotter and more humid afternoons.
The temperature and time of day might have influenced participants' performances.
2. Interpretation on the existence of the GMA concept in this study was based on the factor
loadings extracted from the first-order factors using higher-order factor analysis.
3. The age groups were limited to 12 – 15 year old adolescents because this represented the
ages when Malaysian begin to choose competitive sports. However, it should be noted
that, at these ages, biological and chronological ages can vary greatly.
4. Convenience testing was used s access to subjects prevented random stratified sampling.
6
Figure 1. The conceptual framework of motor ability testing and talent identification
program.
7
1.6. DEFINITION OF TERMS
The following terms were adopted for operational use in this study.
Motor Ability Traits or capacities that underlie a variety of
movement skill performances.
General Motor Ability A trait underlying performance in movement skills
(Burton & Rodgerson, 2001; Schmidt & Lee, 1999).
Talent Potential physical capacity and trainable motor
ability contributing to superior performance in
various sport domains.
Expertise Individual ability to manipulate biological (physical
capacity, motor ability, genetics) and environmental
(personality, family, coaching and others) factors
through intensive training and obtain excellent
performances in various sport domains.
MAND
McCarron Assessment of Neuromuscular
Development standardised test is used to identify
individual fine and gross motor skills, especially
with poorly coordinated subjects (McCarron, 1982).
AIS The Australian Sports Commission (1998) stated that
the AIS test instrument assesses four different motor
abilities - speed, ability to spring in a vertical
direction, upper body strength and aerobic fitness.
BMC A motor skill instrument specifically developed for
this research to assess two components of Burton and
Miller’s (1998) movement skill foundations; namely,
balance and movement coordination.
8
NDI score Neuromuscular Development Index score is
determined from the total of the standardised scores
for each of the MAND tasks.
Coordination group Poor, Normal and High groups were determined
through the NDI scores from the MAND test, based
on their coordination scores.
High coordinated group Those achieving an NDI score > 115 in MAND test.
Normal coordinated group Those achieving an NDI score from 85-115 in
MAND test.
Poor coordinated group Those achieving an NDI score < 85 in MAND test.
Motor ability group Three groups of participants were determined via the
factor score of GMA extracted from a higher order
factor analysis of AIS+BMC tests.
High motor ability group Individuals who achieve the top 10% of the ‘g’
factor scores among all participants.
Normal motor ability group Individuals who achieve from 11% to 89% of ‘g’
factor scores among all participants.
Low motor ability group Individuals who achieve the bottom 10% of ‘g’
factor scores among all participants.
9
CHAPTER 2
LITERATURE REVIEW
2.1. TALENT IDENTIFICATION IN SPORTS
Attempts to identify talented athletes have operated since organised sport began (Burgess,
1997) and TI is recognised as a key component in successful elite programs (Hoare, 1998). It
has evolved from an unstructured process relying on competition results, towards a more
systematic and structured approach using evidence-based assessments for TI and
development programs. However, using a scientific approach to predict future elite
performances is difficult (Regnier et al., 1993) and demands a high work commitment
(Gulbin, 2001). Different countries use similar procedures such as mass screening that
include general motor capacity and physical tests; followed by sport specific and sport
suitability tests; and, finally, a sport specific development program (Hoare, 1995; Jarver,
1981).
Previous research has shown that selection test items and criteria used in one country might
not be suitable in another (Abbott & Collins, 2002; du Randt, 2000; Viljoen, Malan &
Pienaar, 2004). Also, despite similar procedures being utilised by different countries, the
motor skill instruments selected as tools for TI are diverse. Performance differences for
particular motor skills across different populations has led to the development of tests and
norms for individual countries (du Randt, 2000; Viljoen et al., 2004), and each sport has its
own specific components (Bompa, 1985; Brown, 2001; Famaey-Lamon, Hebbelinck &
Cadron, 1979; Hoare, 2000; Hoare & Warr, 2000; Pienaar & Spamer, 1998; Pienaar, Spamer
& Steyn, 1998; Williams & Franks, 1998; Williams & Reilly, 2000).
2.1.1. Talent and Expertise
Gagne (1996) claims the concept of talent can have two divergent interpretations. Either it is
seen as raw material that is natural ability presented with varying intensity, or as
systematically developed abilities which are characteristics of an expert. Within Gagne’s
model (1996), the former interpretation is known as giftedness and the latter as talent.
Generally, the definition of giftedness almost parallels the definition of motor ability as it
refers to one’s innate ability in the sport domain.
10
The giftedness of Gagne’s model is the phase in which mass screening tests for TI programs
are significantly involved, and the main focus of this study. Relating the concept of talent in
Gagne’s model with TI in sport, TI processes sporting (via a mass screening or talent
detection phase) raw material or ‘giftedness’ such as motor ability, perceptual-motor and
psychological aspects. Thus, in the talent development phase, one nurtures the raw material
to obtain sport expertise by bringing that ‘talent’ to fruition. Both giftedness and talent are
inclusive in sport TI. However, the term ‘talent’ is often referred to as raw material.
Accordingly, talent has been defined as “any innate capacity that enables an individual to
display exceptionally high performance in a domain that requires special skills and training”
(Simonton, 1999). In relation to sports, talent can be seen as an undeveloped ability that
exceeds the average standard (Williams & Franks, 1998). Individuals who possess talent in
sport will exhibit specific characteristics that lead to excellent sporting performance
(Williams & Franks, 1998). For the purpose of the current research, similar to the notion of
giftedness by Gange (1996), talent will refer to the raw materials that are natural abilities
present in an individual.
The ultimate goal of a TI program is to maximise sport performance or sport expertise.
Expertise is defined as ‘skilfulness by virtue of possessing special knowledge or ability’
(Webnox Corp, 2003). An expert is a person who possesses the raw material, special
knowledge or ability to perform skilfully following deliberate practice (Ericsson, 1996;
Ericsson & Lehmann, 1996; Richman, Gobet, Staszewski & Simon, 1996; Webnox Corp,
2003). In addition, an expert performance is defined as being consistently superior on a set
of tasks which are representative of a specific domain (Ericsson & Lehmann, 1996). As skill
is important in sport and acquired through training, expertise consists of a combination of
training, experience, and innate differences in capacities and talents (Ericsson & Lehmann,
1996). Gagne’s (1996) interpretation of expertise is referred to as a talent in which expertise
is the product of a developmental process of giftedness in a particular domain.
2.1.2. Talent identification process
Development and TI programs in sport claim to pursue sports excellence via scientific
endeavour (Williams & Franks, 1998; Williams & Reilly, 2000). There is general consensus
that TI contains several phases of identification (Australian Sports Commission, 1998;
Grice, 2003; Hahn, 1991; Jarver, 1981; Kozel, 1996; Loko, 1994; Majlis Sukan Negara,
1998; Malina, 1997; Matsudo, Rivet & Pereira, 1987; Mohan, 2003; Moreland, 1994;
11
Riordan, 1987; Williams & Franks, 1998; Wu, 1992). The TI and development process
comprises the four key phases of detection, identification, selection and development
(Williams & Franks, 1998) (Figure 2.)
Figure 2. Talent identification and the development process.
From: Williams, A. M. & Franks, A. (1998). Talent identification in soccer. Sports, Exercise
and Injury, 4, 159-165
Within Williams and Reilly’s (2000) model, talent detection involves finding prospective
performers who are presently not participating in any sport. Talent detection is based on the
prediction of performance with the assumption that measurable underlying factors such as
anatomical structure, abilities and traits do exist (Regnier et al., 1993). Talent identification
is a process of finding sports participants to train and become elite athletes. Guidance into
suitable sports is also provided in this stage (Woodman, 1985). On the other hand, talent
selection is a continuous process of identifying athletes who qualify at certain levels of
performance. Finally, talent development refers to a stage where potential athletes are
exposed to appropriate learning which exploits their potential.
Williams and Reilly’s (2000) model indicates that there are different target groups when
applying talent detection and TI processes. However, several countries (Australian Sports
Commission, 1998; du Randt, 2000; Grice, 2003) combine talent detection (referred to as
basic/mass or preliminary screening testing) with the other TI processes to represent a TI
program. The current research also uses talent identification as referring to both the talent
detection and the TI processes of the Williams and Franks (1998) model.
Potential athletes undergo several measurements within the various TI phases. Then, athletes
who demonstrate superior results in tests which also predict success in a specific sport, are
Development
Identification Selection
Detection
12
invited to participate in that sport-specific talent development program. Talent development
implies that selected athletes will be placed in a suitable learning environment to maximise
their sport potential and be monitored at regular intervals (Williams & Reilly, 2000). Gulbin
(2001) affirmed that developing the talent is the more challenging aspect, as both sporting
excellence and the person as a whole need to be developed.
2.1.3. Issues in Sports Talent Identification
During the 1960s, TI programs were operating in the Russian bloc (USSR) and other
countries (Arnots & Gaines, 1986). They were considered to be effective in helping
countries with lower populations compete with larger countries to depict the success of their
political systems (Arnots & Gaines, 1986). However, the TI contribution to performance
enhancement is still controversial (Pienaar, 1998). The practical use of a systematic
approach (Williams & Reilly, 2000), ethical and moral issues (Malina, 1997; Pienaar, 1998;
Williams & Reilly, 2000); and validity, reliability and objectivity of the tests used to predict
performance have been questioned (Durand-Bush & Salmela, 2001). Further, Moore et al.
(1998b) wrote that evidence on TI in sport is fragmented and incomplete.
The nature of TI is multidimensional (Durand-Bush & Salmela, 2001; Mohan, 2003;
Simonton, 1999; Williams & Reilly, 2000). Hence, different approaches have been applied
to identify talented athletes (Regnier et al., 1993). It is agreed that factors from sociology,
physiology, psychology and anthropometry need consideration. However, their significance
also depends on age, type of sport and the development phase in which the prediction of
potential athletic success is currently placed (Abbott & Collins, 2002; Burgess, 1997;
Malina, 1997; Moore et al., 1998b; Pienaar, 1998; Pienaar & Spamer, 1998; Reilly et al.,
2000; Williams & Franks, 1998; Williams & Reilly, 2000; Woodman, 1985).
Issues in TI requiring greater clarification
Talent - heredity versus environment : The controversial issue of whether talent is largely
inherited or the result of training and environment factors, has been widely reported
(Ericsson, 1996; Ericsson & Lehmann, 1996; Geladas, Koskolou & Klissouras, 2007; Howe,
Davidson & Sloboda, 1998; Simonton, 1999). Although there appears to be an increasing
acceptance of the nurture influence over nature (Klissouras, Geladas, & Koskolou, 2007;
Simonton, 1999), evidence of the contribution of inherent abilities are explicit in sport.
Kutsar (1991) wrote that hereditary anatomical, physiological and psychological pre-
requisites were important for developing physical performance capacities. This genetic
make-up is important in aerobic capacity, adaptability to training (Cowart, 1987), muscle
13
fibre composition (Bompa, 1985; Cowart, 1987; Kutsar, 1991), and personality traits
relating to competitiveness and leadership (Cowart, 1987; Plomin, 1989).
Strength and power are closely related to body shape, composition, proportionality and
posture (Bloomfield, Ackland & Elliott, 1994). Anthropometric measures of height or limb
length can be important in sport (Bompa, 1985). Moreover, height and body mass influence
biomechanical advantage (Olgun & Gurses, 1984). As weight and blood volume increase
exponentially with height, changes in size produced variations in the impact of strength,
weight, power output, acceleration and work capacity (Watson, 1995). The inter-
relationships of these inherent components indicated that body size and shape can be an
advantage in different sports. Several studies of twins have demonstrated the contributions
of both hereditary and environmental influences on motor performance (Kovar, 1976;
Kutsar, 1991; Marisi, 1977; Watanabe, Mutoh & Yamamoto, 2000, 2001; Williams &
Gross, 1980). However, not all those with inherited pre-requisite traits actually succeeded
when directed into their most appropriate sporting activities (Kutsar, 1991).
The influence of environmental factors on motor development and motor ability has long
been acknowledged (Branta, Haubenstricker & Seefeldt, 1984; East & Hensley, 1985;
Haywood, 1993b; Malina, 1973; Malina & Mueller, 1981; Nelson, Thomas, Nelson &
Abraham, 1986; Thomas, 2000; Thomas & French, 1985; Thomas & Thomas, 1988).
According to Haywood (1993b), environmental factors such as race, culture, ethnic group,
socio-economic status, nutrition and child-rearing practices were probably more influential
than genetic factors in motor performance. This was especially so when average, rather than
elite, performers were observed.
Malina (1973) outlined socio-cultural factors influencing motor development during infancy
and early childhood. These were family size, number of siblings, birth order and
socioeconomic background; and the opportunity to practise, infant stimulation, and
availability of toys and equipment. Several studies found that girls were socialised away
from more aggressive competitive activities and boys were socialised into competition
(Branta et al., 1984; East & Hensley, 1985; Greendorfer & Lewko, 1978; Haywood, 1993b;
Malina, 1973; Malina & Mueller, 1981; Nelson et al., 1986; Thomas, 2000; Thomas &
French, 1985; Thomas & Thomas, 1988).
Jurimae and Volbekiene (1998) conducted a comparative cross-cultural study of motor
abilities between two Baltic countries. Estonian adolescents scored significantly higher in
the 20m endurance shuttle-run test, hand grip strength, 10x5m shuttle-run, sit-and-reach, and
14
flamingo balance, than children from Lithuania. On the other hand, the Lithuanian boys
scored higher in bent-arm hang than Estonian boys of the same age.
Finally, access to facilities, coaching, training and practice are environmental factors that
influence individual motor performances. Ericsson and Lehman (1996) argued that
deliberate practices were a necessary preparation for one to become an expert performer.
Additionally, Ericsson (2007) argues that objectively examining expert performance to
identify expert qualities and then devising methods to assess these qualities will be more
rewarding than just focusing upon the basic mechanisms that influence an athlete’s
development.
Thus, since the ultimate goal for identifying talented performers is to maximise that talent
into sports expertise, the heredity and environment contributions on athletic expertise must
be considered (Baker & Davids, 2007; Button & Abbott, 2007; Simonton, 1999; Singer,
1975).
Age of identification:
Talent identification and development may take place at various ages, and vary with
different sports (Malina, 1997; Williams & Franks, 1998). Some countries start TI and
selection of potential athletes at 8-10 years (Tabachnick, 1991), and other countries start
screening at 14-16 years old (Australian Sports Commission, 1998). Identifying talented
athletes could occur as early as 3-8 years old in sports such as gymnastic, diving, swimming
or figure skating, because the peak performances in these sports can occur during
adolescence (Malina, 1997). Thus, it appears important to identify talented athletes at young
ages so that their success can be enhanced by quality preparation during growth years
(Tabachnick, 1991).
However, selecting very young participants creates an ethical and moral dilemma. With
intensive training at early ages, participants must specialise in a narrow band of activities
and skills, and are deprived of exploring other potential abilities. Children should be
encouraged to participate in a variety of different activities, and develop a wide range of
skills consistent with their interests and their abilities. Cote and Fraser-Thomas (2006)
reviewed youth in sport, and suggested that there are many physical, psychological and
social benefits to early diversification and some costs to early specialisation. Indeed, Button
and Abbott (2007) noted that an athlete’s ability to negotiate critical transitions as they
progress through different stages of development could be a critical factor in an athlete’s
sporting success (Bloom, 1985; Cote, 1999). Furthermore, there could be implications for
15
growth, nutrition and delay in sexual maturation; and heightened occurrences in adulthood
of myocardial dysfunction, depression, psychosocial interactions and heat stress (Committee
on Sports Medicine and Fitness, 2000).
Predictive items and the construction of test instruments:
Generally, a set of anthropometric and physical tests have been used to evaluate all-round
performance capacity during the mass screening test phase of TI (Hoare, 1995; Jarver,
1981). Typically, these items do not require sophisticated equipment or specialist assessors
(Jarver, 1981), and try to represent a wide range of factors that influence various types of
sports performances. Hence, potential sport success could be predicted by identifying the
contributing motor abilities. Kirkendall, Gruber & Johnson (1987) stated that motor ability is
a general quality able to facilitate future performance in more specific motor tasks. Thus, TI
can be used to predict exceptional sporting performance (Howe et al., 1998).
However, Moore et al. (1998b) found that no national sport governing bodies in the UK had
a TI strategy grounded on a validated, criterion-based performance model. Furthermore, the
motor ability constructs being assessed have not been clearly defined and little agreement
exists as to the major characteristics for any particular test item (Blomqvist, Luhtanen,
Laakso & Keskinen, 2000). Consequently, the test item validity and usefulness, as well as
the theoretical constructs upon which they are based, require further study (Durand-Bush &
Salmela, 2001).
A TI inventory for predicting success was conducted by specific sports associations in the
USA for children in grades six, seven and eight (e.g., The Kid Test; Grice, 2003). After
developing an inventory focused on aerobic power, motor skills and coordination, data were
placed in a working model to identify talent in specific sports. A legend was developed by
using the mean and standard deviation to cross reference many sports and calculate a
corresponding composite score for each sport. Grice (2003) found relatively high logical
validity of performance on various test items across the three grade levels but no
significance levels were provided. Grice (2003) also suggested that further research was
needed to determine the predictive and concurrent validity, and reliability of the items
among adolescents.
Predicting superior performances by young children is based on the assertion that skill in
sport is associated with the ability to produce a consistent motor pattern (Allard & Burnett,
1985). Another important consideration for TI is that the patterns underlying a particular
task can change with age, practice and experience (Schmidt, 1991). Also, variables chosen
16
for TI must have considerable ability to discriminate between performers at all skill levels
(Woodman, 1985). Talent is harder to predict in later years because the population of sport
performers becomes smaller and more homogeneous, particularly with respect to physical
and physiological profiles (Williams & Franks, 1998). According to researchers, it is
impossible to accurately predict performance from such tests because of chronological and
anthropometric differences among children (e.g., Durand-Bush & Salmela, 2001; Vaeyens,
Lenoir, Williams & Philippaerts, 2008).
Individual versus team sports:
It was first thought that TI programs were effective for individual sports with discrete
physical and physiological demands such as athletics, canoeing and rowing. They were
considered unsuitable for team sports as they required qualities other than physical and
physiological attributes to determine success (Hoare, 1995). Team sport success is often
related to players’ knowledge of the game skills and strategies (Allard & Burnett, 1985).
Currently, TI research programs for team sports such as soccer (Helsen, Hodges, Van
Winckel & Starkes, 2000; Hoare & Warr, 2000; Williams & Franks, 1998; Williams &
Reilly, 2000), basketball (Hoare, 2000; Matsudo et al., 1987), volleyball (Matsudo et al.,
1987) and rugby (Gibson, Okely, Webb & Royall, 1999; Pienaar et al., 1998) have found
that TI programs can be useful. The only reservation is that each sport requires its own
specific criteria (tests) that accurately measure attributes contributing to success (Woodman,
1985). Thus, considerable work needs to be done to identify the motor skills that are related
to successful performance in a particular sport.
2.1.4. Talent Identification in Malaysia
A TI program in Malaysia was in place prior to hosting the Commonwealth Games in 1998.
The TI test procedures and normative data for Malaysian adolescents aged 11-14 years, and
Malaysian elite athletes were published but no other information was available (Majlis
Sukan Negara, 1998). No documented concepts or groundwork on the TI tests or related
matters are available. The test items were height, arm span, sitting height, body mass, skin
fold measurements, weight throw, vertical jump, 40m sprint, agility hexagon test, and
endurance shuttle run or an 800m run. As Malaysian international sport performances have
diminished, the Ministry of Youth and Sport has sought to overcome past problems and
reconvene the TI mass screening.
17
2.1.5. Talent Identification Testing in Australia
Australia has a well established TI program in sports (Australian Sports Commission, 1998).
The mass screening phase is called the Talent Search Program to identify and prepare
potentially talented athletes for domestic, national and international competitions. The
assessment measures are height and sitting height, body mass, arm span, basketball throw,
vertical jump, 40m sprint and shuttle run (multi-stage fitness test). Three test items are
anthropometric in nature while the other four measure aspects of motor performance.
Several tests have been developed for phase two, depending on the sports under
consideration (Australian Sports Commission, 1998). Each of the four motor performance
tests measures an individual’s specific athletic capability. The motor performance tests have
different characteristics and measure different aspects of motor ability (see Table 1). Test
items in the mass screening testing phase of the Australian Talent Search program have
different predominant characteristics than those used to measure individual capabilities in
phase two. In some cases, the same test items might be used but with different purposes in
mind. In addition, to date, there was no evidence outlining the degree to which the individual
test items discriminated between talented youngsters.
2.1.6. Differences in Items Used in Talent Identification Test Batteries
Researchers in several countries have devised different motor performance tests to identify
talented young athletes in different sports (see Table 2). In general, these tests measure
individual strength, endurance, power, agility and speed. However, despite some similarities
in the types of tests used in different nations, one country’s test items and criteria might be
unsuitable in another country (du Randt, 2000; Viljoen et al., 2004). For example, a
consideration of gender dis/advantage and urban/rural area distribution factors revealed that
78% of the relevant test performances were significantly different for adolescents in
Australia and South Africa. Hence, when using tests developed in another country, such tests
need to be validated first on the population under consideration prior to their adoption based
solely on theoretical grounds.
18
Table 1. Test Items with Predominant Characteristics in the Australian Talent Search
Program.
Test Items Predominant Characteristics
Vertical Jump Ability to spring in a vertical direction (Australian Sports
Commission, 1998).
Motor Explosiveness (Larson, 1941).
Test of Power, Strength, Speed, Energy, Dexterity (McCloy,
1968).
Explosive Strength (Fleishman, 1964; Sargent, 1968)
Power (Johnson & Nelson, 1986).
Shuttle Run (Multi-
stage fitness)
Aerobic fitness (Australian Sports Commission, 1998; Jurimae &
Volbekiene, 1998; Leger & Lambert, 1982; Tomkinson, Olds &
Gulbin, 2003).
40 m Sprint Speed (Australian Sports Commission, 1998; Johnson & Nelson,
1986; Safrit, 1995).
Basketball Throw Upper body strength (Australian Sports Commission, 1998).
Strength and/or Coordination (Barrow & McGee, 1964). Muscular
Strength & Speed of Movement (Arnheim & Sinclair, 1979).
Arm & shoulder coordination (Barrow, 1954).
Height and Sitting
Height
Motor capacity
Body Mass Motor capacity
Arm Span Motor capacity
19
Table 2. Different Sets of Items to Identify Talented Young Athletes.
Articles/Reports Sets of Test Items Age Groups Remarks
Australian Talent
Identification
Program
(Australian
Sports
Commission,
1998).
Phase 1:
Basketball throw (upper body
strength), Vertical jump (ability to
spring in a vertical direction), 40-m
sprint (speed), Shuttle run - multi-
stage fitness test (aerobic fitness)
12-17 years
old
For mass screening
tests.
Australian Talent
Identification
Program
(Australian
Sports
Commission,
1998); (Hoare,
2000; Hoare &
Warr, 2000).
Phase 2:
Counter movement jump (leg
explosiveness), Vertical jump
(ability to spring in a vertical
direction), Cricket ball throw (arm
speed), Radar speed (over arm
throwing speed), Forward shot
throw (general explosiveness),
Seated shot throw (body
explosiveness), Basketball throw
(upper body strength), Bench pull
(arm and shoulder strength), Agility
run (agility), 20 and 40 meter sprint
(ability to accelerate), 20 meter fly
(ability to accelerate), Multistage
fitness test (aerobic fitness),
Arm/leg ergometer (aerobic power)
12-17 years
old
Athletics, baseball,
basketball,
canoeing, cycling,
diving, hockey,
judo, netball,
rowing, rugby,
skiing, soccer,
softball,
swimming, tennis,
triathlon,
volleyball, water
polo, weightlifting
and wrestling.
Brazil (Matsudo
et al., 1987)
50m dash, 40s run, Vertical jump
(without arm assist), Vertical jump
(with arms), Long jump, Shuttle run
7-18 years
old
Talent
identification
assessment.
USSR (Jarver,
1981)
30 m from standing start (speed),
12/15 min run (endurance), Harvard
step tests (work capacity), Standing
long jump (power)
8-14 years
old
Basic selection.
20
Table 2 continued.
KidTest USA
(Grice, 2003)
Body fat, standing long jump, 15
yard shuttle run, 40 yard dash,
throw, catch, kick and run
Grade 6, 7
and 8
Talent
identification
inventory.
Estonian Talent
selection
procedures
(Loko, 1994)
30 m sprint from standing start, 10 s
maximal speed running on the sport,
3 x 10 m shuttle run, 5-minute run,
Standing long jump, Vertical jump,
Medicine ball throw with two hand
from sitting position, Pull-ups,
Push-ups, Flexibility.
Not reported The preliminary
selection phase.
Rugby Union –
Australia (Gibson
et al., 1999)
Strength, Aerobic fitness,
Balance whilst running with ball,
Speed, Agility, Power,
Acceleration, Flexibility
12-17
years old
Coach checklist had
been used to identify
rugby union individual
skill attributes
Soccer - UK
(Reilly et al.,
2000)
20 m progressive run tests (aerobic
performance), 5, 15, 25, 30 m
sprints (anaerobic performance),
40m sprints with turn (agility),
Repeated sprints (speed endurance),
Vertical jump (power).
15-17
years old
Talent identification in
soccer.
Swimming –
Canada
(Poppleton &
Salmoni, 1991)
Height and weight, grip strength,
shoulder, trunk and ankle flexibility
8-17
years old
Involving other
instruments – swim
performance,
perceived competence,
family sport history
and maturational
status.
21
Specifically, the two motor ability constructs of balance and movement coordination were
examined along with previously developed TI tests from Australia’s Talent Search Program
(Australian Sports Commission, 1998). The selection of balance and movement coordination
components from the Movement Skill Foundations Checklist by Burton and Miller (1998)
was based upon their strong evidence regarding the importance of balance and coordination
in skilled sporting performances.
Balance: The inclusion of balance in many motor ability tests is indicative of its
importance in movement skill proficiency (Burton & Miller, 1998). Balance is generally
described as either static or dynamic (Bass, 1939). Static balance involves maintaining a
position while the body is stationary, whereas dynamic balance involves maintenance and
control of body posture when moving. Both kinds of balance also have been related to
general motor ability or GMA. Willgoose (1961) and Bass (1939) indicated that dynamic
balance reflected the strongest relationship with talented participants.
Balance was included here because there are no specific items of balance in the Australian
TI test. The balance tasks contain interactive effects of visual control, kinaesthetic senses,
body sway and base of support during upright stance. Including these items ensures that
visual control will either separate or merge these balance factors together. It also provides
the opportunity to construct pre-constructional aspects of postural control or postural
stability within motor ability. The pre-construction of postural control should be considered
because balance and postural control are more frequently viewed as synonymous terms
(Burton & Davis, 1992; Burton & Miller, 1998; Kollmitzer, Ebenbichler, Sabo, Kerschan &
Bochdansky, 2000; Slobounov & Newell, 1994; Westcott, Lowes & Richardson, 1997).
Coordination: Coordination is the ability to integrate the various parts of a body’s
muscular, motor and sensory systems, and the environment; without unnecessary tension,
and in proper sequence to perform movement skills harmoniously and rhythmically.
Movement coordination is a cooperative interaction of the neuromuscular system (Tittel,
1988) where groups of muscles are patterned or programmed within the integrative
processes of the CNS to work sequentially in a smoothly timed manner (Barrow & Brown,
1988). Coordination reflects an ability to perform movements of various degrees of
difficulty very quickly, precisely and efficiently (Bompa, 1990). Barrow (1977) indicated
that levels of coordination reflect the ability of an individual to integrate all types of
movements into specific patterns.
22
Coordination is a complex bio-ability (Bompa, 1990) and is closely interrelated with agility,
balance (Barrow, 1977), speed (Barrow, 1977; Bompa, 1990), flexibility (Bompa, 1990),
relationships of kinaesthetic sense; with the learner’s perceptions (Barrow, 1977; Barrow &
McGee, 1979), strength and endurance (Bompa, 1990). Broer (1973) inferred that
coordination consisted of timing the control of body parts and rhythmic movements which
involve speed, sequence and duration; and muscular control of voluntary movement
sequences which involve force, speed, direction and range of muscle contractions which
adapt easily to the purpose of the movement. Thus, coordination is the combining of simple
movements without unnecessary tension and in proper sequence to make a smooth complex
movement (Broer, 1973), and motor skills specifically designed to assess movement
coordination were included in the current research.
2.1.7. Sport Expertise
Sport expertise studies were influenced by the development of cognitive science and
cognitive psychology (Feltovich, Prietula & Ericsson, 2006), and by cognitive and
perceptual-motor skills (Ericsson & Lehmann, 1996). Generally, studies focused on the
factors, stages and process that influenced the development of sport expertise (Abernethy,
2005). Sports expertise is defined as consistently superior athletic performance over an
extended period (Starkes & Allard, 1993).
The purpose of conducting talent detection, identification, selection and development in
sport is to produce experts in specific sports. Several factors influencing sport expertise,
including nature versus nurture, and biological and environmental contributions, are still
matters of debate. Some research has found that deliberate practice is more influential than
innate talent, having reported significant correlations between the hours of specific sport
practice and levels of attainment (Baker, Cote & Abernethy, 2003; Baker, Horton,
Robertson-Wilson & Wall, 2003; Ericsson, Krampe & Tesch-Romer, 1993; Helsen et al.,
2000; Helsen, Starkes & Hodges, 1998; Hodges & Deakin, 1996; Hodges & Starkes, 1996;
Starkes, 2000; Starkes, Deakin, Allard, Hodges & Hayes, 1996). Others have maintained
that heredity is just as important in responses to training (Baker, 2001; Baker & Horton,
2004; Bouchard et al., 1999; Bouchard et al., 1998; Bouchard, Lykken, McGue, Segal &
Tellegen, 1990; Hopkins, 2001; Maes et al., 1996; Rankinen et al., 2001, 2002).
Extending these issues into empirical studies, Baker and Horton (2004) divided items that
could influence sport expertise into primary and secondary factors. Primary factors directly
influence acquisition of expert performance and include genetics, training and psychological
23
factors. The secondary factors were socio-cultural and contextual elements (Baker & Horton,
2004). Delineation of nature and nurture is an important step in considering talent
holistically, and would hopefully enable more complete approaches to future motor ability
research.
A heightened awareness of the need to examine sports expertise has led to current
investigations becoming more varied. Studies previously focused on individual differences
in perceptual-motor and information processing abilities, but now include other areas such as
sports expertise concepts in the TI and development processes (Abernethy, 1990, 1991;
Abernethy & Wood, 1999; Allard, Graham & Paarsalu, 1980; Allard & Starkes, 1980;
Starkes, 1987; Williams, Davids, Burwitz & Williams, 1994).
Assessments of individuals with talent, together with practice over a certain period of time,
and based on the requirement of that sport’s domain, are important to produce high
performers in sport. Hence, TI attempts to match innate or learned features in a given sport
to maximise the probability of finding exceptional athletes (Regnier et al., 1993). Talent or
ability recognition requires complex interactions, is dependent on environmental conditions,
is partially innate and internal, and acknowledges that different motor abilities underlie
motor performances in different sports (Williams & Franks, 1998). The notion that motor
ability underlies motor performance demonstrates the need to investigate the types of motor
ability being assessed by the motor performance tests in TI programs.
The success or failure of TI programs also depends upon matching a person’s ability with
the demands of the sport (Malina, 1997). At the upper levels of TI programs, other factors
such as experience, training, anticipation, game skills and ability to read the game become
increasingly essential because, generally, no significant differences in motor ability are
shown at the elite level (Allard & Burnett, 1985; Helsen et al., 2000; Starkes, 1987).
Furthermore, there is evidence of maximal adaptation to task constraints through deliberate
practice (Ericsson & Lehmann, 1996) and the element of enjoyment (Helsen et al., 2000)
which contributes to exceptional performance.
To summarise, the TI process has highlighted several issues surrounding talent
identification. The age of identification and the influence of both hereditary and
environmental factors need careful consideration. Also, more research is required to identify
motor skills related to successful performance in particular sports. The Australian TI model
was adopted by Malaysia to identify their talented sporting individuals but concerns have
been raised regarding the validity of their motor skill assessment in populations outside
24
Australia. Given a resurgence of interest in the TI program in Malaysia, the current research
sought to examine the Australian motor skill assessment more closely; along with other
motor skills which assess basic fundamental movement skills not assessed by this
instrument, for subsequent use in TI in Malaysia. Finally, TI is the first important step in the
development of sport expertise. However, there is an assumption that experts in particular
sports exhibit specific motor abilities that are associated with those sports. Thus, not only is
it important to assess individuals’ motor performances using a variety of motor skill
instruments, it is also important to understand what motor abilities these instruments are
assessing.
The rest of this chapter will review literature on motor ability.
2.2. MOTOR ABILITY
Many motor ability tests exist with various rationales and objectives for different age
groups, and contain different test items. The final selection of these items has been through
correlation analysis (Brace, 1927; Carpenter, 1940; McCloy, 1937), factor analysis (Alden,
Horton & Caldwell, 1932; Bruininks, 1978; Larson, 1941); or a combination of item
difficulty, item discrimination and correlation studies (Henderson & Sugden, 1992). In
addition, the selection of test items is based on different classification systems of motor
abilities and movement skills (Burton & Miller, 1998; Irvine, 1951).
Through the development of motor ability tests, studies relating to generality and specificity
have been conducted. The generality of motor ability (GMA) refers to the ability underlying
more specific abilities. Specific motor ability (SMA) refers to an ability that reflects a
characteristic from similar motor abilities. However, issues surrounding generality versus
specificity have disrupted further developments of general motor ability tests. The
acceptance or rejection of the concept of GMA is typically based upon interpretation at the
inter-correlational level (Burton & Rodgerson, 2001). The low and inconsistent levels of
inter-correlations reported in research (Burton & Davis, 1992; Drowatsky & Zuccato, 1967;
Fleishman, 1964; Harris, 1969; Rivenes & Sawyer, 1999) and review articles (Alderman &
Howell, 1969; Bachman, 1961; Battinelli, 1984; Henry, 1958, 1968; Macintosh, 1974;
Oxendine, 1967) has led to some doubts regarding the GMA concept.
However, Darlington (2002) wrote that such findings need not necessarily indicate that the
data fail to support the existence of GMA and fit the hypothesis, but merely show that the
variables analysed have little in common with each other. These statements suggest that,
25
whilst there is low common variance in composite scores and within-task correlations, these
discrepancies represent the GMA concept. Although extensive research on the GMA
concept by Fleishman (1954) was conducted 50 years ago, more recent re-examinations of
Fleishman’s data have led to either an acceptance (Chaiken, Kyllonen & Tirre, 2000) or
rejection (Rivenes & Sawyer, 1999) of the GMA concept. Thus, if GMA is to become a part
of movement skill assessment taxonomy, it needs further research when an opportunity
arises (Burton & Rodgerson, 2001). This study afforded such an opportunity. Barrow
(1977) indicated that general qualities of individuals have been divided into motor capacity,
motor ability, motor educability and motor fitness. A major focus in this study was motor
ability. However, to understand the precise nature of these general qualities, each is defined
to clarify the terminology adopted by this project.
2.2.1. Motor Capacity and Motor Ability
Motor capacity and motor ability nomenclature in movement assessment can be confusing
because both examine body development through the total performance of the individual
(Irvine, 1951). Differentiation between the underlying concepts of motor capacity and motor
ability is not clearly described and, while the term motor ability has been described
extensively, motor capacity has not.
Motor capacity has been defined as a wide-ranging quality which demonstrates individual
innate ability or potential to perform complex motor skills (Barrow, 1977; Irvine, 1951;
Johnson & Nelson, 1986; Philips & Wendler, 1950). McCloy (1934b) identified four
indicators of individual motor capacity; namely, size and maturity, power, motor educability,
and agility and coordination.
Motor ability entails traits or capacities of an individual when performing a variety of
movement skills (Burton & Miller, 1998; Burton & Rodgerson, 2001; Fleishman, 1964;
Magill, 1993; Sage, 1984) that are innate and acquired (Johnson & Nelson, 1986), and stable
over a long period of time (Barrow, 1977; Keogh & Sugden, 1985).
These definitions suggest that both concepts have similarities and are important components
of motor performance. The difference is that motor capacity is specific to an individual's
innate ability, whereas motor ability is regarded as both an innate and acquired (developed)
capacity (Irvine, 1951). In addition, motor ability is an indication of an individual’s
performance level, while motor capacity refers to an individual’s innate potential for motor
skills (Philips & Wendler, 1950; Willgoose, 1961).
26
2.2.2. Motor Educability and Motor Fitness
Motor educability is the ability to learn new skills rapidly (Baumgartner & Jackson, 1991;
Philips & Wendler, 1950). Barrow (1977) stated that motor educability implies that speed
and rate of learning motor skills will differentiate an individual’s level of motor
performance. Motor educability is related to maturation, size, physique and most of the
factors listed under components of movement (speed, power, balance, flexibility, hand-eye-
foot-eye coordination, coordination, kinaesthetic sense and accuracy), but very little to
strength and endurance (Barrow, 1977). Baumgartner and Jackson (1991) claimed that the
motor educability concept gained ground with developments in the field of intelligence.
Therefore, motor educability is sometimes called motor intelligence (Willgoose, 1961).
Motor fitness is the ability to perform basic motor skills involving elements of power,
agility, speed and balance (Johnson & Nelson, 1986). It is similar to motor ability only in
that motor fitness refers to individual traits or capacities that are heavily weighted towards
excessive stress and fatigue (Barrow, 1977). In addition, motor fitness rapidly improves with
training and conditioning (Burton & Miller, 1998; Clarke & Clarke, 1987; Johnson &
Nelson, 1986; Keogh & Sugden, 1985).
Thus, when describing movement behaviour, these general qualities tend to be used
interchangeably. Given the interrelated nature of the four qualities, one can understand why
confusion exists with their use. However, they are qualitatively different and each should be
used with care. This study focused on motor ability as it refers to the level of innate
individual traits for performing a variety of movement skills.
2.2.3. General Motor Ability (GMA)
General motor ability is an aptitude for doing many different motor performance things
(Willgoose, 1961). Specific to movement behaviour, GMA is a common factor that enables
certain individuals to perform well, or to quickly acquire a high level of proficiency, on any
motor task (Sage, 1984). Barrow and McGee (1964) defined GMA as the presently acquired,
innate ability to perform motor skills of a general or fundamental nature. Schmidt and Lee
(1999) refer to GMA as a single trait of a person that underlies performance in all movement
skills. Sometimes, GMA is recognised as the general athletic ability enabling one to excel in
sport (Barrow, 1977; Campbell & Tucker, 1967; Johnson & Nelson, 1986).
27
Two important components of GMA are ability and skill. General descriptions of these terms
are included to avoid any switching of terms in this study. Ability is an individual trait
underlying the performance of motor skills (Magill, 1997; Schmidt & Lee, 1999). Skill
requires voluntary body and/or limb movements to achieve a specific goal (Magill, 1997)
that results in maximum certainty with minimum outlay of time and energy (Guthrie, 1952).
Abilities are more general, and underlie simple and complex tasks; while skills are more
specific and narrow, and involve complex tasks (Henry & Hulin, 1989; Magill, 2001). Motor
abilities are the foundations for acquiring skills rapidly; and such skills are learned through
practice and rely on the presence of inherent abilities (Sage, 1984).
2.3. GENERALITY VERSUS SPECIFICITY OF MOTOR ABILITY
The concept of GMA is related to that of general intelligence, or ‘g’, in human psychology
(Kirkendall, Gruber & Johnson, 1987; Sage, 1984). Although still controversial, the ‘g’
factor has survived extensive investigation and is widely accepted (Aluja-Fabregat et al.,
2000; Caroll, 1993; Colom et al., 2000; Detterman & Daniel, 1989; Jensen & Weng, 1994;
Johnson et al., 2004).
Johnson et al. (2004), investigated the ‘g’ question from three different perspectives. The
first related to the ‘g’-loading on mental ability tests and the stability of a test when inserted
into other sets of tests. Secondly is the degree to which the ‘g’ loadings for tests depend on
the particular method of factor analysis used to extract the ‘g’ factor. The third perspective
relates to the consistency of the ‘g’ factor from one mental ability test battery to another.
Johnson et al. (2008) set out to replicate this finding in another sample by examining five
cognitive test batteries. Again, a second-order confirmatory factor analytic approach of the
test batteries was undertaken. Initially, exploratory factor analyses were performed on each
of the batteries in order to develop second-order factor models independently. From the first-
order analyses, several independent ‘g’ factors were found for each battery. Then, the
second-order confirmatory analysis found correlations between the five ‘g’ factors ranged
from .77 to 1.00 which indicated that the concept of ‘g’ as a unitary construct was evident.
The GMA is a complex concept which maintains that an individual has the ability to perform
a broad spectrum of activities (Clarke & Clarke, 1987). Also, the GMA assumes an
integrated composite of individual strength, endurance, power, speed, agility, balance,
reaction time and coordination that underlie performances in various complex motor skills;
as well as other physical, mental, emotional and social domains. The principle of generality
28
maintains that one could measure motor ability by simply measuring the height of a vertical
jump (Sargent, 1921, 1968). The notion was that a superior GMA would enable one to
perform well on any motor task (Burton & Rodgerson, 2001; Schmidt & Lee, 1999). Other
studies developed tests of motor ability to either classify students homogeneously for
physical education classes or to evaluate general athletic performance (Alden et al., 1932;
Brace, 1927; Carpenter, 1942; Cozens, 1929; Garfield, 1924; Humiston, 1937; Johnson,
1932; Kistler, 1937; McCloy, 1938; Powell & Howe, 1938, 1939; Scott, 1939, 1943). Brace
(1927) conducted 20 gross motor stunt tests of varying difficulty to measure individual
ability and suggested that these tests could classify students for physical education classes.
Cozens (1929) measured the general ability of college men and found that physical skill test
items adequately measured athletic ability. Also, he suggested that GMA was composed of
seven components of motor ability: (i) arm and shoulder coordination with implements, (ii)
arm and shoulder girdle strength, (iii) hand-eye, foot-eye and arm-eye coordination, (iv)
jumping, or leg strength and flexibility, (v) endurance or sustained effort body coordination,
(vi) agility and control; and (vii) speed of legs with coordination of the body. By
comparison, Barrow (1954) developed a motor ability test for college men. He
recommended two test batteries for predicting GMA and identified eight underlying
components: (i) arm and shoulder coordination, (ii) flexibility, (iii) power, (iv) hand-eye-
foot-eye coordination, (v) speed, (vi) strength, (vii) balance and (viii) agility.
Magill (2001) reported how different scholars have different notions of what actually
constitutes motor ability. Some agree with the notion of GMA underlying movement tasks
and performances, while others believe in specific motor ability (SMA). Controversy over
GMA versus SMA has arisen because of disputes regarding how they relate to one another
in the same individual (Magill, 2001). Further, there is disagreement regarding data
interpretation, and inconsistent use of terms and constructs (Burton & Rodgerson, 2001).
Interest in motor tests of generality was strongest between 1930 and 1960 (Baumgartner &
Jackson, 1975), but a change to specific tests was driven by Henry’s Specificity Hypothesis
(Henry, 1958, 1968). The theory of motor specificity was based upon the low inter-
correlations found between various motor performances (Burton & Rodgerson, 2001; Henry,
1958, 1968; Seashore, 1942). The general motor tests did not adequately predict many
different tasks on the basis of a single or limited number of test items (Bachman, 1961;
Burton & Davis, 1992; Macintosh, 1974; Schmidt & Lee, 1999). The SMA theory
considered that abilities were largely independent (Henry, 1958, 1968; Magill, 2001), task-
specific (Henry, 1958, 1968; Hensley & East, 1989); and that motor transfer between them
29
was generally low and positive (Schmidt & Lee, 1999). Evidence supported the concept of
SMA due to the low inter-correlations found between different reaction time and movement
time tasks (Henry, 1961; Loockerman & Berger, 1972), balance tasks (Burton & Davis,
1992; Drowatsky & Zuccato, 1967), throwing and kicking tasks for accuracy (Singer, 1966),
and strength tasks (Loockerman & Berger, 1972). The low correlation coefficients suggested
that the above-mentioned abilities were task specific, rather than representative of GMA .
Fleishman (1957) suggested that motor ability was general at first but became more specific
with practice. The instructional strategy either supports or refutes generality of ability
depending upon the individual learning stage. Specificity emerges as the skill elements come
together with greater practice. Fleishman’s (1958a) suggestion was evident in a study of 24
fine motor ability items which found low correlations between variables. He concluded that
abilities were specific in nature, non-transferable and task-specific.
Briefly, task generality or specificity needs further clarification via theoretical reasoning and
research evidence, because task and ability are not the same thing (Barrow, 1977). By their
nature, tasks are specific while ability is broader in scope. In addition, ability could be more
general because it includes different movement patterns rather than just an isolated task.
The GMA hypothesis claims that many different motor abilities of an individual are highly
related and can be characterised in terms of singular or global motor ability (Magill, 2001).
As motor abilities were usually identified through correlations or factor analyses, it was
assumed that the inter-correlations between the wide ranges of different motor abilities
would be fairly high. But, the statistical analyses of correlation, factor analysis, and higher
order and hierarchical factor analysis have resulted in correlation coefficients ranging
between 0.40 and 0.50 (Detterman & Daniel, 1989; Thorndike, 1987). However, Burton and
Rodgerson (2001) claimed that rejecting the GMA because of low correlations may not be
valid and should be re-evaluated. Despite low correlation coefficients, the factors in motor
ability can still be related and further investigations of the GMA construct should be
continued using methodologies capable of identifying ‘g’.
2.4. MOTOR ABILITY TESTS
Inspired by the general ability and intelligence concept, some physical educators consider
that individuals possess an inherent level of GMA and motor intelligence to perform motor
activities (Willgoose, 1961). Recognising the need to assess individual levels of GMA and
motor intelligence, motor behaviour researchers have developed several standard tests of
30
motor ability. The purpose of motor ability measurement is to determine individual
proficiency in specific movement skills and assess the factors that underlie a broad range of
motor skills.
Historically, the first motor ability test battery was reported in 1912, The Sigma Delta Psi
Test (Clarke & Clarke, 1987); and was continuously modified until the 1950s. During this
time, the motor ability tests were used to apportion physical education classes into
homogeneous groups (Alden et al., 1932; Barrow, 1954; Humiston, 1937; Johnson, 1932;
Kistler, 1937; Powell & Howe, 1938, 1939; Scott, 1939, 1943). Those instruments used a
single index or composite score to interpret individual GMA. The concept was that one who
learned and performed certain skills easily, would learn and perform others equally well
(Oxendine, 1967). Since then, research of motor ability instruments has used different
perspectives and research methodologies. However, the emergence of the SMA theory
slowed the investigation of motor ability constructs.
Subsequently, it was considered important to assess individual motor ability in order to
recognise and understand individual motor development or motor impairment. With
uncertainty surrounding task generality or specificity, the ‘motor ability’ phrase has resulted
in the use of terms such as motor proficiency, motor skill or motor performance. As the new
phrases became popular, motor ability issues were gradually blurred, and investigations into
GMA decreased.
With the research focus then tending to examine specific aspects of motor ability, several
test instruments were developed and widely accepted. Such instruments include the
Bruininks-Oseretsky Test of Motor Proficiency (Bruininks, 1978), Movement Assessment
Battery for Children – Movement ABC (Henderson & Sugden, 1992), Test of Gross Motor
Development (Ulrich, 1985, 2000) and the McCarron Assessment of Neuromuscular
Development - MAND (McCarron, 1982) (see APPENDIX A on the CD for a summary of
the motor ability test batteries). Although those tests did not clearly define GMA, they still
measured individual GMA. Thus, Burton and Rodgerson (2001) revisited the issue of motor
ability and supported the GMA concept by proposing a new movement taxonomy. This new
taxonomy highlights the need for further investigation on the underlying construct of motor
ability and GMA.
31
2.5. HUMAN MOVEMENT TAXONOMY
Motor ability tests have been developed over the last 90-100 years for different purposes and
age groups. Several developed taxonomies pertaining to movement skill assessment largely
draw on one another. As the study of GMA and human movement developed and expanded
the discipline areas, knowledge domains as well as taxonomies have also been transformed.
Guilford (1958) pioneered taxonomy in motor ability and human movement. He proposed a
two-dimensional system of motor abilities that classified ability on the basis of body parts
involved in the movement (Table 1, APPENDIX B on the CD). These classifications are
based on three types of test items; tests of physical fitness, apparatus-tests and printed tests,
and are supported by studies using factor analysis. The two-dimensional classification
system has several implications as other abilities could remain undiscovered. This ability
classification was specific to anatomical aspects and no categorisation had been allocated for
combined-factors such as power and agility (Guilford, 1958).
Therefore, Fleishman (1964) developed a classification system based on experimental
confirmation by using factor analysis (Table 2, APPENDIX B on the CD) to identify nine
physical proficiency abilities (Fleishman, 1964) and 11 psychomotor abilities (Fleishman,
1966). These classifications are widely adopted in studies of motor ability and the
psychomotor domain (Burton & Miller, 1998). Then, Clarke (1967) generated a
classification system pertaining to the psychomotor domain (Figure 1, APPENDIX B on the
CD). She set out to categorise components of GMA and demonstrate their roles in motor
fitness and physical fitness. This classification system showed that the same test item(s)
sometimes could be used to measure individual GMA, motor fitness and physical fitness
(Clarke, 1967).
Due to the need for a comprehensive taxonomy from different areas of research, Harrow
(1972) developed a taxonomy of the psychomotor domain which included six main levels in
the taxonomy: Reflex Movements, Basic-Fundamental Movements, Perceptual Abilities,
Physical Abilities, Skilled Movements and Non-Discursive Communication (Table 3,
APPENDIX B on the CD). The physical abilities that were highly related to motor abilities
were divided further into sub-categories of endurance, flexibility, strength and agility.
Harrow (1972) maintained that, if physical abilities were not adequately developed, the
achievement of highly skilled movement could be limited. Others agreed that motor ability
achievement would limit movement or define potential for success in a particular activity
32
(Hensley & East, 1989; Schmidt & Lee, 1999). Baumgartner and Jackson (1975, 1991)
included another three additional factors to the commonly used physical abilities noted
above. These were power, balance and basic movement patterns; that involve sprinting,
jumping and throwing (Table 4, APPENDIX B on the CD). In these taxonomies, the power,
balance and basic movement patterns mostly focused on the individual. As task and
environment influence movement, Knapp (1963) developed a classification of skills based
along a continuum of closed to open skills. Based on this continuum, Gentile (1987)
classified movement tasks into 16 levels according to how they were influenced by variation
in the environment and object manipulation (Table 5, APPENDIX B on the CD).
In 1998, Burton and Miller (1998) introduced a taxonomy of movement skills classified into
six levels, namely: movement skill foundations, motor abilities, early movement milestones,
fundamental movement skills, specialised movement skills and functional movement skills
(Figure 2, APPENDIX B on the CD). In the taxonomy of movement skills, the movement
foundations influence motor abilities. However, because tests of motor abilities do not
directly evaluate movement skills, motor abilities are isolated from the sequence of true
movement skills and assessed independently (Burton & Miller, 1998). This classification
also excluded motor ability from any developmental levels of movement skills on the basis
that motor ability evaluated movement competency.
Burton and Rodgerson (2001) revised this taxonomy of movement skills and GMA (Figure
3, APPENDIX B on the CD) into four primary levels – movement skills, movement skill
sets, movement skill foundations and GMA. In this taxonomy, motor ability was recognised
as a movement skill foundation. Thus, assessment of movement skill foundations would
identify factors that might limit or facilitate the functional outcome of a particular movement
skill. The existence of any new taxonomy of movement skills and GMA demonstrates the
need to clarify further the knowledge domain related to motor ability and GMA.
2.6. FACTORS UNDERLYING MOTOR ABILITY
Historically, Spearman (1904) investigated children’s performances on academic tests and
concluded that there was one general intelligence factor called ‘g’ which underlies
performance on all sub-tests. Inspired by Spearman’s study, Garfield (1924) investigated
motor abilities that underlie human motor performance. Larson (1941) sought to identify
common characteristics of the various tests in order to develop a motor ability test for
college men. In the first of two studies to develop the test, four motor ability factors were
33
identified from 33 test items. The four factors were dynamic strength, static dynamometric
strength, gross body coordination and abdominal strength. The second study analysed
dynamic strength with other motor ability tests. Factor analysis extracted four motor ability
factors: gross body coordination and agility, dynamic strength, motor educability and motor
explosiveness. Based on these findings, Larson (1941) created an Indoor Test Battery and an
Outdoor Test Battery. The Indoor test items were dodging run, bar snap, chinning, dipping
and vertical jump. A baseball distance throw, chinning, bar snap and vertical jump were the
items in the Outdoor test battery.
A series of factor analysis studies (Fleishman, 1954, 1956, 1957, 1958b) were conducted to
investigate basic abilities and motor skills. Fleishman (1964) identified six physical abilities
and 10 perceptual-motor abilities. The physical abilities included explosive strength, extent
(flexion and extension) flexibility, dynamic flexibility, gross body equilibrium, balance with
visual cues and speed of limb movement. The perceptual-motor abilities were control
precision, multi-limb coordination, response orientation, reaction time, speed of arm
movement, rate control, manual dexterity, finger dexterity, arm-hand steadiness, wrist-finger
speed and aiming.
After identifying physical and perceptual-motor ability factors, a test battery was developed.
Fleishman (1964) recommended a battery of Basic Fitness Tests classified by the factors
they measured. The 10 test items included extent flexibility, dynamic flexibility, shuttle run,
softball throw, handgrips, pull-ups, leg lifts, cable jump, balance and 600-yard run/walk. The
tests were claimed to measure: extent and dynamic flexibility; explosive, static, dynamic and
trunk strength; coordination, equilibrium and stamina. Previous factor analysis studies
acknowledged nine underlying factors of physical qualities and 18 underlying factors of
motor educability (Carpenter, 1941; Gates & Sheffield, 1940; McCloy, 1934a; McCloy &
Young, 1954). They were categorised as either orthogonal or oblique factors (see Table 3).
Oblique factors provided strong confirmation that second-order factor analysis was needed
to better explain motor ability and GMA (McCloy & Young, 1954). In addition, several
studies focused more on one element of motor ability such as coordination (Cumbee, 1954;
Cumbee, Meyer & Peterson, 1957), flexibility (Harris, 1969), muscular strength (Kollias,
Hatzitaki, Papaiakovou & Giatsis, 2001); or related areas such as physical fitness
(Baumgartner & Zuidema, 1972; Zuidema & Baumgartner, 1974) with an emphasis on
measuring individual physical fitness rather than motor ability (Fleishman, 1964).
As definitions of motor coordination were diverse, Cumbee (1954) investigated 21 motor
coordination variables among first year college women to determine what they actually
34
measured. Factor analysis with oblique rotation extracted eight factors. The factors found to
underlie motor coordination were balancing objects, tempo, two-handed agility, speed of
change of arm and hands, coordination, body balance and two unnamed factors. Cumbee
(1954) concluded that previous motor coordination test items and motor proficiency or
sports skills, cluster together a number of motor abilities.
Cumbee, Meyer and Peterson (1957) did a follow-up study on motor coordination ability
among third and fourth grade girls. Nine factors were extracted using factor analysis with
oblique rotation, and several were similar to those in the previous study. However, some
different factors also were found to underlie motor coordination ability. The factors
identified that were similar to those in the earlier study were: balancing objects, speed of
change of direction of the arms and hands, and body balance. Total body quick change of
direction, vertical body quick change of direction and four other unnamed factors were also
identified.
Comparative analyses on results from both studies by Cumbee (1954) and Cumbee et al.
(1957) showed that the inter-correlation matrices among elementary samples were more
specific when compared with those of the earlier college sample. Therefore, Cumbee et al.
(1957) suggested that a different definition of motor coordination for different age levels
should be considered.
35
Table 3. Categorised Factors Underlying Physical Qualities and Motor Educability.
Factors Group of Factors Type of factor
Physical Muscular strength Orthogonal
Qualities Speed of muscular contraction Orthogonal
Dynamic energy Probably oblique
Ability to change direction Probably oblique
Muscular endurance Orthogonal
Cardio-respiratory endurance Orthogonal
Agility Probably oblique
Dead weight Orthogonal
Flexibility Probably orthogonal
Motor Insight into nature of skill Probably orthogonal
Educability Depth perception Probably orthogonal
General-kinaesthetic sensitivity and control Orthogonal
Balance
Eyes and balance in movement in general
Eyes and balance in forward-and-
backward movement
Eyes and balance in sideward movement
Vertical semicircular canals and balance
Horizontal semicircular canals and balance
Tension-giving reinforcement
Kinaesthetic sensitivity and control
Orthogonal
Orthogonal
Orthogonal
Orthogonal
Orthogonal
Probably oblique
Not reported
Perceptual speed Probably orthogonal
Ability to visualise spatial relationships Orthogonal
Sensory-motor coordination I Orthogonal
Sensory-motor coordination II Probably oblique
Judgment concerning time, height, distance
and direction
Orthogonal
Coordination for complex unitary movement Orthogonal or oblique
Coordination for combination of movements Not reported
Arm control Probably oblique
Accuracy of direction Probably oblique
Sensory rhythm Not reported
36
Table 3 continued.
Timing
Duration of time
Insightful timing
Probably oblique
Probably oblique
Probably oblique
Motor rhythm Probably oblique
Quick and adaptive decisions Not reported
Aesthetic feelings Probably oblique
From: McCloy, C. H. & Young, N. D. (1954). Tests and Measurements in Health and
Physical Education. New York: Appleton-Century-Crofts Educational Division
Meredith Corporation.
In summary, research has found basic constructs underlying the test items measuring a
specific motor skill but, due to the specificity of the research, it has not enabled an
examination of GMA. This study included various motor skill measurements to examine
their underlying constructs and explore the possibility of the GMA construct.
2.7. DISCRIMINATING ITEMS IN MOTOR ABILITY
Several studies investigated test items which allocate individuals into different motor ability
or performance groups by using discriminant analysis (Gabbett, Georgieff & Domrow, 2007;
Rarick, Dobbins & Broadhead, 1976). Others have examined anthropometric and biomotor
ability variables among adolescent females (Leone, Lariviere & Comtois, 2002), male
athletes (Leone & Lariviere, 1998) and from different sports (Cagno et al., 2008; Cavala,
Rojulj, Srhoj, Srhoj & Katic, 2008; Douda, Toubekis, Avloniti & Tokmakidis, 2008; Falk,
Lidor, Lander & Lang, 2004; Lidor, Hershko, Bilkevitz, Arnon & Falk, 2007).
Leone et al. (2002) examined young female athletes aged 12-17 years who participated in
tennis, skating, swimming or volleyball. Findings from their first discriminant function
analysis showed that most of the variability reflected differences in anthropometric and bio-
motor variables between figure skaters and other composite groups of athletes. The
anthropometric measures showed that figure skaters obtained the lowest values when
compared with other athletic groups. From the bio-motor measures, figure skaters obtained
the best scores in burpees and trunk flexibility, but lowest in the push-up test. The flexibility
test significantly discriminated between figure skaters and tennis players (p < .05).
37
The second level discriminant function analysis showed that the anthropometric measures
also could differentiate between swimmers and volleyball players. Body mass, biceps brachii
and calf girths, and height were the best discriminators of these groups. The volleyball
players were taller and heavier than the swimmers, while the swimmers had larger bicep
girths than volleyball players. From the bio-motor perspective, the swimmers revealed
higher scores than volleyballers except for maximal aerobic power. However, the bio-motor
measures were unable to discriminate swimmers from other groups of athletes. The authors
concluded that anthropometric measures had greater potential to discriminate between
female athletes from four different sports than did the bio-motor measures.
Male participants from tennis, figure skating, cycling and gymnastics (Leone & Lariviere,
1998) demonstrated maximal aerobic power (MAP) and trunk flexibility discriminated
between gymnasts and the other athletes. In contrast, MAP and muscular endurance (burpee
test) best discriminated between cyclists and tennis players, although both groups recorded
similar anthropometric dimensions.
Gabbett et al. (2007) conducted a discriminant analysis on selected and non-selected junior
volleyball players to a high level squad to determine the performance data that would
discriminate between those selected and those not selected. The results of the study indicated
that passing technique and serving technique, but not physiological or anthropometric data
discriminated between successful and non-successful volleyball players.
Cagno et al. (2008) investigated leaping ability and morphological characteristics in
rhythmic gymnastics to determine the characteristics most useful for TI. Several factors
emerged when comparing elite gymnasts with sub-elite gymnasts. The elite gymnasts were
taller, had longer legs, more fat free mass and hopped higher than the sub-elite counterparts.
Cavala et al. (2008) examined a range of morphological characteristics, basic motor abilities,
and specific situational motor abilities in female handball players. They found that handball
performance was determined by agility and explosiveness, and by integrating basic motor
abilities of coordination/agility and all explosive strength types (throwing, running and
jumping). Morphologically, greater muscle mass also was found to be important in handball.
Douda et al. (2008) investigated anthropometric predictors of gymnastic performance. The
elite gymnasts recorded greater aerobic capacity, flexibility, explosive strength and
anaerobic capacity, but lower body mass and particular anthropometric characteristics than
their sub-elite counterparts. These qualities appear to be important in gymnastics.
38
Lidor et al. (2007) examined a battery of physical and motor tests, and two serving skill tests
were examined for their usefulness in the early detection and development of volleyball
players. Results indicated that only one physical explosive power movement, the vertical
jump with approach discriminated between starting and non-starting volleyball players. The
other speed tests, agility run, explosive power tests, endurance test and serving skill tests,
were not sensitive enough to distinguish between good and very good players.
Finally, Falk et al. (2004) found that the players selected for the National water polo team
already were skilled at swimming, handling the ball and game intelligence. Over the 2-year
period the selected players improved their swimming and throwing-the-ball-for-distance
than the non-selected players.
These studies indicated that anthropometric and bio-motor measures can discriminate
between athletes, and can serve as markers to help direct potential athletes into sports for
which they are most suited. Knowing these characteristics would also help streamline the
tests done during the TI mass screening phase.
2.8. MOTOR ABILITY STUDIES AMONG ADOLESCENTS
Generally, TI assesses motor ability during adolescence when body size changes and motor
performances significantly impact on proficiency in sports skills (Beunen et al., 1988).
While the variation in growth spurts and hormonal changes presents interpretative
challenges, individuals often select a main sporting direction at these ages. Hence, evidence
based guidance for choosing where maximum success might be achieved is timely and
effective. Espenschade (1940) conducted a longitudinal study of the relationship between
motor performance and age, gender, physical growth and maturity among 13-15 year old
adolescents. The test items were: distance throw (arm and shoulder girdle coordination);
target throw (hand-eye, arm-eye coordination); standing long jump, and jump-and-reach
(jumping or leg strength and flexibility); Brace test (body coordination, agility and control);
and the 50 yard (45.7 m) dash and dodging run (speed of legs). When comparing these
motor test scores with other indicators of motor performance, consistent agreement in
differentiating between superior and inferior subjects were reported.
Espenschade (1940) also found the mean performance of all boys to increase steadily in all
events with age but the girls were different in some events. The girls’ mean performances
improved but reached a maximum level around 14 years and decreased gradually thereafter.
A gradual decline was evident in the dash and long jump tests. However, the girls’ scores in
39
distance throws and Brace tests were fairly stable, and increased in the jump-and-reach
scores. Adolescent girls reached their maximum performances in certain events around 14
years, whereas adolescent boys continued to improve their motor performances through to
17 years old. Also, gender differences were noted at all ages and the older subjects
demonstrated bigger performance discrepancies than the younger children.
Later, a review of motor development changes from childhood to adolescence in the USA
showed similar trends in motor performance (Glassow & Kruse, 1960; Rarick & Smoll,
1967). This suggested that human motor development shows stability across age and gender,
with a few variations because of different cohort variations during childhood and
adolescence.
Beunen et al. (1988) carried out a 5 year, longitudinal growth and performance study of
Belgian adolescent boys. They obtained descriptive data with which to compare the distance
and velocity curves of other Belgian data, and to investigate adolescents’ changes in somatic
and motor characteristics based on chronological age. The study reported anthropometric
measures and motor ability tests which emphasised strength. The motor ability test items
(and factors identified) were bent-arm hang (functional strength), arm pull (static strength),
vertical jump (explosive strength), leg lifts (trunk strength), sit-and-reach (flexibility), 50m
shuttle run (running speed), plate tapping (speed of limb movement), stick balance (eye-
hand coordination) and one minute step test (pulse recovery).
In the chronological age-based distance and velocity curves, static and explosive strength
(arm pull and vertical jump) closely followed the reference group pattern, and increased
linearly; with the largest increase occurring from 14.5-17 years old. However, static strength
continued to increase while explosive strength reached a plateau at 17 years. Functional
strength also increased from 12.5 to 16 years, while trunk strength increased in a limited
fashion at discrete ages; namely, 14.0, 14.5 and 17.5 years. Speed of limb movement also
increased linearly between 12.5 and 16.5 years of age, and plateaued thereafter. Running
speed performance improved from 12.5 to 17.5 years, while flexibility increased from 13.0
to 17.5 years. Running velocities and speed of limb movements declined steadily after 13-14
years of age but the velocity curves of strength and flexibility remained stable.
Proctor & Ruhling (1981) studied adolescent female athletes and non-athletes to determine
the similarities in seven selected motor characteristics. Subjects participated in basketball
and gymnastics, and non-athletes acted as controls. The seven selected motor tests were arm
circling, balance test, basketball wall pass, dash, leg rise, stand and squat, and the standing
40
long jump. The stepwise discriminant analysis showed that only dash, stand and squat, and
balance tests discriminated between the three groups. Based on the constants and
coefficients from those three variables, Proctor and Ruhling (1981) developed equations to
predict individual future group allocation.
Also, a cross-cultural study measured motor abilities between the two Baltic countries of
Estonia and Lithuania by using EUROFIT tests (Jurimae & Volbekiene, 1998). Participants
were 11-17 years old and selected from schools in every division of towns in each country.
The EUROFIT motor ability test items were sit-and-reach, hand-grip strength, standing long
jump, 10m x 5 m shuttle-run, plate tapping, bent-arm hang, sit-ups and 20m endurance
shuttle-run. No significant differences were found in anthropometric measures between the
Estonian and Lithuanian children. The Estonian boys and girls scored significantly higher in
the 20m endurance shuttle-run than did Lithuanian children of similar age and gender,
except for the 14-year old boys. Generally, Estonian children scored higher on hand-grip
strength, 10 x 5m shuttle-run, sit-and-reach (among girls aged 12-15 years) and flamingo
balance (girls). On the other hand, the Lithuanian boys scored higher in bent arm hang than
Estonian boys of the same age. Jurimae and Volbekiene (1998) indicated that, despite the
Estonian and Lithuanian children’s motor abilities being comparable with other European
children, performances were influenced by environment factors. Environmental influences
were observed in gender differences and it was suggested that such influences are vital prior
to puberty (Jurimae & Volbekiene, 1998; Thomas & French, 1985).
Several studies of anthropometric variables and developmental changes in motor abilities
related to age have been conducted on boys (Cheng, 2001; Planinsec, 2001; Viru et al.,
1998) and girls (Kim, French & Spurgeon, 1999; Little, Day & Steinke, 1997; Loko, Aule,
Sikkut, Ereline & Viru, 2003; Viru et al., 1998; Volver & Selge, 1997; Volver, Viru & Viru,
2000). Anthropometric variables and improvement in motor abilities are associated with the
periodical acceleration of changes in adolescence. In most cases, age periods have been used
as an index for monitoring accelerated improvement in motor abilities. Other studies have
employed maturational stages as a reference when assessing motor ability performance.
Table 4 summarises different time periods in which peak rates of motor ability
improvements were found among adolescents (Viru et al., 1998).
It is difficult to interpret different rates of improvement in motor abilities according to age
and gender, and large growth and development variations during adolescence, when
selecting exceptional abilities among children in a TI program. It is a complex mission
involving factors other than just motor ability. Fleishman (1957) suggested that motor ability
41
was, initially, a general trait that became more specific after practising the motor skills. As
the goal of TI is to recognise talent or expertise, the latter phase is the one in which talent is
domain specific.
Table 4. Rate of Motor Ability Improvements.
Motor Abilities Boys (age) Girls (age)
Speed 7 – 8 and 14 - 15 years old 8 – 9 and 12 – 13 years old
Explosive strength 7 – 9 and 13 – 16 years old 6 – 8 and 11 – 12 years old
Muscle strength 14 – 16 years old 12 – 13 years old
Aerobic endurance 11 – 15 years old 11 – 13 years old
From Viru et al. (1998)
In summary, motor ability and talent in sport are important concepts that portray a human
motor ability continuum. The given definitions of ‘raw talent’ or giftedness, and ability,
show that both elements are similar under the same continuum, and represent an individual,
internal and innate capacity. Abilities are general traits which underlie capabilities that
support many skills (Schmidt & Lee, 1999) whereas being expert refers to an exceptionally
high skill level that is relatively domain specific (Howe et al., 1998). Researchers now use
the concept of raw talent extensively to predict exceptional abilities (Howe et al., 1998) and
to develop expertise (Ericsson & Lehmann, 1996).
Generally, TI programs use mass, sport specific and talent development screening; and
motor ability affects performance in these processes. Thus, thoroughly assessing motor
ability would assist in identifying potential athletic talent to maximise opportunities for
success via appropriate training in sports for which they are best suited. The present study
set out to do this within a Malaysian context.
Further investigation of inter-relationships, factors across gender and age, accuracy of
standard tests with other TI tests and motor ability instruments, should confirm previous
evidence of the value of basic level TI assessments. Identifying potentially talented athletes
via scientifically based motor ability tests could possibly affirm the existence of GMA.
42
CHAPTER 3
METHODS AND PROCEDURES
Approval for the study was granted by The University of Western Australia Human Rights
and Ethics Committee. Prior to any testing, procedures followed three distinct phases over a
six month time period. The first phase was selection of motor skill instruments.
3.1. INSTRUMENTS.
Three motor skill instruments in the current research are summarised below.
i. McCarron Assessment of Neuromuscular Development Test (MAND) (McCarron,
1982) - was selected because, unlike other standardised motor ability tests, it can measure a
range of ages from three years old to young adults. Also, age based test norms are provided
for grip strength and jumping items, and norms according to gender and age are available
which start at the 14 year old group. Other motor ability tests generally focus on children
and young adolescents. For example, the Movement Assessment Battery for Children can be
used with children aged 4-12 years old (Henderson & Sugden, 1992). The Test of Gross
Motor Development is for evaluation of gross motor skill development of children aged 3-
10 years (Ulrich, 1985, 2000). Finally, the Bruininks-Oseretsky Test of Motor Proficiency
(Bruininks, 1978) measures gross and fine motor abilities among 4-14 year old children, but
does not include separate norms for gender. However, it acknowledges that gender
differences should be considered. The MAND consists of five fine motor tasks and five
gross motor tasks. The fine motor tasks are beads-in-box, beads-in-rod, finger tapping, nut-
and-bolt, and rod slide. The gross motor tasks include hand strength, finger-nose-finger
movements, jumping, heel-toe-tandem-walking and standing-on-one-foot.
ii. Australian Institute of Sport Talent Identification Test (AIS) (Australian Sports
Commission, 1998) – selected because the Australian Sports Commission (1998) found that
the 40m sprint measures a speed component, the basketball throw measures upper body
strength, the vertical jump measures ability to spring in a vertical direction, and the
multistage fitness test measures aerobic fitness. The tests included in this battery are height,
weight, 40m sprint, vertical jump test, basketball throw and multistage fitness test.
43
iii. Balance and Movement Coordination (BMC) Test – was developed by the author for
this study because the Movement Skill Foundations Checklist by Burton and Miller (1998),
and considerable research, recommended including two components of motor ability,
namely, balance and movement coordination. (see APPENDIX C on the CD for additional
detail for the selection of the motor skills for the BMC). These were measured via field-
based tests. There were three motor skills of balance and six motor skills of movement
coordination making up the BMC. The balance skills were one-foot-balance-with-eyes-open,
one-foot balance-with-eyes-closed and dynamic balance. For movement coordination, the
motor skills were shuttle-run-without-object, shuttle-run-with-object, hopping-in-square,
hopping speed, zigzag run and quadrant jump. The BMC motor skills have good test-retest
reliability but, as the BMC was developed for this study, details of the reliability analysis are
in Chapter 5. The motor skills included in the BMC were based on a task analysis of the
requirements of the activity. The justification for including tasks in order to test for a
particular ability is provided below.
• Balance ability
Static balance was assessed via a one-foot-balance-with-eyes-open and a one-foot-balance-
with-eyes-closed. Balancing on one foot is commonly used to measure static balance (Bass,
1939; Burton & Davis, 1992; Johnson & Nelson, 1986; Largo, Fischer & Calflisch, 2002;
McCarron, 1982). To avoid ceiling effects among participants, difficulty was increased by
subjects holding a rod overhead with outstretched arms to hold the balance for a 60s
maximum time on each foot, with eyes open and closed. Dynamic balance has been tested
using sidesteps with the legs together while jumping sideways (Largo et al., 2002). In this
study, participants jumped sideways continuously for 10s.
• Movement coordination ability
Six motor skills were selected to measure movement coordination ability: shuttle-run-
without-object, shuttle-run-with-object, hopping-in-square, hopping speed, zigzag run and
quadrant jump. Generally, running has been considered an adequate test of gross body
coordination (Pyke, 1986; Williams, 1983). However, task analyses on the shuttle-run-
without-object, shuttle-run-with-object and zigzag run, have indicated that these tasks also
involved propulsion, running action and ability to change directions, while moving and
executing the manoeuvres continuously. Propulsion requires a maximum force to initiate
forward motion. As coordination ability governs and organises movements, the adjustment
of timing and muscular control (Broer, 1973) fine tunes the organised movement for
efficient performances based on the task demands. The adjusted elements of reaching objects
in a shuttle run (amplitude and displacement), changing direction when performing a zigzag
44
run and quadrant jump tasks (ability to adjust movement based on context and specific task
demands) requires specific muscular control of force, speed, direction and range of
movements. In addition, the ability to hop continuously in a stationary position (hopping-in-
square) or moving forward (hopping speed) requires strength, coordination, balance and
rhythm. The coordination factor refers to the ability to perform simple movements without
unnecessary tension and in proper sequence (Broer, 1973) to effect a smooth complex
movement (Fleishman, 1964). Hence, the six motor skills chosen were considered
sufficiently complex to elicit varying degrees of coordination in this study.
3.2. TRANSLATION OF TEST INSTRUMENTS INTO MALAY.
As the subjects were Malaysian, and Bahasa Malaysia (Malay language) was their first
language, instructions and descriptions of all instruments needed to be translated into Malay.
Thus, the second phase involved translating the instructions and procedures of those tests for
use in Malaysia, and a pilot evaluation of the translations. Initially, the translation of the
MAND, AIS and BMC tests involved preparing a preliminary Malay version by two
bilingual individuals and the author. This provided an initial Malay version of the
instruments, which focused on presenting the clarity and quality of the Malay language into
a relatively simple format, compatible with the education levels of the proposed subjects.
These initial translations then were scrutinised further by two bilingual translators
(Malaysian postgraduate students who studied at UWA and taught English as a second
language at university level in Malaysia) and the author, and modifications were made to
improve the clarity of the test instruments. This was a lengthy process and, approximately 3
weeks after the first meeting, a second meeting was held to discuss, evaluate and reach
agreement on the Malay language terminology selected by the two translators from a
language perspective; and the author and one Malaysian sports scientist as content experts.
In the final part of the translation phase, the experimental version of the test instruments was
pre-tested by two Malaysian high-school students (1 x 13 year old girl; and 1 x 14 year old
boy) and three Malaysian college-students (mean age of 18.3 years). Malay was their first
language, and they read and demonstrated the Malay language versions of the three motor
ability test batteries. The author and a Malaysian sports scientist assessed their movements
while they performed the tests, and monitored the parallel movements via the Malay
instructions for the test instruments. Comments and suggestions from the students were
taken into account when fine-tuning the next version. Each pilot test was conducted
individually to prevent any subject copying another student’s interpretations of the language
45
when following the instructions. This fine tuning stage took 3 weeks to finish as the
participants performed the testing over weekends and the researchers re-drafted the
instructions during the week. During the developmental phase, participants also were
encouraged to question anything that was unclear about the instructions and test items.
Subsequently, they provided some minor changes on task instructions and test items for the
final Malay version of the three motor ability test batteries. The author and Malaysian sport
scientist had a final discussion regarding the suitability and relevance of the final
instructions. Then, the instruments were cleared to use for data collection (see APPENDIX
D on the CD). This whole process required more than 6 months of full-time development
and fine-tuning.
The third phase of the investigation involved accessing permission to proceed from the
District and State Departments of Education, the Malaysian Ministry of Education; and then
having actual schools and students agree to participate (see APPENDIX E on the CD for
letters of permission). An attempt at stratified sampling was made initially but this was
hampered by school principals’/teachers’ perceptions of interruption to student class time
and use of sporting facilities. Hence, convenience sampling was used.
3.3. PARTICIPANTS.
Three hundred and thirty students (165 boys and 165 girls) aged 12-15 years were recruited
from two Malaysian high schools (see Table 5). The mean age for girls was 13.4 ± .99 years
and the mean age for boys was 13.1 ± .98 years. Facilities were made available for the
indoor and field tests over the four months of time needed for actual data collection.
Table 5. Numbers and Percentages of Participants in the Research.
Number of Participants
Age (in years) Boys Girls Total %
12 55 37 92 27.9
13 53 54 107 32.4
14 41 49 90 27.3
15 16 25 41 12.4
TOTAL 165 165 330 100
46
3.4. ADMINISTRATION OF THE TESTS
The participants were performance tested on the AIS, MAND and BMC motor skill
instruments. A full description of these instruments is in APPENDIX F on the CD. The AIS
instrument was administered to the adolescents following the methods outlined by the
Australian Sports Commission (1998). The MAND was administered using the methods
outlined by McCarron (1982). The BMC instrument was specifically designed for the
current research and included motor skills focusing on movement coordination and balance
(see APPENDIX F on the CD).
A pilot study investigated the test-retest reliability of the BMC, MAND and AIS tests.
Thirty-three subjects aged 13 years (20 boys, 13 girls) attended two test-retest sessions.
These took place separately during two scheduled physical education lessons in the same
week, and two days apart. Identical procedures were followed at both sessions. During both
counterbalanced testing trials, instructions were offered verbally because students were keen
to perform the tests rather than increase time by reading the instructions. The means,
standard deviations and reliability scores for each test item are presented in Chapter 5.
The first testing session was conducted indoors and participants were measured individually
on the MAND motor skills in counterbalanced order. Then, in pairs, they moved from
station-to-station for height and weight measures, one-foot-balance-with-eyes-closed and
one-foot-balance-with-eyes-open (BMC test), and vertical jump (AIS test), alternately. For
the second session, groups of eight participants performed the other motor skills of the AIS
and BMC in a counterbalanced sequence. The participants had adequate rest between each
test while waiting their turn. The sessions were conducted on the school field and subjects
wore suitable physical education clothing (tracksuit, t-shirt, rubber-soled shoes).
3.5. DATA ANALYSIS
Several stages of data reduction and analysis were then performed.
i. Initially the data were screened for normality. Where there were issues regarding
some of the variables they were transformed and the analyses run again. In all cases
the analyses with corrected data the resuls were the same as that found for the
uncorrected data. Thus, the results of the analyses with uncorrected data are
presented.
47
ii. The MAND and the NDI - from the raw scores of participants’ fine and gross motor
tasks, a scaled score was derived using age-appropriate norm tables provided in the
MAND manual (McCarron, 1982). The Neuromuscular Development Index (NDI)
was obtained by summing the scaled scores of the fine and gross motor averages,
and converting the values using the tables provided (see APPENDIX G on the CD).
The NDI was based on a distribution with a mean score of 100 and standard
deviation of 15. The Neuromuscular Development Index (NDI) score determined
participants’ levels of fine and gross motor skills. Descriptive information was
obtained for the NDI.
iii. Reliability of The MAND, AIS and BMC - The MAND, AIS and BMC motor skill
instruments all underwent test-retest reliability procedures prior to formal testing.
iv. Factor analyses were applied to determine the motor abilities underlying each of the
MAND, AIS and BMC motor skill test batteries. Specifically a Principal
Components Analysis (PCA) using the orthogonal (VARIMAX) rotation method
was undertaken to reveal the underlying motor abilities. To remove the possible
confounding effect of chronological age and gender differences, all the motor skill
raw scores of the MAND, AIS and BMC were standardised separately for each
gender by age-group classification. They were then transformed into T-scores based
on means and standard deviations, for each gender and age group, and used in the
subsequent analyses. Following an initial examination of the motor abilities
underlying the MAND, AIS and BMC; a test for a ‘g’ in motor ability was
undertaken via a factor analysis which proceeded in two stages. A first-order
analysis was performed to determine the components underlying the combined
AIS+BMC motor skills set and was followed by a higher-order factor analysis of the
first-order components to test for ‘g’. A PCA analysis with oblique (PROMAX)
rotation method (Rummel, 1970) was applied to reveal the underlying AIS+BMC
components, and a higher-order factor analysis was performed on these components.
The PROMAX oblique rotation method was used as this allowed factors to be
correlated (Bohman, Heger, Smith, Barker & He, 1995) and was appropriate for
second-order (Thurstone, 1947) or higher-order (Rummel, 1970) factor analysis.
v. Descriptive statistics of the MAND, AIS, and BMC were then generated to provide
normative information for future use in Malaysia. The raw scores of the MAND,
AIS and BMC were utilised in these analyses. Additionally, a series of ANOVAs
also were conducted to examine for age and gender effects. In total, there were 11
48
separate analyses and a Bonferroni correction was applied to the significance value
of .05. The resulting p value used was .005. Effect sizes and Confidence Intervals
were also calculated to clarify the meaningfulness of any significant results.
vi. Finally, stepwise discriminant function analyses were used to identify the motor
skills that reliably categorised the participants into either one of three motor
coordination groups (i.e., Poor, Normal or High) based on their MAND scores or
motor ability groups (i.e., Low, Normal or High) based on their motoric ‘g’ scores.
Although it might not be strictly correct to use the 13 motor skills to create the ‘g’
scores, and then use the same skills to find the best sub-set of motor skills to reliably
discriminate the 3 motor ability groups derived from these ‘g’ scores, this was done
to see if a framework grounded in sport (i.e., the AIS+BMC) would provide better
separation than one grounded in disability (i.e., the MAND). To remove any
confounding effect of chronological age and gender differences, all the motor skill
raw scores of the AIS and BMC were standardised separately for each gender-by-
age-group classification. They were then transformed into T-scores based on means
and standard deviations, for each gender and age group and used in the discriminant
analysis. The discriminant function analysis was conducted on all participants only
due to insufficient numbers in the low motor coordination/ability groups for the
boys. The statistical methodology employed was to derive the discriminant function
via stepwise estimation. This approach reveals the best set of motor skills that can
discriminate between the three groups. Then, an examination of the discriminant
functions was made to note any motor skills of importance that, due to collinearity
issues, were not present in the stepwise findings. The fit of the discriminant analysis
was then assessed via jackknife classification using prior probabilities to account for
the unequal sizes of the groups. The jackknife classification procedure was
employed because the sample was unable to be split to allow for cross validation of
the discriminant findings. This procedure estimates the discriminant model by
leaving out one observation and then predicting that case with the estimated model.
As this is done in turn for each observation, that observation never influences the
discriminant model that predicts its classification (Hair et al., 1998). So, jackknifed
classification gives a more realistic estimate of predictors and can separate the
groups (Tabachnick & Fidell, 2007). Finally, the adolescents who were misclassified
were examined to understand the nature of these individuals. For the
misclassification examination, the standard predicted membership was based on the
standard classification procedure, not the jackknife procedure. Thus, the
misclassified groups’ n may differ from that used for the jackknife classification.
49
Factor analyses and discriminant function analyses are highly sensitive to outliers and
missing data (Coakes & Steed, 1997; Tabachnick & Fidell, 1989). Additionally,
standardising scores is essential if variables are measured on different scales (Field, 2000).
Cases with standardised scores in excess of +3.00 standard deviations are potential outliers
(Tabachnick & Fidell, 1989) and, usually, they are deleted. As this study sought to identify
talented participants in which outlier data are important, data that exceeded +3.00 standard
deviations were retained. All data were analysed using SPSS software 12.00 (SPSS Inc,
2004). As the description of results will be presented in chapters 4, 5 and 6, a summary of
the statistical analyses is described in Table 6.
50
Table 6. Motor Skills, Scoring Method & Statistical Analyses from Chapters 4-6.
Test
Items
Score and
Statistical Analysis
Score and
Statistical Analysis
MAND Bead in box Used raw score to
obtain the percentile
score and for ANOVA
analyses in Chapter 5
Used standardised T-
scores for First-Order
Factor Analysis, and
Discriminant Function
Analyses
Bead in rod
Finger tapping
Nut and bolt
Rod slide
Hand strength
Finger-nose-finger movements
Jumping
Heel-toe tandem walking
Standing on one-foot
Note. The NDI was obtained by summing the scaled scores of each item and converting the
values by using the tables provided in the manual.
AIS Height Used raw score to
obtain the percentile
score and for ANOVA
analyses in Chapter 5
Used standardised T-
scores for First and
Second Order Factor
Analyses, and
Discriminant Function
Analyses
Weight
40 m sprint
Vertical jump test
Basketball throw
Multistage fitness test
BMC One-foot balance with eyes open,
One-foot balance with eyes
closed
Dynamic balance
Shuttle run without object
Shuttle run with object
Hopping-in-square
Hopping speed
Zigzag run
Quadrant jump
Note. The ‘g’ score was obtained for each participant by deriving individual factor scores
from the higher-order factor analysis testing a motoric ‘g’.
51
CHAPTER 4
FACTOR ANALYSES OF THE MOTOR SKILL INSTRUMENTS
This chapter discusses the underlying constructs of the motor skill test batteries used in the
study; and whether statistical approaches, such as higher order factor analysis, can provide
evidence to support the concept of GMA, and if this is gender specific. Section A examines
the factor structure of the motor skill tests. Burton and Miller (1998) contended that motor
skill instruments must suitable for the population being studied and measure what the author
purports it to measure. This is particularly pertinent to the current Malaysian research study
as both the MAND (McCarron, 1982) and the Australian Institute of Sport Talent
Identification Instrument (Australian Sports Commission, 1978) tests were developed with
non-Asian populations. Also, the MAND (McCarron, 1982) was specifically developed as a
diagnostic tool for neuromuscular difficulties that may indicate broader problems such as
mental retardation and neurological dysfunction. However, in this study, the MAND served
to help categorise the adolescents into three levels of basic motor skill and coordination for
analysis purposes. McCarron (1982) provided details of two factor structures. The first was
derived from a disabled population and supported a 2-factor fine and gross motor skill
model. The second was derived from a normal population and resulted in a 4-factor model.
The four motor ability components found were persistent control (rod slide and finger-nose-
finger movement), muscle power (finger tapping, hand strength, and jumping), kinaesthetic
integration (beads-in-box, heel-toe-tandem-walking, and standing-on-one-foot), and bi-
manual dexterity (beads-on-rod, and nut-and-bolt). However, subjects came from the USA
and the two factor structures needed testing for their relevance with Malaysian adolescents.
The AIS instrument identified four motor ability components that were important for
identifying athletic talent in Australia. It consists of four motor skills that individually tap
into each of speed, vertical jumping, upper body strength and aerobic fitness. This is an
efficient way of measuring several motor abilities but, whether this model of motor abilities
holds up outside an Australian context (i.e., Malaysian adolescents) remains to be seen.
In contrast to the AIS instrument, the BMC is a motor skill instrument specifically
developed by the author to measure motor ability components not assessed by the AIS
instrument. The BMC assesses coordination and balance, two items of Burton’s (1993)
52
movement skills foundation checklist (Burton & Miller, 1998). These two movement skill
foundations are essential to fundamental movement and an exhaustive library search was
undertaken focusing on motor skills that assessed either balance or movement coordination.
For each motor skill uncovered, a task analysis of that skill was then undertaken to ascertain
whether that skill reflected fundamental movement (i.e., balance, running, jumping and
hopping) with additional action (i.e., static, in-motion or change direction). This search
uncovered nine motor skills for inclusion in the BMC that assessed movement coordination
(i.e., the shuttle-run-without-object, shuttle-run-with-object, hopping-in-square, hopping
speed, zigzag run and quadrant jump); and three motor skills that assessed balance (i.e., the
one-foot-balance-with-eyes-open, one-foot-balance-with-eyes-closed and dynamic balance).
Factor analysis was anticipated to support the existence of these two motor abilities.
Section B examines the concept of GMA. Burton and Miller (1998) noted that, in order to
assess motor skills adequately, at least two motor skill instruments that assess unique motor
skills are necessary. Given that the current research was focused on finding motor skills
relevant in helping identify athletic talent of Malaysian adolescents, the AIS and BMC
motor skill instruments were combined to form the AIS+BMC instrument. This met Burton
and Miller’s (1998) recommendation and also provided more motor skills than either the
AIS or BMC alone, could provide a better opportunity to examine the existence of a ‘g’ in
motor ability. The MAND was not included in these analyses as the motor skills assessed by
the MAND were more suited to a basic level recognition of individual motor problems.
However, the AIS and BMC are made up of motor skills that, at face value, appear to
provide a wider range of motor skills more pertinent to sport. There are several approaches
that can be used to test for the existence of GMA (Burton & Richardson, 2001). The
approach used here was higher order factor analysis (Rummel, 1970). Given the exploratory
nature of these analyses, no formal hypotheses were made on the motor abilities arising from
such a pairing. However, from the first-order factor analysis of the AIS+BMC, the
underlying factors or motor abilities were subjected to a second order factor analysis to test
for a ‘g’ in motor ability.
SECTION A - FACTORS UNDERLYING THE MAND, AIS AND BMC
4.1. RESULTS
Initially, the raw scores from the motor skill tests were transformed into z scores for each
gender-by-age-group classification, then into T-scores to eliminate the effects of gender and
age. A series of Principal Component Analyses (PCA) were performed separately on the
53
MAND, AIS and BMC instruments. An examination of the Kaiser-Myer-Olkin and
Bartlett’s Test of Sphericity indices indicated that the data were appropriate for factor
analysis (Field, 2000). Specifically, the MAND item interdependence was suggested by a
significant Bartlett's test of sphericity of 197.043 (p < .001) with a Kaiser-Meyer-Olkin
sampling adequacy statistic of .62. The AIS test reported a Bartlett's test of sphericity of
103.495 (p < .001) and a Kaiser-Meyer-Olkin sampling adequacy statistic of .64. For the
BMC test, a Bartlett's test of sphericity of 978.979 (p < .001) and the Kaiser-Meyer-Olkin
sampling adequacy statistic was .83 was found. While KMO values higher than .90 are
desirable, scores around .60 are tolerable.
Decisions about the number of components to be extracted were based upon Kaiser’s
criterion (eigenvalues > 1) and Cattell’s screen test. To ease interpretation of the pattern
matrix, an orthogonal rotation method using the VARIMAX rotation was conducted on each
motor skill battery (Coakes & Steed, 1997; Rummel, 1970).
Results for each factor analysis consist of rotated factor loadings > .10. However, the factor
interpretations were based on the variables with loadings of ≥ .40. This more conservative
loading was chosen, given the sample size (N = 330) and number of variables being analysed
in the smallest test battery (i.e., four in the AIS) (Hair, Anderson, Tatham & Black, 1998).
4.1.1. The MAND
McCarron (1982) developed the MAND to assess fine and gross motor ability at a basic
level. The instrument is primarily used to identify motor problems that underscore
neurological dysfunction within individuals. McCarron (1982) provides details of a 2-factor
psychometric model and a 4-factor psychometric model based upon the development work
of the MAND. The 2-factor model consists of the fine and gross motor components derived
by McCarron (1982) from a sample of motor challenged children. The second is a 4-factor
model that McCarron (1982) reported for a sample of normal children aged 7 years (see
APPENDIX H on the CD for the CFAs for these two factor structures).
For the sample of Malaysian adolescents, a Principal Component Analysis (PCA) with
VARIMAX rotation of the MAND’s 10 motor skills extracted three components. Results
showed a modest 43.91% of the total variance was explained by these components. The
eigenvalue, loadings greater than .10 and the intercorrelation matrix are presented in Table 7.
Component One: The motor skills that loaded substantially were: heel-toe (.705), balance
MAND (.639), finger-nose-finger (.587) and jumping (.518).
54
Component Two: This component had loadings on: beads-on-rod (.723), beads-in-box (.708)
and nut-and-bolt (.596).
Component Three: High loadings for this component were: grip strength (.766) and finger
tapping (.689).
Table 7. Correlations, Components and Loadings for the MAND for All Participants.
Component 1 2 3
Eigenvalue 1.96 1.33 1.10
Motor Skill 1 2 3 4 5 6 7 8 9 10
1. Heel-toe - .23 .21 .22 .07 .02 .01 .12 .06 .14 .705
2. Balance MAND - .17 .18 .14 .09 .05 .05 .00 .08 .639 -.132
3. Finger-nose-finger - .14 .15 -.02 .06 .14 .13 -.01 .587
4. Jumping - .12 .16 .07 .09 .21 .02 .518 .199 .157
5. Beads on rod - .31 .22 .17 .06 .11 .141 .723 .107
6. Beads in box - .16 .12 -.07 -.04 .708 -.276
7. Nut and bolt - .06 .04 .02 .596 .104
8. Rod slide - .03 .08 .229 .340 .115
9. Grip strength - .17 .104 .766
10. Finger tapping - .689
Note. Loadings greater than .40 are in bold.
4.1.2. The AIS Instrument
The Australian Sports Commission (1998) stated that the AIS test instrument assesses four
different motor abilities - speed, ability to spring in a vertical direction, upper body
strengthand aerobic fitness. Therefore, it was expected that this 4-factor solution would be
revealed in the PCA. However, this was not the case. The exploratory factor analysis
extracted one component that was comprised of all four motor skills, with a modest 42.9%
of total variance being explained. The eigenvalue, loadings greater than .10 and the
intercorrelation matrix are presented in Table 8.
55
Table 8. Correlations, Component and Loadings for the AIS for All Participants.
Component 1
Eigenvalue 1.72
Motor Skill 1 2 3 4
1. 40m sprint - .40 .27 .18 .754
2. Multistage fitness test - .22 .12 .701
3. Vertical jump - .21 .642
4. Basketball - .495
Note. Loadings greater than .40 are in bold.
Component One: The loadings for the four motor skills are: 40m sprint (.754), multistage
fitness test (.701), vertical jump (.642) and basketball throw (.495).
4.1.3. The BMC
In the current research, an additional motor skill instrument was developed to assess
movement skills not covered by the AIS. Specifically, nine motor skills were selected to
assess two aspects of Burton and Miller’s (1998) movement skills foundation checklist.
They are movement coordination (via the shuttle-run-without-object, shuttle-run-with-
object, hopping-in-square, hopping speed, zigzag run and quadrant jump) and balance (via
the one-foot-balance-with-eyes-open, one-foot-balance-with-eyes-closed and dynamic
balance). It was expected that factor analysis would support the existence of these two motor
abilities. However, the exploratory PCA revealed three rotated components from the nine
motor skills of the BMC, with a modest 66.3% of total variance being explained. For
eigenvalues, loadings > .10 and the correlation matrix, see Table 9.
Component One: The motor skills that loaded substantially on this component were: shuttle
run (.881), shuttle-run-with-object (.875), hopping speed (.781) and zigzag run (.713).
Component Two: High loadings in this component were: the dynamic balance (.808),
hopping-in-square (.763) and quadrant jump (.592).
Component Three: This component had loadings on: one-foot-balance-with-eyes-open
(.853) and one-foot-balance-with-eyes-closed (.728).
56
Table 9. Correlations, Components and Loadings for the BMC for All Participants.
Components 1 2 3
Eigenvalue 3.75 1.17 1.04
Motor Skill 1 2 3 4 5 6 7 8 9
1. Shuttle run - .81 .65 .57 .32 .26 .34 .18 .33 .881 .176 .125
2. Shuttle run with object - .59 .55 .27 .23 .31 .15 .27 .875 .128
3. Hopping speed - .58 .31 .24 .29 .22 .27 .781 .182 .164
4. Zigzag run - .35 .31 .29 .17 .30 .713 .290 .131
5. Dynamic balance - .43 .40 .18 .22 .173 .808 .115
6. Hopping-in-square - .24 .15 .17 .112 .763
7. Quadrant jump - .15 .21 .259 .592 .113
8. One-foot balance with eyes open - .33 .105 .853
9. One-foot balance with eyes closed - .255 .117 .728
Note. Loadings greater than .40 are in bold.
4.2. DISCUSSION: FACTORS UNDERLYING THE MAND, AIS AND BMC
4.2.1. The MAND
The exploratory factor analysis on the MAND data identified three motor abilities
explaining 43.9% of the total variance. Using a loading cut-off level of .40, nine of the
MAND motor skill tests loaded positively on to one of the three identified motor abilities.
This finding is at odds with the 2-factor and 4-factor models reported by McCarron (1982).
The first motor ability consisted of 4 gross motor skills - heel-toe-tandem-walking, standing-
on-one-foot, finger-nose-finger movements, and jumping. Three of these tasks (i.e., heel-toe-
tandem-walking, standing-on-one-foot, and jumping) encompass ‘the ability to maintain or
control the centre of mass in relation to the base of support to prevent falls and complete
desired movements’ (Westcott et al., 1997, p. 630). The jumping task purportedly measures
power, but to perform this task well, individuals must also retain equilibrium throughout the
jump (McCarron, 1982). While the strength component of the jump directly influences
individual power (Fleishman, 1964), to perform this motor skill well requires optimal
balance control in an upright posture (Kollmitzer et al., 2000). Therefore, the postural
57
control aspect of the jumping task may have influenced the strength of its relationship with
the other motor skills in this factor. The motor skill of finger-nose-finger movements also
loaded onto this factor. This is a measure of eye-hand coordination that requires an ability to
focus attention while inhibiting extraneous movements. An ability to inhibit extraneous
movements is also necessary for successful performance in the motor skills of heel-toe-
tandem-walking and standing-on-one-foot. Being able to control extraneous movements in
these three motor skills might explain why the measure of eye-hand coordination loaded
onto this factor. Thus, given the importance of being able to control extraneous motor
movements and being able to control one’s centre of mass, it was decided to label this motor
ability ‘postural control’.
The three motor skills making up component two consisted of the fine motor skills of beads-
on-rod, beads-in-box, and nut-and-bolt. These three motor skills require skill in well directed
arm-hand movements to manipulate objects at speed (Fleishman, 1964; Hempel &
Fleishman, 1955). The motor skills of beads-on-rod, and nut-and-bolt, require two-hand
coordination to perform successfully. It should be noted that McCarron (1982) found these
two fine motor skills loaded together to form a bi-manual dexterity factor in a factor analysis
of the MAND in normal children. However, this research found the beads-in-box motor skill
also loaded highly with beads-on-rod, and nut-and-bolt. This motor skill was like the other
two in that it requires consistent movements of hand and forearm to move the beads into the
box, and a degree of coordination to perform the task successfully. Finally, although not
reaching the cut-off point for inclusion in a factor; the rod slide task, a slow activity, also
loaded positively at .34 on component two. As a package then, the motor skills making up
this factor all require well directed and controlled arm-hand movements (Kent, 1994). Thus,
although slightly different from McCarron’s findings, this motor ability could also be called
‘bi-manual dexterity’.
Component three consists of two motor skills; namely, grip strength and the fine motor skill
of finger tapping. McCarron (1982) also found these two motor skills both loaded onto a
factor (i.e., muscle power) in a factor analysis of the MAND in normal children. However,
unlike McCarron’s findings (the motor skill- jumping- also loaded), only these two motor
skills loaded onto component three in the current research. Both motor skills require rapid
coordination of muscular movements, either via fast and continuous finger tapping, or
through one explosive act of grip strength. Both motor skills also require control of muscle
movements in sequence and displacement at speed. Attainment of both tasks indicates an
individual’s ability to act in response to a rapid and precise movement (Kent, 1994). Given
58
that component three is similar to the muscle power factor reported by McCarron (1982) this
component was similarly named, ‘muscle power’.
The PCA analysis revealed three underlying motor abilities for the MAND - postural
control, bi-manual dexterity and muscle power. Others also have found underlying
constructs for the MAND. For instance, 8-12 year old Australian children exhibited three
factors for the MAND that were similar to those reported in this study (Larkin and
Rose,1998). They labelled their factors as kinaesthetic integration, manual dexterity and
muscle strength. However, as noted before, McCarron (1982) identified a 2-factor model
among working, mentally disabled adults (i.e., fine motor skill and gross motor skill), and a
4-factor model among normal children aged 7 years old (i.e., persistent control, muscle
power, kinaesthetic integration and bi-manual dexterity). Thus, the MAND is a competent
instrument when assessing motor skills at a basic level. But, when it comes to understanding
what it is that the MAND is measuring from a more general perspective, this appears to
change and depend upon the population under investigation. Earlier research acknowledged
that different factor constructs will emerge from different age groups and different levels of
motor difficulty (e.g., McCarron, 1982; Rarick, Dobbins & Broadhead, 1976). So, to
understand what that instrument is assessing, relative to the population under investigation,
Burton and Miller’s (1998) recommendation to examine the psychometric properties of an
instrument appears warranted.
Finally, the MANDs solution had a percentage of total variance explained below 50%.
Tinsley and Tinsley (1987) have cautioned researchers when interpreting identified factor
solutions having < 50% of the total variance explained, because such solutions are marginal.
Thus, the 3 component solution found here for the MAND needs to be cross validated before
one can be confident in its veracity. Therefore, caution is required when interpreting the
present findings of the MAND.
4.2.2. The AIS Instrument
The AIS instrument records information regarding basic anthropometric measures of height
and weight; and motor skill performance. Specifically, the Australian Sports Commission
(1998) identified four motor skills that assessed speed, ability to spring in a vertical
direction, upper body strength and aerobic fitness. However, this structure was not found for
the Malaysian adolescent sample. In the current research, the factor analysis demonstrated
that one component explained approximately 43% of the total variance in the solution in
contrast to that purported by the Australian Sports Commission (1998). An examination of
the nature of this component was then undertaken. Anaerobic power is demonstrated in both
59
the 40m sprint and the multistage fitness tests because both require maximal rates of energy
production with high intensity (Anshel, 1991; Bencke et al., 2002; Manning, Dooly-
Manning & Perrin, 1988). Additionally, explosive power also is indicated via the basketball
throw and the vertical jump because each activity requires one explosive act (Bencke et al.,
2002; Kent, 1994; Manning et al., 1988). Considering the contribution of the elements of
anaerobic power and explosive power being fundamental to this construct, and that ‘energy
burst’ or ‘explosive act’ has been referred to as anaerobic power (Manning et al., 1988), this
component was described as ‘explosive power’.
A task analysis of the motor skills making up the AIS instrument indicated that each skill
has similar performance requirements by the upper and lower limb power contributions to
execute the tasks. According to Fleishman (1964), inefficient test batteries are those with too
many tests on one factor, or none from one or more of the other factors identified. Within the
context of the current research, if one just wanted to assess explosive power in Malaysian
adolescents, having them complete four tests that measure explosive power may be
inefficient. However, each motor skill of the AIS instrument assesses different aspects of
explosive power and, consequently, each may be important when identifying athletic talent.
Similarly, just focusing on one or two aspects of explosive power provides only part of the
picture.
Finally, there are two concerns regarding the PCA analysis of the AIS test. Firstly, these
findings seem to suggest that the AIS instrument does not assess four separate motor
abilities - only assessing one motor ability, that of explosive power. Therefore, as with the
MAND, it may be necessary to evaluate the AIS instrument at each new setting. Secondly,
as with the MAND, the AIS solution found here had < 50% of total variance explained.
Thus, despite the intuitive appeal of the four motor skills measuring anaerobic power,
caution is needed when interpreting the findings of the AIS analysis.
4.2.3. The BMC Test
Nine motor skills were chosen to assess two motor ability components from the Burton and
Miller (1998) fundamental movement skill foundations checklist - movement coordination
and balance. The subsequent exploratory factor analysis revealed three motor abilities
underlying the nine BMC motor skills and these explained 66% of the total variance in the
solution.
Four motor skills were extracted for the first component. These were shuttle run, shuttle-run-
with-object, hopping speed and zigzag run. The loadings for the two shuttle run tasks were
60
above .80. These loadings are considered very high and, subsequently, these two motor skills
became important in defining the nature of factor one (Hair et al., 1998). Hilsendager, Strow
and Ackerman (1969) maintained that the shuttle and zigzag runs require agility, as both
tasks involve rapid movement and change in the direction of movement. However,
Hilsendager et al. (1969) also rejected the interaction of speed and strength in the agility
component. Hopping at speed requires both strength and speed (Chelly & Denis, 2001) and
an ability to maintain balance in a small base of support (Haywood, 1993a). A task analysis
of the shuttle-run-with-object and the zigzag run showed that these tasks required additional
movement through manipulating an object (i.e., shuttle-run-with-object) or completing a
zigzag run from two different directions (i.e., the zigzag run). In other words, the
participants were required to coordinate and integrate different movements into specific
patterns peculiar to that motor skill. Barrow (1977) indicated that speed, balance, agility and
kinaesthetic sense are related to coordination, while strength only will influence
coordination at the onset of fatigue. Thus, since the tasks loaded on this component require
well timed and well balanced functioning of several muscles during a single movement
(Broer, 1973), this factor was named ‘movement coordination’.
Component two consisted of the dynamic balance, hopping-in-square and quadrant jump
motor skills. The loading for dynamic balance was high and, along with the quadrant jump,
demonstrated components of agility. Both tasks also involve the capacity to change body
position quickly and accurately (Brown, 2001). It is true that the strength component of the
jump directly influences individual power (Fleishman, 1964). To perform this motor skill
well, requires optimal balance control in an upright posture (Kollmitzer et al., 2000).
However, the hopping-in-square task categorises dynamic balance because it requires
posture control to perform the task (Liemohn & Knapczyk, 1984). If one rejects any
interaction of speed and strength in the agility component (Hilsendager et al., 1969), and
strength contributed in all extracted items, the naming of this factor as something other than
agility is more appropriate. As these tasks require control and adaptable force to regulate the
posture, this component was labelled ‘postural control’ (Burton & Davis, 1992; Kent, 1994).
Component three consisted of the two motor skills assessing static balance - one-foot-
balance-with-eyes-open and one-foot-balance-with-eyes-closed. Both motor skills require
one to maintain or control the centre of mass in relation to the base of support to prevent
falling and complete the desired movements (Westcott et al., 1997). Also, being able to
balance on one leg requires an ability to focus attention while inhibiting extraneous motor
movements. Success in these two motor skills requires control of extraneous motor
movements and centre of mass, especially with the eyes closed. Fleishman (1964) suggested
61
that skills measured with eyes open or closed, assesses gross body equilibrium. Given the
static nature of these two skills, the factor was named ‘static balance’.
The 3 component solution reported for the BMC instrument (i.e., movement coordination,
postural control and static balance) is at odds with the intended 2-factor model (i.e., balance
and movement coordination). At a more general level, the 2-factor model appears to
adequately represent what the BMC motor skills measure. However, empirically this was not
the case. It appears that more specific aspects of these motor skills, or perhaps important
basic qualities of these skills, determined a 3 component solution. Six of the motor skills did
load correctly on to their respective components (movement coordination ability - shuttle
run, shuttle-run-with-object, hopping speed, zigzag run; balance ability - one-foot-balance-
with-eyes-open and one-foot-balance-with-eyes-closed). The other three motor skills formed
a separate component named postural control (i.e., dynamic balance, hopping-in-square and
quadrant jump). Examining the 3 component solution suggests that the motor skills making
up movement coordination, as a group, reflect a basic coordination of upper and lower body
limbs while moving in a forward direction. However, the other two component s appear to
assess different aspects of balancing ability – being able to stand perfectly still and being
able to control one’s balance while moving in a confined area. This breakdown of balance
into two related, but separate, aspects is important to help clarify what is being assessed by
these motor skills. The two motor skills making up the static balance factor have been found
to load together on a single factor in previous research (Bass, 1939; Burton & Davis, 1992).
However, in this setting, the BMC assesses more than one aspect of balance. Despite the
BMC being designed to assess two of Burton’s (1993) movement skill foundations,
empirically it was found that the BMC not only does that, but also examines two aspects of
balance rather than one. However, it is possible that the solution found for the BMC is only
pertinent to the current sample. Therefore, it is recommended that these findings be validated
on another sample.
Summary: A series of PCA factor analyses were undertaken to explore the nature of the
underlying constructs of the motor skill instruments of the MAND, AIS and BMC. It was
found that the MAND assessed three motor ability component s (postural control, bimanual
dexterity and muscle power); the AIS assessed one motor ability component (anaerobic
power); and the BMC assessed three motor ability components (movement coordination,
postural control and static balance). The motor skills and identified motor abilities from the
three motor skill instruments are illustrated in Figure 3.
62
Figure 3. The motor skills, loadings & motor abilities for the MAND, AIS & BMC.
Heel Toe
Balance MAND
MA
ND
Te s t A
IS Te s t
Finger Nose Finger
Jumping
Beads Rod
Beads Box
Nut Bolt
Rod Slide
Grip Strength
Finger Tapping
40m Sprint
MSFT
Vertical Jump
Basketball Throw
Shuttle Run
Shuttle Run Object
Hopping Speed
Zigzag Run
Dynamic Balance
Hopping-in-Square
Quadrant Jump
Balance Eyes Open
Balance Eyes Closed
BM
C Te s t
Postural Control
Bi-manual Dexterity
Muscle Power
Explosive Power
Movement Coordination
Postural Control
Static Balance .73
66.3%
.85
.59
.76
.81
.71
.78
.88
.88
.49
.64
.70
.75
.69
.77
.59
.71
.72
.52
.59
.64
.71
42.9%
43.91%
*MSFT – Multistage Fitness Test
63
SECTION B – AN EXAMINATION OF GENERAL MOTOR ABILITY
Section B examines the concept of GMA. Burton and Miller (1998) noted that, in order to
assess motor skills adequately, at least two instruments, which assess unique motor skills,
are needed. Thus, the AIS and BMC motor skills were examined together to constitute a
combined AIS+BMC instrument. This combination provided a larger set of motor skills with
which to test for GMA than using the AIS or BMC alone. Several approaches can be used to
test for the existence of GMA (Burton & Richardson, 2001; Jensen & Weng, 1994; Johnson
et al., 2004, 2008). The approach used here was higher order factor analysis (Rummel,
1970). A ‘g’ in motor ability was examined separately for boys and girls, as past research
reported differences between boys and girls in their performances of fundamental movement
skills (Seefeldt & Haubenstricker, 1982; Thomas & French, 1985; Thomas, Michael &
Gallagher, 1994). Despite this, Burton and Miller (1982) noted that some research in motor
ability typically ignores the possibility of gender differences at a higher level. Thus,
recognising the possibility of gender differences, this study tested for the existence of a ‘g’
separately in boys and girls. Given the exploratory nature of these analyses, no formal
hypotheses were made on the motor abilities arising from such an examination. However,
from the first-order factor analysis of the AIS+BMC, the underlying factors, or specific
motor abilities, can be subjected to a second order factor analysis to test for a motoric ‘g’.
Statistical Analysis Procedure. Firstly, raw scores from the motor skill tests were
transformed into z scores for each gender-by-age-group classification, then into T-scores to
eliminate the effects of gender and age. The first-order factor analysis on the combined
AIS+BMC motor skills used PCA with oblique PROMAX rotation. The oblique rotation is
essential for further higher-order analysis as this rotation offers a continuous range of
correlations between the factors (Tabachnick & Fidell, 1989). A higher-order factor analysis
was then conducted to test for a ‘g’ in motor ability. For both the first-order and higher-order
factor analyses, the interpretation of the first-order components and higher-order factor was
based on the motor skills and components, respectively, loading ≥ .50 (Rummel, 1970). For
the results testing for a motoric ‘g’ in all participants, see APPENDIX I on the CD.
4.3. RESULTS – BOYS’ SUB-SAMPLE
The exploratory PCA factor analysis revealed four rotated components that accounted for
60.56% of variance. The eigenvalues, loadings and the intercorrelation matrix for the boys’
sub-sample are presented in Table 10.
64
4.3.1.
First-Order Factor Analysis
Component One: The variables that loaded on this component were shuttle run (.911),
shuttle-run-with-object (.903), hopping speed (.817) and zigzag run (.673).
Component Two: High loadings on this component were one-foot-balance-with-eyes-open
(.709) and multistage fitness test (.536).
Component Three: This component had loadings on dynamic balance (.770), quadrant jump
(.756) and hopping-in-square (.709).
Component Four
: This component had high loadings on vertical jump (.841) and basketball
throw (.641).
4.3.2. Higher-Order Factor Analysis
The higher-order factor analysis extracted one factor accounting for 45.5% of variance. For
eigenvalues, loadings and intercorrelation matrix for the higher-order analysis see Table 11.
Table 10. Correlations, Components and Loadings of the AIS+BMC for the Boys.
Component 1 2 3 4
Eigenvalue 4.30 1.44 1.11 1.03
Motor Skill 1 2 3 4 5 6 7 8 9 10 11 12 13
1. Shuttle run - .92 .65 .59 .19 .11 .30 .29 .30 .30 .29 .22 .37 .911
2. Shuttle run/object - .63 .58 .20 .27 .30 .30 .29 .29 .22 .16 .30 .903
3. Hopping speed - .47 .16 .17 .22 .44 .22 .14 .19 .08 .28 .817 .117 -.119
4. Zigzag run - .18 .26 .33 .32 .30 .20 .22 .20 .30 .673 .175
5. One-foot balance with eyes open - .11 .29 .29 .25 .07 .18 .22 -.01 .709 .222
6. Multistage fitness test - .17 .32 .18 .10 .15 .13 -.01 .263 .536 -.180
7. One-foot balance with eyes closed - .21 .25 .18 .17 .13 .06 .234 .470 .102
8. 40m sprint - .37 .21 .21 .23 .20 .199 .456 .239
9. Dynamic balance - .33 .47 .21 .11 .191 .770
10. Quadrant jump - .18 .08 .19 .114 -.273 .756
11. Hopping-in-square - .18 .08 -.100 .123 .709
12. Vertical jump - .26 -.159 .244 .841
13. Basketball throw - .361 -.393 .641
65
Note. Loadings ≥ .50 are in bold.
Table 11. Higher-order Factor Analysis of the AIS+BMC for the Boys.
Higher-Order Factor 1
Eigenvalue 1.82
% of variance 45.51
Component 3 1 4 2
Component 3 - .39 .24 .28 .771
Component 1 - .35 .34 .737
Component 4 - .14 .601
Component 2 - .566
4.3.3. Discussion
The PCA analysis on the boys identified four components that explained 60.56% of the
variance in the solution. Two motor skills failed to reach the .50 cut-off level for inclusion;
namely, the one-foot-balance-with-eyes-closed and the 40m sprint.
The four motor skills that loaded onto the first component were shuttle run, shuttle-run-with-
object, hopping speed and zigzag run. The task analysis on the shuttle run, shuttle-run-with-
object and zigzag run; indicated that strength, speed, balance, agility and endurance
components are necessary when performing these motor skills. Given the nature of these
skills, it was reasoned that this component could be labelled ‘movement coordination’.
Component two was made up of one-foot-balance-with-eyes-open and the multistage fitness
test. Maintaining body balance (static and dynamic) and achieving equilibrium with the
integration of sensorimotor input from large muscle groups are important components in
these motor skills. It was noted that the motor skills of one-foot-balance-with-eyes-closed
and the 40m sprint also loaded substantially, but were not included as they did not reach the
cut-off level of .50. Given that successful performances of these skills require control of
balance and orientation of the body in space (McCarron, 1982), this component could be
labelled ‘kinaesthetic integration’.
The three motor skills that loaded onto component three were dynamic balance, quadrant
jump and hopping-in-square. All of these tasks are performed in an upright position and,
subsequently, require strength and balance to maintain posture during the dynamic
66
movements needed for these tasks (Burton & Davis, 1992; Westcott et al., 1997).
Accordingly, Kollmitzer et al. (2000) wrote that strength and balance were important
components in postural control. Posture serves two main functions; ‘to remain inside the
supporting surface’ and ‘as a reference frame for perception and action with respect to the
external world’ (Massion, 1994 pg. 877). Therefore, since controlling body posture is an
important aspect of this component it was named ‘postural control’.
Finally, the motor skills of vertical jump and basketball throw loaded onto the final
component. The vertical jump task was established as a test of explosive strength (Australian
Sports Commission, 1998; Eisenmann & Malina, 2003; Fleishman, 1964; Larson, 1941;
McCloy, 1968; Sargent, 1968). On the other hand, the basketball throw task is claimed to
measure upper body strength (Australian Sports Commission, 1998), strength and/or
coordination (Barrow, 1954; Barrow & McGee, 1964), and muscular strength and speed of
movement (Arnheim & Sinclair, 1979). Hempel and Fleishman (1955) have proposed a
‘limb strength’ factor for tasks involving both arm and leg strength movements. Considering
that these tasks demonstrated an ‘ability to mobilise quickly and effectively maximum
energy or force’ (Fleishman, 1964, pg. 96), this component was labelled ‘explosive power’.
Higher-Order Factor Analysis. The higher-order factor analysis conducted on the four
components which emerged from the combined AIS+BMC (i.e., movement coordination,
kinaesthetic integration, postural control and anaerobic power), revealed one higher-order
factor. This higher-order factor accounted for 45.51% of the variance, and the loadings for
the first-order factors ranged from .57 to .77. Given that one higher-order factor
encompassed all of the first-order components it is suggested that this demonstrates the
presence of a ‘g’ in motor skill ability for the boys. The motor skill tests, first-order
components and higher-order factor for the boys’ sub-sample on the combined AIS+BMC is
illustrated in Figure 4.
67
First-Order FA Higher-Order FA
Figure 4. The AIS+BMC motor skills, first-order components and higher-order factor
for the boys.
4.4. RESULTS – GIRLS’ SUB-SAMPLE
Three rotated factors were extracted in the exploratory PCA factor analysis and they
accounted for 57.7% of variance. The eigenvalues, rotated factor loadings and the
intercorrelation matrix are presented in Table 12.
4.4.1. First-Order Factor Analysis
Component One: The variables that loaded on this component were the shuttle run with
object (.851), shuttle run (.845), 40m sprint (.780), hopping speed (.722), zigzag run (.698),
multistage fitness test (.675) and vertical jump (.633).
Component Two: This component had loadings on hopping-in-square (.793), dynamic
balance (.762) and basketball throw (.676).
Component Three: High loadings on this component were one-foot-balance-with-eyes-open
(.886) and one-foot-balance-with-eyes-closed (.699).
AIS+B
MC
Te s t
Shuttle Run
Shuttle Run Object
Hopping Speed
Zigzag Run
40m Sprint
MSFT
Dynamic Balance
Hopping-in-Square
Quadrant Jump
Basketball Throw
Vertical Jump
Balance Eyes Open
Balance Eyes Closed
Movement Coordination
Postural Control ‘g’
Kinaesthetic Integration
.71
.54
.76
.71
.77
.67
.90
.91
.74
.77
.57
Explosive Power .84
.64
.82
.60
68
Table 12. Correlations, Components and Loadings of the AIS+BMC for the Girls.
Component 1 2 3
Eigenvalue 5.07 1.26 1.17
Motor Skill 1 2 3 4 5 6 7 8 9 10 11 12 13
1. Shut. Run/obj - .73 .47 .52 .51 .41 .26 .17 .25 .20 .32 .12 .25 .851 -.189
2. Shuttle run - .57 .68 .57 .50 .36 .30 .34 .31 .39 .17 .35 .845
3. 40m sprint - .55 .56 .48 .30 .27 .27 .16 .32 .14 .28 .780
4. Hopping speed - .70 .57 .35 .30 .40 .28 .44 .28 .32 .722 .106 .135
5. Zigzag run - .52 .32 .39 .40 .24 .39 .16 .27 .698 .179
6. Multistage fitness test - .33 .26 .27 .26 .35 .15 .25 .675
7. Vertical jump - .15 .23 .15 .17 -.03 .13 .633 -.361
8. Hopping-in-square - .39 .32 .29 .12 .17 .793
9. Dynamic balance - .28 .46 .12 .19 .762
10. Basketball throw - .23 .15 .14 .676
11. Quadrant jump - .23 .24 .148 .499 .198
12. One-foot balance with eyes open - .36 -.159 .886
13. One-foot balance with eyes closed - .184 .699
Note. Loadings ≥ .50 are in bold.
4.4.2. Higher-Order Factor Analysis
A higher-order factor analysis extracted one factor accounting for 59.5% of the variance.
The eigenvalues, percentage of variance, component loadings and intercorrelation matrix are
presented in Table 13.
Table 13. Higher-order Factor Analysis of the AIS+BMC for the Girls.
Higher-Order Factor 1
Eigenvalue 1.78
% of variance 59.47
Components 1 2 3
Component 1 - .54 .33 .833
Component 2 - .29 .803
Component 3 - .668
69
4.4.3. Discussion
The exploratory Principal Component Analysis reduced the 13 motor skills of the combined
AIS+BMC to 3 components that explained 57.67% of total variance. The tests loading on
component one were shuttle-run-with-object, shuttle run, 40m sprint, hopping speed, zigzag
run, multistage fitness test and vertical jump. A task analysis on the skills showed an agility
component (for the shuttle run, the shuttle-run-with-object & the zigzag run), speed, strength
and balance maintenance components (for the 40m sprint & hopping speed tests), another
strength component (for the vertical jump) and endurance component (for the multistage
fitness test). As these skills combine the interrelationships between strength, speed, balance,
agility and endurance components, the component was labelled ‘movement coordination’.
The motor skills of hopping-in-square, dynamic balance, basketball throw and quadrant
jump loaded onto component two. A task analysis of these skills highlighted the
requirements of coordination and balance while maintaining a vertical position. The
basketball throw purportedly assesses upper body strength (Australian Sports Commission,
1998) but one needs to maintain balance and good posture throughout the throw in order to
perform the skill well. Balance and strength appear to be major characteristics of this factor
and these are important aspects of postural control (Kollmitzer et al., 2000). As the motor
skills loading on this component are performed in an upright position, strength and balance
are required to maintain posture during the dynamic movements needed to accomplish these
tasks (Burton & Davis, 1992; Westcott et al., 1997). Therefore, controlling body posture is
important for these motor skills and this component was named ‘postural control’.
Component three included one-foot-balance-with-eyes-closed and one-foot-balance-with-
eyes-open. Fleishman (1964) suggested that skills measured with eyes open or closed assess
gross body equilibrium. Tests loading here indicated an ability to maintain vertical balance
while static and was labelled ‘static balance’ (Bass, 1939; Burton & Davis, 1992)
Higher-Order Factor Analysis. The higher-order factor analysis conducted on the three
identified components from the combined AIS+BMC (i.e., movement coordination, postural
control and static balance) extracted one factor. This higher-order factor accounted for
59.47% of the variance, and the loadings for the first-order components ranged from .67 to
.83. Given that one higher-order factor encompassed all of the first-order factors, it is
suggested that this demonstrates the presence of a ‘g’ in motor skill ability for the girls. The
AIS+BMC motor skills, first-order components and higher-order factor for the girls are
illustrated in Figure 5.
70
First-Order FA Higher-Order FA
Figure 5. The AIS+BMC motor skills, first-order components and higher-order factor
for the girls.
4.5. DISCUSSION – GMA ANALYSES
The first-order factor analyses conducted on the combined AIS+BMC revealed three
components for the adolescent girls. Given the nature of the motor skills reported and that
found for the exploratory factor analysis of the BMC, they were labelled similarly -
movement coordination, postural control and static balance. Although unanticipated, it is not
surprising that the three components emerging from the exploratory factor analysis of the
BMC are also present in the combined AIS+BMC analyses given that the BMC has twice as
many motor skill tests as the AIS. Despite this imbalance, four first-order components were
found for the adolescent boys. However, two of these components were similar to those
found for the girls - movement coordination and postural control. The other two factors were
different, kinaesthetic integration and explosive power.
AIS+B
MC
Te s t
Shuttle Run
Shuttle Run Object
Hopping Speed
Zigzag Run
40m Sprint
MSFT
Dynamic Balance
Hopping-in-Square
Quadrant Jump
Basketball Throw
Vertical Jump
Balance Eyes Open
Balance Eyes Closed
Movement Coordination
Postural Control ‘g’
Static Balance
.63
.89
.68
.79
.76
.69
.85 .85
.83
.81
.67
.69
.72
.78
.68
71
The AIS+BMC analyses extracted quite similar motor abilities. In particular, movement
coordination and postural control were found in each analysis and some balancing ability
also emerged. The adolescent girls’ sub-sample assessed static balance, whereas a more
general balance factor emerged for the adolescent boys, that of kinaesthetic integration. This
balance ability for the boys is quite general in nature as it requires one to maintain balance
both while standing still and when changing direction at speed. The final motor ability found
only for the adolescent boys was explosive power. This suggested that, when assessing some
forms of motor skill, power appears to be more relevant for adolescent boys. Past research
has noted gender differences in motor skills and gender should be considered. These
findings support that notion and suggest that gender differences could also occur at the
motor ability level. Therefore, researchers need to be aware that a motor skill instrument
may assess different things according to the gender being examined.
Moderate to high loadings were demonstrated (ranging from .56 to .87) on each higher-order
factor extracted from the second-order factor analyses. These factors suggested the existence
of a ‘g’ in motor ability and, in this case, a ‘g’ associated with the AIS+BMC instrument.
Such higher order analyses are rare and previous studies have not usually taken the next step
to examine for the existence of ‘g’ in motor ability. With more sophisticated analysis tools
available, some researchers have begun to examine for the existence of ‘g’. For instance,
Chaiken, Kyllonen and Tirre (2000) re-analysed Fleishman’s 1954 psychomotor data to
investigate a ‘g’ in psychomotor ability. Fleishman (1954) found four psychomotor abilities
in his exploratory factor analytic approach in which the unrotated solution factor loadings
were then rotated to reveal a simple structure. Such an approach was typical of the time and
didn’t allow the examination of the data beyond this first-order level. Chaiken et al. (2000)
tested whether these four factors could be nested within a psychomotor ‘g’. Using
confirmatory factor analysis, they found evidence for a ‘g’ in psychomotor ability. A
strength of the Chaiken et al. (2000) study was the examination of a large set of
psychomotor skills that were associated with four psychomotor abilities. This allowed for a
greater opportunity to test for the existence of ‘g’ in psychomotor ability. The current study
only examined two motor skill instruments encompassing thirteen motor skills. Even so,
from this small pool of motor skills, specific abilities were extracted which enabled an
examination of ‘g’ and concluded that a ‘g’ in motor ability existed. However, if the AIS
instrument alone was studied, can it be said that the single first-order component extracted is
a ‘g’ element, or is it an aspect of power ability? Thus, future research needs to include a
greater number of motor skill instruments in order to study ‘g’ in motor ability in greater
detail.
72
Some studies into ‘g’ in general intelligence have reported a null sex difference in the types
of ability found ( Aluja-Fabregat, Colom, Abad & Juan-Espinosa, 2000; Colom et al., 2000)
and others have found gender differences in the abilities revealed (Jorm, Anstey, Christensen
& Rodgers, 2004). Although contradictory findings also have been shown when examining
mental ability, scholars have suggested that the biological and socio-cultural issues could
influence ‘g’ (Jorm et al., 2004; Richardson, 1997). Given that view concerning general
intelligence, it could well be that biological, socio-cultural and environmental issues will
also influence motor ability. In the current research, gender was examined as a potential
influence. The findings suggested that the AIS+BMC motor skill instrument assesses similar
motor abilities in boys and girls – movement coordination, postural control and balance, on
the whole. The AIS+BMC also assessed power ability for the boys thereby demonstrating
gender differences in the motor abilities assessed. However, when these motor abilities
underwent further factor analysis, only one ‘g’ element was found for each gender. The
variance differences found for the boys’ (45.51) and the girls’ (59.47) is unclear, but
possibly can be explained by different sample sizes. One also needs to examine the
relatedness of the ‘g’s found for the girls and boys, given that the AIS+BMC was a newly
constructed instrument. Finally, it should be noted that the percentage of variance accounted
for is tolerable but not strong, and 40% for the girls and 55% for the boys remains
unexplained. Some of the unexplained variance could be due to motivation, practice and
opportunity. Future investigations could consider also the influence of genetic variables such
as speed in conjunction with variables not necessarily impacting on sport performance (e.g.,
static balance). Therefore, whether the ‘g’ found for the girls and boys represents a more
general ‘g’, awaits further examination.
In summary, whilst the analyses did not provide definitive evidence for the existence of
GMA, neither did it deny the existence of a motoric ‘g’. Further research is needed to clarify
this issue. There were gender differences in the motor abilities assessed by the AIS+BMC.
Therefore, researchers need to be aware that a motor skill instrument may assess different
things according to the sample under investigation. Additionally, the gender differences
found in the motor abilities assessed by the AIS+BMC need further investigation before one
can be confident in the veracity of this finding. Finally, despite gender differences reported
in the motor abilities assessed by the AIS+BMC, these differences did not impact on finding
a single higher-order factor, and subsequent existence of a ‘g’ in motor ability as assessed by
the AIS+BMC. The hierarchical models (Jensen & Weng, 1994; Rummel, 1970) found in
this study are described in Table 14.
73
Table 14. The First-order Components and Higher-order Factor of the AIS+BMC for
the Boys’ and Girls’ Sub-samples.
Boys Girls
First-order Components
Movement coordination Movement coordination
Kinaesthetic Integration Postural control
Postural control Static balance
Explosive Power
Higher-order Factor
‘g’ ‘g’
4.6. GENERAL DISCUSSION
Burton and Miller (1998) have recommended examining the factorial validity of motor skill
instruments and this is especially relevant when the sample under investigation is different in
nature from that used to establish the instrument. Because this was the case in the current
study, the factorial validity of the MAND, AIS and BMC motor skill instruments was
examined in the first section of this chapter. The factor analyses of the MAND, AIS and
BMC produced factor structures that were different from those purported by their authors.
Factor analysis allows the examination of the factorial validity of the motor skill instruments
and can provide an empirical solution for each unique situation. If the factor structure is the
same, but a reduced set of motor skills are relevant, then one can be confident in the author’s
description of the instrument. However, if the factor structure changes from sample to
sample, care is needed to present the findings in light of these changes and recognise that the
findings may be sample specific. It was noted that the three factors found for the BMC were
also found in the combined AIS+BMC. This suggested that the BMC factors appeared to be
relatively stable in this sample. Indeed, the motor skills making up the factors found for the
BMC also made up the similarly named factors in the AIS+BMC analyses. However, the
AIS test appears not to be as stable. Of the four motor skills assessing explosive power, only
the multistage fitness test was found to load onto a component (i.e., movement coordination
for the adolescent girls and general balance for the boys). The other three motor skills were
inconsistent in their loading pattern, although when they did load onto a component, their
inclusion was not inconsistent with the overall nature of that factor. However, an explosive
power ability type, which is what the AIS seems to assess in this sample of Malaysian
74
adolescents, was only found in the boys. In spite of this, it is quite satisfying that the
AIS+BMC analyses extracted the motor abilities of movement coordination and postural
control for the girls and the boys. However, two other abilities were also reported for the
boys that were quite different from those reported for the adolescent girls, namely,
kinaesthetic integration and explosive power. Whether, these three and four component
solutions can be verified with other samples awaits further investigation. However, the
results of the current research seem to suggest that Burton and Miller’s (1998)
recommendation for examining the psychometric properties of an instrument appears
warranted and that recognising gender differences is also warranted. Finally, despite finding
specific sets of motor abilities for the motor skill instruments it should be noted that the
percent of variances in each case are reasonable but not outstanding. There is still a sizeable
chunk of variance unaccounted for in each case, which may be just as important (e.g.,
practice, motivation & genetics). This cautionary note is also relevant for the ‘g’ analyses in
that up to 50% of the variance was unaccounted for in each case. A possible reason for the
factor analyses not producing great results and different to those reported in other research
could be due to the homogeneity of the sample (i.e., small sample size, small age range, no
special needs participants, no special athletic participants). Unfortunately, these limitations
were unable to be avoided and future research needs to take these points into consideration.
Burton and Rodgerson (2001) presented a taxonomy of movement skills and general motor
ability made up of four levels – movement skills, movement skill sets, movement skill
foundations and general motor ability. These four levels are presented in a hierarchical
structure where movement skills at the first level can be grouped into movement skill sets at
the second level. Underlying both movement skills and movement skill sets, sits movement
skill foundations. Basic movement skill foundation areas are thought to influence skilled
performance from the third level. Finally, at the fourth level and underlying all, is GMA.
Burton and Rodgerson (2001) wrote that GMA manifests itself in just about all movement
situations by drawing the movement skill foundations together to perform efficiently in the
face of complexity. Additionally, GMA can be inferred by performance on either motor
skills or movement skill sets but, as with movement skill foundations, is not in itself a
movement skill or combination of skills (Burton & Rodgerson, 2001). Keeping this
taxonomy in mind, Section Two of this chapter set out to find a ‘g’ in motor ability.
For these analyses, the BMC and AIS were analysed together. It was anticipated that by
combining the BMC with the AIS this would provide a more comprehensive examination of
motor ability via a larger pool of motor skills. Additionally, the factor analysis of the BMC
found that this motor skill instrument assessed three motor abilities that differed from the
75
one assessed by the AIS. The 13 movement skills of the AIS+BMC extracted two sets of
movement skill sets – movement coordination, postural control and static balance for the
adolescent girls; movement coordination, postural control, kinaesthetic integration and
explosive power for the adolescent boys. The higher-order factor analyses of these extracted
movement skill sets did reveal a ‘g’ in motor ability. The finding of ‘g’ in this manner
supports the hierarchical nature of Burton and Rodgerson’s (2001) taxonomy – that a ‘g’ in
motor ability can be inferred by performance on either movement skills or movement skill
sets. However, the author in this study considers this ‘g’ to be a ‘g’ that is associated with
the motor skills assessed by the AIS+BMC. It is highly likely that other ‘g’s will be found
from the different motor skill instruments. Subsequent examination of these ‘g’s will
advance research closer to establishing the existence of GMA.
Thus, this chapter set out to examine the factorial validity of motor skill instruments used in
the current research and to find ‘g’ in motor ability. The higher-order factor analyses of the
AIS+BMC motor skill set supported the existence of a ‘g’ in motor ability, thereby adding to
the growing awareness of this ‘g’ in human behaviour. Factorial validation analyses
indicated that such an analysis was warranted given that the instruments assessed something
different from that purported by their authors. Thus, one can talk about what a particular
motor skill instrument assesses at a more general level but that was not the main thrust of the
current research. The overall drive of the current research was to build on previous work
surrounding identification of athletic talent in Malaysia and, primarily, to initiate the
development of a TI instrument relevant to Malaysians. Past work into identifying athletic
talent in Malaysia used the AIS motor skill instrument. Although the authors of the AIS
purport that this instrument measures four components of motor ability, this study found that
it only assesses one type of motor ability – explosive power. Since the aim was to develop a
TI instrument that provides a more rounded assessment, it was decided to refer to a checklist
of movement skill foundations hypothesised to be important in the execution of movement
skills (Burton and Miller, 1998). Subsequently, nine motor skills were selected that assessed
two of these foundations – movement coordination and balance. This new instrument was
called Balance and Movement Coordination, and it was anticipated that the combined BMC
and AIS batteries would create a more comprehensive examination of motor ability for TI in
Malaysia. Whether this combination will be helpful to identifying athletic talent is beyond
the scope of the current research. For the remainder of the thesis the focus turns to the motor
skills themselves, since performance on the motor skills is what counts when identifying
athletic talent. Chapter Five will examine the descriptive data and provide some normative
data for future reference; and an examination of age and gender on motor performance also
follows. Comparisons with what is found in these analyses here will be undertaken for the
76
MAND and the AIS. Then, Chapter Six seeks to establish motor skills that could help in
identifying athletic talent. Specifically, motor skills are sought that can reliably discriminate
the adolescents into a) three motor coordination groups derived from scores on the MAND,
and b) three motor ability groups derived from the scores on ‘g’.
77
CHAPTER 5
RESULTS AND DISCUSSION FOR THE MOTOR SKILL PERFORMANCES
Unless otherwise stated the participant’s raw score for the motor skills making up the
MAND, AIS and BMC were utilised in the following analyses. There were 26 separate
ANOVAs performed on the data and an overall Bonferroni correction was applied to all
effects. Thus, the overall p value used was p < .002. The results are organised into three
sections: i) basic height, weight, and BMI information and performances on four motor skills
assessed via the AIS instrument; ii) fine and gross motor skill performances measured via
the MAND; and iii) balance and movement coordination motor skill performances assessed
via the BMC. Within each section, basic Descriptive statistics are detailed and followed by a
series of ANOVAs. To aid in the presentation of results, the participants were classified
according to gender and age. This resulted in eight sub-groups. The girls were classified
into: girls aged 12 (G12), girls aged 13 (G13), girls aged 14 (G14) and girls aged 15 (G15).
The boys were classified as: boys aged 12 (B12), boys aged 13 (B13), boys aged 14 (B14)
and boys aged 15 (B15). The chapter concludes with a discussion of the findings.
5.1. AUSTRALIAN INSTITUTE OF SPORT (AIS) TALENT IDENTIFICATION
INSTRUMENT
The AIS instrument records information regarding basic anthropometic measures of height
and weight; and motor skill performance for the 40m sprint, vertical jump, basketball throw
and the multistage fitness test. Initially, the results of the reliability analysis of the AIS
motor skills are presented. Then the Descriptive and ANOVA results are presented
separately for the anthropometry and motor skill assessments.
5.1.1. Reliability of the AIS
The results showed that AIS had very good test re-test reliability with all tests exhibiting
coefficients ≥ .92 (see Table 15). In particular, the 40m sprint and t he basketball throw
recorded very high reliability scores. The average r across all the AIS motor skills was .95.
This analysis indicates that reliability of the motor skills on the AIS is acceptable, and can be
used with Malaysian adolescents.
78
Table 15. Means ± SDs and Reliability Coefficients for the AIS Tests.
Testing Session
First Second
Variables Mean SD Mean SD r
Basketball Throw (m) 4.55 1.15 4.60 1.19 .98
Vertical Jump(cm) 24.61 7.42 24.45 7.04 .94
40m Sprint(s) 8.10 1.12 8.09 1.15 .97
Multistage Fitness Test (laps) 12.24 4.12 12.10 4.65 .92
5.1.2. Anthropometry Tests
Descriptives
Table 16 presents the Means ± SDs for the anthropometry measures. The mean heights of
boys increased more rapidly with age than those for girls. The mean heights of girls
increased from 12-14 years (G12-14), but were unchanged at age 15 years (G15). The mean
weights for boys increased in parallel with heights, as did the mean weights for girls,
including the G15s. The BMIs of the G12-14s years were higher than boys of similar ages.
However, at 15 years, the BMI of boys was similar to that of the girls, having increased
rapidly between 14 and 15 years old.
ANOVAs
A series of two (gender) by four (age) way ANOVAs were conducted to determine whether
there were any significant differences on the height, weight and body mass index for these
groups (see Table 17). The results revealed significant gender-by-age interaction for height
(p < .001) and an age main effect for weight (p < .001). The gender by age interaction
explained how an expected increase in height was evident for both boys and girls, but this
was more so with the boys. Specifically, all of the older boy age groups were significantly
taller than their younger counterparts. For the girls the G12s were significantly shorter than
the G14s and G15s. The age main effect for weight indicated that the adolescent’s weight
increased as they got older. Specifically, the post-hoc analysis revealed that the 15-year-olds
were heavier than all age groups and that the 14-year-olds were heavier than the 12-year-
olds (see Table 18).
79
Table 16. Means ± SDs for the AIS Anthropometry Measures.
Age Boys Girls
(years) Mean SD Mean SD
Height (m) 12 1.48 a b c d .08 1.50 a .06
13 1.55 a b c d .09 1.53 .06
14 1.60 a b c d .08 1.56 a .05
15 1.65 a b c d .06 1.56 a .06
Weight (kg) 12 39.96 10.09 45.78 13.70
13 46.60 14.14 46.20 13.99
14 47.17 11.36 48.53 9.95
15 59.06 18.01 51.58 11.70
BMI (kg/m2) 12 18.14 4.03 20.22 5.33
13 19.25 4.43 19.63 5.42
14 18.44 3.84 20.01 3.81
15 21.58 5.89 21.09 4.65
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25. Same superscript letters indicate significant differences within group pairwise
comparisons. Same subscript letters indicate significant differences between group pairwise
comparisons. Bolded letters indicate the focus for the comparisons being made (i.e.,. a = B12
and G12, b = B13, c = B14, d = B15). A Bonferroni correction is used for all post-hoc
comparisons.
80
Table 17. ANOVA Results for the AIS Anthropometry Measures.
Effect df F Sig.
Height Gender 1/322 17.872 .001
Age 3/322 38.511 .001
Gender by Age 3/322 7.511 .001
Weight Gender 1/322 .005 .943
Age 3/322 9.221 .001
Gender by Age 3/322 2.806 .040
Body Mass Index Gender 1/322 3.407 .066
Age 3/322 2.087 .102
Gender by Age 3/322 1.007 .390
Table 18. Descriptives for the Main Effect Age on the AIS Weight Assessment.
Weight n Age Mean SD CI
92 12 41.11*# 12.10 39.60 – 44.62
107 13 46.40# 13.99 43.72 – 49.08
90 14 47.91*# 10.65 45.68 – 50.14
41 15 54.52# 15.02 49.78 – 59.27
Note. * denotes significant difference found between the 12-year-olds and the 14 year olds. #
denotes a significant difference found between the 15-year-olds and all other age groups. CI
= 95% Confidence Interval.
81
5.1.3. Motor Skill Performances
Descriptives
The older boys and girls generally threw the basketball further than the younger groups (see
Table 19). For the vertical jump, the older participants typically jumped higher than their
younger cohorts. The 40m-sprint performance also tended to improve with age for both boys
and girls. Finally, the multistage fitness test performances were relatively stable for the boys
with the exception of the B15s. The girls’ multistage fitness test performances were varied
across the age groups.
ANOVAs
Table 20 presents the results of the two-way ANOVAs for the four AIS motor skills. A
significant gender-by-age interaction was found for basketball throw (p < .001) and
significant main effects for gender (i.e., vertical jump and 40m sprint) and age (i.e., vertical
jump) (all ps < .001).
Interaction Effects
Basketball Throw. The simple effect analysis on basketball throw showed that the B12
group threw a significantly shorter mean distance than the other boys (Effect sizes: .75, 1.34,
and 2.61, for B13, B14 and B15, respectively). The B13 group also threw a significantly
shorter mean distance than the B15 group (Effect size: 1.51), but threw further than the G12,
G13 and G14 groups (Effect sizes: 1.26, .91, and .93, respectively). The B14 group showed
significantly better performances than all of the girl groups (Effect sizes: 2.07, 1.64, 1.61,
and 1.38, for G12, G13, G14 and G15, respectively) but had a shorter mean distance score
than the B15 group (Effect size: 1.15). The B15 group showed a significantly better distance
performance when compared with any of the age groups. No significant differences in
basketball throwing distance were found for the within-girl age group analysis. Figure 6
graphs the significant age-by-gender interaction for the basketball throw.
82
Table 19. Means ± SDs for the AIS Motor Skills.
Boys Girls
Age Mean SD Mean SD
Basketball Throw (m) 12 4.41 a d 1.03 3.96 b c d .63
13 5.26 a b d b 1.23 4.33 b c d .76
14 5.83 a c d c 1.09 4.27 b c d .86
15 7.02 abcd d .89 4.51 c d .69
Vertical Jump (cm) 12 24.98 5.25 21.51 4.30
13 27.40 6.59 24.50 6.56
14 28.80 9.04 22.51 5.51
15 35.63 8.29 25.68 6.49
40m sprint (s) 12 7.58 .62 9.17 1.48
13 7.53 .64 8.87 1.33
14 7.41 .60 8.34 1.03
15 7.43 .95 8.35 1.05
Multistage Fitness Test 12 13.87 4.50 10.43 3.25
(laps) 13 13.91 5.17 13.76 5.53
14 13.88 4.56 11.64 4.76
15 11.56 3.48 13.40 3.50
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25. Same superscript letters indicate significant differences within group pairwise
comparisons. Same subscript letters indicate significant differences between group pairwise
comparisons. Bolded letters indicate the focus for the comparisons being made (i.e.,. a =
B12, b = B13, c = B14, d = B15). A Bonferroni correction is used for all post-hoc
comparisons.
83
Table 20. ANOVA Results for the AIS Motor Skills.
Effect df F Sig.
Basketball Throw Gender 1/322 145.25 .001
Age 3/322 28.16 .001
Gender by Age 3/322 12.74 .001
Vertical Jump Gender 1/322 53.19 .001
Age 3/322 11.94 .001
Gender by Age 3/322 3.54 .015
40m sprint Gender 1/322 100.02 .001
Age 3/322 4.51 .004
Gender by Age 3/322 1.92 .127
Multistage Fitness Test Gender 1/322 3.23 .073
Age 3/322 2.36 .072
Gender by Age 3/322 3.93 .009
Figure 6. Significant age by gender interaction for the Basketball throw.
Basketball Throw
1
3
5
7
9
12 13 14 15
Age (years)
Met
ers
Boys Girls
84
Main Effects
Vertical jump. There was a significant main effect for gender, F(1,322) = 53.19, p <
.001, with the boys jumping higher than the girls (Boys, M = 27.74 ± 7.61 and Girls, M =
23.42 ± 5.94; Effect size: .63; 95% CI 26.57 – 28.91 and 22.51 – 24.33, respectively). A
significant main effect was also found for age F(3,322) = 11.94 p < .001. The post-hoc analysis
on the age groups showed that the 15-year-olds jumped higher compared to the other age-
groups (Effect sizes: .93, .50, and .51, for 12-, 13- and 14-years, respectively) (see Table 21).
40m Sprint. For the 40m sprint, there was a significant main effect of gender, F(1,322)
= 100.02, p < .001, with the boys running quicker times than the girls (Boys, M = 7.51 ± .66s
and Girls, M = 8.71 ± 1.28s; Effect size: 1.18; 95% CI 7.41 – 7.61 and 8.51 – 8.89,
respectively).
Table 21. Descriptives for the Main Effect Age on the Vertical Jump Motor Skill.
Vertical Jump n Age Mean SD CI
92 12 23.59# 5.16 22.52 – 24.66
107 13 25.93# 6.70 24.65 – 27.22
90 14 25.38# 7.94 23.72 – 27.04
41 15 29.56# 8.67 26.83 – 32.30
Note. # denotes a significant difference found between the 15-year-olds and all other age
groups. CI = 95% Confidence Interval.
5.2. McCARRON ASSESSMENT OF NEUROMUSCULAR DEVELOPMENT
(MAND)
The MAND instrument records information regarding fine and gross motor skills. The fine
motor skills include putting beads in a box, placing beads on a rod, finger tapping, turning a
bolt into a nut, and sliding a peg along a rod. For the gross motor tasks the skills are grip
strength, finger-nose-finger, two-feet jumping for distance, heel-toe walking, and balancing.
Initially, the results of the reliability analysis on the MAND are reported. Then the
Descriptive and ANOVA results are presented separately for the fine motor and gross motor
tests.
85
5.2.1. Reliability of the MAND
The Means ± SDs for the analysis are presented in Table 22. The results show that the
MAND had good test-retest reliability with a Cronbach alpha range of .72 for finger tapping
to .97 for jumping. In particular, the skills of jumping, finger-nose-finger, balance, and nut
and bolt tests had reliability scores ≥ .90. There was an average of r = .86 across all tests.
This analysis indicates that reliability of the test items on the MAND is acceptable, and can
be used to examine fine and gross motor skills in Malaysian adolescents.
5.2.2. The Neuromuscular Development Index
The Neuromuscular Development Index (NDI) is an index of fine and gross motor skill
level. The motor skills raw scores were initially scaled, then summed, and then converted
using the tables provided by McCarron (1982). The NDI is based on a distribution with a
mean score of 100 and a standard deviation of 15. The age group Means and SDs for the
Boys were B12 - M = 111.2 ± 15.11, B13 - M = 114.5 ± 14.6, B14 - M = 108 ± 16.31, and
B15, M = 104.9 ± 13.64. The Girls’ age group NDI Means ± SDs were G12 - M = 108.8 ±
11.93, G13 - M = 101.7 ± 15.59, G14 - M = 106.2 ± 17.44, and G15 - M = 91.2 ± 10.10.
Table 22. Means ± SDs and Reliability Coefficients for the MAND.
Testing Session
First Second
Variables Mean SD Mean SD r
Beads in Box 53.45 4.70 52.42 5.26 .89
Beads on Rod 26.21 2.88 26.24 3.27 .82
Finger Tapping 90.06 11.17 92.61 10.83 .72
Nut and Bolt 166.42 5.964 167.03 6.12 .90
Rod slide 87.55 6.06 87.12 5.45 .77
Grip strength 44.45 8.52 44.30 7.84 .87
Finger-nose-finger 69.39 6.48 69.21 6.426 .95
Jumping 72.76 12.36 73.36 11.89 .97
Heel-toe 37.67 2.80 38.06 2.36 .74
Balance 96.73 18.82 100.39 18.93 .93
86
5.2.3. Fine Motor Skills
Descriptive and ANOVA analyses were conducted to describe, and also determine, if
performances on the MAND fine motor skills differed with gender and age. The raw scores
reported for each motor skill, and not the MAND scaled scores, were used in these analyses.
Descriptives
Table 23 reports the Means and SDs for the MAND fine motor skills. The results indicate
that the girls placed more beads in the boxes and beads on rods than the boys in all age
groups. The finger tapping performance, however, showed the boys outperforming the girls
in each age group. The girls tended to score higher than boys for the nut and bolt test except
for G14s who achieved similar scores. Results for the rod slide indicated that both the boy
and girl groups showed varied rod slide performances.
ANOVAs
The two (gender) by four (age group) ANOVA results presented in Table 24 reveal no
significant gender-by-age interactions. However, there were significant main effects for both
gender and age.
Main Effects
Gender. Specifically, significant gender effects were found for the fine motor skills
of: beads in box and beads on rod (ps < .001). The results showed that the girls performed
significantly better than the boys for beads in box (Girls, M = 56.99 ± 6.04 and Boys, M =
54.3 ± 5.62; Effect size: .46; 95% CI 56.06 – 57.92 and 53.44 – 55.17, respectively) and
beads on the rod (Girls, M = 27.82 ± 2.46 and Boys, M = 26.20 ± 2.67; Effect size: .63; 95%
CI 27.45 – 28.20 and 25.79 – 26.61, respectively).
Age. For age, significant main effects were found for the fine motor skill finger
tapping (p < .001). The post hoc analysis revealed that the 13-year-olds group (M = 90.39 ±
13.17) performed significantly better than the younger group aged 12 years (M = 82.85 ±
9.93; Effect size: .64) and the older group aged 14 years (M = 82.77 ± 12.0; Effect size: .60)
(see Table 25).
87
Table 23. Means ± SDs for the MAND Fine Motor Skills.
Age Boys Girls
(years) Mean SD Mean SD
Beads in Box 12 53.29 4.29 55.65 4.81
13 54.74 5.43 55.15 5.82
14 54.76 7.68 59.16 6.54
15 55.19 3.73 58.68 5.60
Beads on Rod 12 26.36 2.47 27.27 2.49
13 26.04 3.13 27.63 2.22
14 26.34 2.67 28.10 2.50
15 25.81 1.72 28.52 2.71
Finger Tapping 12 83.24 9.29 82.27 10.93
13 92.26 12.85 88.56 13.34
14 86.51 12.07 79.63 11.13
15 87.38 11.75 83.48 11.74
Nut and Bolt 12 166.20 5.48 168.95 4.97
13 165.02 5.30 167.39 6.17
14 167.98 6.39 167.20 5.03
15 164.56 5.01 165.68 4.15
Rod slide 12 85.02 9.70 87.35 5.38
13 89.32 3.24 87.24 6.50
14 89.02 3.44 89.47 3.35
15 88.25 3.79 87.68 3.52
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25
88
Table 24. ANOVA Results for the MAND Fine Motor Skills.
ANOVA
Variables Effect df F Sig.
Beads in Box Gender 1/322 15.14 .001
Age 3/322 4.01 .008
Gender by Age 3/322 2.11 .099
Beads on Rod Gender 1/322 31.96 .001
Age 3/322 .57 .638
Gender by Age 3/322 1.17 .322
Finger Tapping Gender 1/322 7.58 .006
Age 3/322 9.15 .001
Gender by Age 3/322 .95 .418
Nut and Bolt Gender 1/322 4.34 .038
Age 3/322 2.86 .037
Gender by Age 3/322 1.91 .128
Rod slide Gender 1/322 .002 .963
Age 3/322 4.50 .004
Gender by Age 3/322 2.51 .059
Table 25. Decriptives for the Main Effect Age on the Finger Tapping Motor Skill.
Finger Tapping n Age Mean SD CI
92 12 82.85# 9.93 80.79 – 84.90
107 13 90.39# 13.17 87.87 – 92.92
90 14 82.77# 12.00 80.25 – 85.28
41 15 85.00 11.76 81.29 – 88.71
Note. # denotes a significant difference found between the 13-year-olds and the 12- and 14-
year-old age groups. CI = 95% Confidence Interval.
89
5.2.4. Gross Motor Skills
Descriptive and ANOVA analyses were conducted to describe, and also determine, if
performances on the MAND fine motor skills differed with gender and age. The raw scores
reported for each motor skill, and not the MAND scaled scores were used in these analyses.
Descriptives
Table 26 reports the Means ± SDs for the MAND gross motor skills. For the Grip Strength
skill, the results showed increases in grip strength for both boys and girls with an increase in
age and that the boys scored higher than girls for this skill at every age. A similar pattern
also was found for the Jumping skill by the adolescents. The Finger-nose-finger skill results
were varied across the age groups. For the Heel-toe tandem walk, the performances of the
boys were stable across the age groups with the girls reporting varied performances. Finally,
the mean Balance times for girls aged 13 years and above were better than any of the boy
age groups, with the boys demonstrating similar times across the age groups.
ANOVAs
A series of two (gender) by four (age) ANOVAs examined whether performances on each
gross motor item differed across the gender and age groups (see Table 27). Significant age
by gender interactions were found for two of the gross motor skills: Grip strength (p < .001)
and Finger-nose-finger (p < .001). Figure 7 plots the significant interactions. Main effects
for Jumping (age and gender) and for Heel-toe (gender) (ps < .001).
Interaction Effects
Grip Strength. The simple effects analysis showed that the B15 and the B14 groups
performed significantly better than the younger aged boy groups (B15 Effect sizes: 2.87,
1.78, and .98 for B12, B13 and B14, respectively and B14 Effect sizes: 1.49 and .71 for B12,
and B13, respectively) and all of the girl groups (B15 Effect sizes: 3.11, 3.50, 2.86, and 2.42
for G12, G13, G14, and G15, respectively and B14 Effect sizes: 1.59, 1.58, 1.18, and .97 for
G12, G13, G14, and G15, respectively). The B13 group also performed significantly better
than the B12 group (Effect size: .71) and G12 group (Effect size: .82). No significant
differences in grip strength were evident between the girl age groups.
Finger-nose-finger. The simple effects analysis revealed that the B12 group
performed significantly better than the B13, B14, G13 and G15 groups (Effect sizes: 1.12,
1.02, 1.80, and 2.12, respectively). Additionally, the B14 group also performed significantly
better than the G15 group (Effect size: .61). The B15 group did significantly better than the
90
Table 26. Means ± SDs for the MAND Gross Motor Skills.
Age Boys Girls
(years) Mean SD Mean SD
Grip strength 12 39.84 b c d 9.85 38.73 b c d 8.79
13 47.79 b c d b 12.38 40.87 c d 6.66
14 56.93 c dc 13.42 44.39 c d 7.48
15 69.88 d d 12.47 45.52 c d 8.24
Finger-nose- 12 75.71 a a 2.87 74.81 e 2.39
finger 13 70.28 a 6.26 67.52 a d e f 5.76
14 71.27 a b c 5.79 72.94 f 5.33
15 74.00 c d 2.73 66.36 a c
d
e f 6.69
Jumping 12 72.76 7.88 62.65 8.79
13 79.62 10.53 64.74 9.32
14 82.44 10.61 66.67 10.43
15 86.87 10.89 66.20 9.25
Heel-toe 12 37.87 2.62 37.03 2.82
13 37.55 2.74 34.65 3.59
14 37.61 2.40 36.45 3.20
15 37.56 3.39 33.88 2.93
Balance 12 95.20 16.35 93.22 21.52
13 95.60 17.71 100.74 17.52
14 95.20 17.58 99.37 19.34
15 94.88 16.92 106.88 16.30
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25. Same superscript letters indicate significant differences within group pairwise
comparisons. Same subscript letters indicate significant differences between group pairwise
comparisons. Bolded letters indicate the focus for all of the comparisons being made (i.e.,. a
= B12, b = B13, c = B14, d = B15, e = G12, f = G14). A Bonferroni correction is used for all
post-hoc comparisons.
91
Table 27. ANOVA results for the MAND Gross Motor Skills.
Effect df F Sig.
Grip strength Gender 1/322 87.79 .001
Age 3/322 38.46 .001
Gender by Age 3/322 13.58 .001
Finger-nose-finger Gender 1/322 15.59 .001
Age 3/322 26.58 .001
Gender by Age 3/322 8.26 .001
Jumping Gender 1/322 175.90 .001
Age 3/322 10.77 .001
Gender by Age 3/322 2.99 .031
Heel-toe Gender 1/322 36.49 .001
Age 3/322 5.14 .002
Gender by Age 3/322 3.57 .014
Balance Gender 1/322 5.00 .026
Age 3/322 1.46 .226
Gender by Age 3/322 1.48 .219
Figure 7. Significant age by gender interactions for the gross motor skills of Grip
Strength and Finger-nose-finger.
G13 group and G15 group (Effect sizes: 1.24 and 1.39, respectively). Comparison amongst
the girl groups indicated that the G12 group performed significantly better than the G13 and
Grip Strength
30
40
50
60
70
12 13 14 15
Age (years)
Scor
e
Boys Girls
Finger-Nose-Finger
60.00
65.00
70.00
75.00
80.00
12 13 14 15
Age (years)
Scor
e
Boys Girls
92
G15 groups (Effect sizes: 1.55 and 1.83, respectively). In addition, the G14s performed
better than the G13 and G15 groups (Effect sizes: .97 and 1.13, respectively).
Main Effects
Jumping. There was a significant main effect of gender for the MAND jumping
motor skill, F(1,322) = 175.90, p < .001. The girls performed poorer than the boys (Girls, M =
65.07 ± 9.57 and Boys, M = 78.74 ± 10.77; Effect size: 1.34; 95% CI 63.59 – 66.54 and
77.08 – 80.40, respectively). There was also a main effect for age, F(3,322) = 10.77, p < .001.
Post-hoc analysis revealed that the 12-year-olds performed the jumping motor skill
significantly poorer than the 14-year-olds (Effect size: .45) (see Table 28).
Heel-toe-heel. There was a significant main effect of gender for the MAND motor
skill heel-toe-heel, F(1,322) = 36.49, p < .001. The boys performed better than the girls (Girls,
M = 35.60 ± 3.41 and Boys, M = 37.67 ± 2.67; Effect size: .68; 95% CI 35.08 – 36.12 and
37.26 – 38.08, respectively).
Table 28. Descriptives for the Main Effect Age on the Jumping Motor Skill.
Jumping n Age Mean SD CI
92 12 68.69# 9.60 66.71 – 70.68
107 13 72.11 12.40 69.74 – 74.49
90 14 73.86# 13.10 71.11 – 76.60
41 15 74.27 14.14 69.80 – 78.73
Note. *, denotes significant difference found between the 12- and 14-year-olds. CI = 95%
Confidence Interval.
5.3. BALANCE AND MOVEMENT COORDINATION (BMC) INSTRUMENT
The BMC instrument records information regarding body balance (3 motor skills) and
movement coordination (6 motor skills). The body balance skills include two static balance
tests - balancing on one-foot with eyes open and balancing on one-foot with eyes closed.
The final balance test is a dynamic balance test involving performing sidesteps with the legs
together while jumping sideways. The movement coordination skill tests involve a shuttle
run without an object, hopping in a square, hopping with speed, a shuttle run with an object,
a zigzag run, and a quadrant jump exercise. The reliability of the BMC, gender and age
93
differences in the BMC tests among Malaysian adolescents were addressed by dividing the
results into:
i. Reliability of the BMC motor skill instrument,
ii. body balance items, and
iii. movement coordination items.
5.3.1. Reliability of the BMC Motor Skill Instrument
Initially, analyses of two BMC item scores obtained from two testing sessions were
examined for the reliability of each item and overall items. Thirty-three participants aged 13
years (20 boys, 13 girls) participated in both sessions. Table 29 presents means and standard
deviations for BMC test items on the two testing sessions, the reliability scores for each of
the BMC items, and the overall reliability score of the BMC tests. Results showed that the
shuttle run with and without object, zigzag run and hopping speed tests recorded the highest
reliability scores (r = .97 - .99) while the quadrant jump test was the lowest (r = .70). The
average r across all measures was 0.88. The high individual and average BMC test scores
indicated that test items were reliable and acceptable for further testing.
Table 29. Means ± SDs and Reliability Coefficients for the BMC.
Testing session
First Second
Variables Mean SD Mean SD r
One-foot balance with eyes open (s) 110.82 21.62 108.42 19.27 .88
One-foot balance with eyes closed (s) 49.82 30.36 65.52 29.66 .81
Dynamic balance (number of jumps ) 19.48 5.39 20.27 5.29 .84
Hopping-in-square (number of hops) 45.73 7.78 47.15 7.58 .84
Hopping speed (s) 11.03 2.91 11.17 2.78 .99
Zigzag run (s ) 13.09 1.36 13.17 1.38 .98
Shuttle run with object (s) 10.65 1.44 10.78 1.53 .97
Shuttle run without object (s) 10.55 1.44 10.56 1.47 .99
Quadrant jump (total score) 27.73 5.77 29.53 7.35 .70
94
5.3.2. Body Balance Motor Skills
Descriptives
The means and standard deviations of body balance items of the BMC can be found in Table
30. For the One-foot-eyes open task the mean time for the girl groups increased with age,
and the boys performances varied across age groups. The mean times for the girl groups also
increased with age for the One-foot balance with eyes closed. However, the boys’ mean
balance times decreased with age. Finally, Dynamic balance of the boys was more stable
than that found for the girls.
ANOVAs
A main effect for gender was found for the dynamic balance motor skill (p < .001)(see Table
31). No other significant differences were noted between the other groups or within each
gender age groups.
Table 30. Means ± SDs for the BMC Body Balance Motor Skills.
Age Boys Girls
Variables (years) Mean SD Mean SD
One-foot 12 117.05 10.96 108.30 20.64
balance 13 114.51 17.78 111.37 15.99
with eyes open 14 113.10 14.95 112.00 14.63
(sec.) 15 117.63 6.72 117.00 7.97
One-foot 12 52.00 28.03 48.97 34.46
Balance with 13 51.25 30.66 57.02 36.14
eyes closed 14 53.05 33.71 60.51 32.92
(sec.) 15 48.63 32.46 67.24 34.27
Dynamic 12 20.11 3.68 19.19 3.94
balance 13 22.72 3.77 20.15 5.38
(jumps) 14 22.98 5.76 17.90 5.16
15 22.69 2.80 22.36 4.31
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25.
95
Table 31. ANOVA Results for the BMC Body Balance Motor Skills.
Univariate ANOVA
Variables Effect df F Sig.
One-foot balance Gender 1/322 3.547 .061
with eyes open Age 3/322 1.065 .364
Gender by Age 3/322 1.197 .311
One-foot balance Gender 1/322 3.375 .067
with eyes closed Age 3/322 .733 .533
Gender by Age 3/322 1.050 .371
Dynamic balance Gender 1/322 16.46 .001
Age 3/322 4.59 .004
Gender by Age 3/322 3.98 .008
A simple effect analysis on dynamic balance indicated that the girls dynamic balance time
was significantly less compared to the boys (Girls, M = 19.60 ± 5.04 and Boys, M = 21.91 ±
4.41; Effect size: .49; 95% CI 18.82 – 20.38 and 21.83 – 22.59, respectively).
5.3.3. Movement Coordination Motor Skills
Descriptives
Results indicated that the mean times of 12-14 year old boys for the Shuttle run without
object were better than the girls’ groups. However, the 15 year old girls were slightly better
than the boys (see Table 32). Boys outscored the girls at Hopping-in-square except at age 15
years, where G15 performed the greatest number of hops. Hopping speed times tended to
improve with age for boys but the reverse tended to occur with girls. The boys’ mean times
for the Shuttle run with object were better than the girls, but the girls’ times did tend to
improve with age. Both boys and girls recorded similar times for the Zigzag run and that
their performances were similar across the age groups. The mean number of Quadrant jumps
for the girls increased with age, and that from age 13 the girls outperformed the boys. The
boys’ performances were varied across their age groups.
96
Table 32. Means ± SDs for the BMC Movement Coordination Motor Skills.
Age Boys Girls
(years) Mean SD Mean SD
Shuttle run 12 10.04 .85 11.02 1.05
Without 13 9.76 .82 10.97 1.12
Object (secs) 14 9.92 .79 10.97 1.14
15 10.60 1.43 10.26 .97
Hopping-in- 12 49.75 9.23 45.59 6.09
Square (jumps) 13 49.33 9.60 46.57 11.03
14 48.61 13.04 44.76 8.23
15 46.19 11.95 50.64 6.77
Hopping speed 12 9.34 a 1.68 10.79 a 3.04
(secs) 13 9.48 1.49 10.74 2.65
14 10.32 1.70 10.17 2.96
15 10.72 2.59 9.14 2.04
Shuttle run with 12 10.18 .82 11.86 1.48
object (secs) 13 9.83 .82 11.49 1.32
14 9.99 .79 11.43 1.33
15 10.76 1.50 11.05 1.21
Zigzag run (secs) 12 13.07 1.38 13.15 1.72
13 12.11 1.53 13.12 1.25
14 12.94 1.28 13.29 1.53
15 13.78 1.17 13.02 1.43
Quadrant jump 12 25.65 5.62 26.55 8.02
(jumps) 13 28.65 6.75 29.17 6.69
14 26.35 5.54 31.30 7.89
15 25.69 6.20 32.70 6.73
Note. ns, B12 = 55, B13 = 53, B14 = 41, B15 = 16, G12 = 37, G13 = 54, G14 = 49, G15 =
25. Same superscript letters indicate significant differences within group pairwise
comparisons. Same subscript letters indicate significant differences between group pairwise
comparisons. Bolded letters indicate the focus for all of the comparisons being made (i.e., a
= B12). A Bonferroni correction is used for all post-hoc comparisons.
97
Table 33. ANOVA Results for the BMC Movement Coordination Motor Skills.
Effect df F Sig.
Shuttle run without Gender 1/322 35.84 .001
Object Age 3/322 .72 .544
Gender by Age 3/322 4.92 .002
Hopping-in-square Gender 1/322 1.83 .178
Age 3/322 .40 .751
Gender by Age 3/322 2.02 .111
Hopping speed Gender 1/322 .76 .384
Age 3/322 .19 .901
Gender by Age 3/322 5.44 .001
Shuttle run with Gender 1/322 90.21 .001
object Age 3/322 1.72 .164
Gender by Age 3/322 3.82 .010
Zigzag Run Gender 1/322 .94 .334
Age 3/322 3.96 .009
Gender by Age 3/322 4.08 .007
Quadrant Jump Gender 1/322 17.11 .001
Age 3/322 3.78 .011
Gender by Age 3/322 3.61 .014
ANOVAs
Results from univariate ANOVAs (see Table 33) revealed a significant gender by age
interaction for Hopping speed (p < .001). Significant main effects for gender were found for
the two Shuttle run motor skill tests and for the Quadrant Jump (ps < .001).
Interactions.
Hopping Speed. The simple effect analysis indicated that the B12s differed significantly
from the G12 group (Effect size: 62). No other significant performance differences were
evident between the boy and girl groups. In addition, no significant performance differences
were found amongst the boy groups or amongst the girl groups. Figure 8 illustrates the
significant age by gender interaction for the Hopping speed motor skill.
98
Figure 8. Significant age by gender interaction for the Hopping speed motor skill.
Main Effects.
Shuttle Run without Object. The simple effect analysis on the Shuttle run without object
times showed that the boys time was significantly quicker than the girls time (Girls, M =
10.91 ± 1.13 and Boys, M = 9.87 ± 1.31; Effect size: .85; 95% CI 10.74 – 11.09 and 9.67 –
10.07, respectively).
Shuttle Run with Object. The subsequent simple effect analysis showed that the
boys time was significantly quicker than the girls time (Girls, M = 11.52 ± 1.35 and Boys, M
= 10.08 ± .92; Effect size: 1.25; 95% CI 11.31 – 11.73 and 9.94 – 10.22, respectively).
Quadrant Jump. The simple effect analysis of the quadrant jump showed that the
boys number of jumps were significantly less to that reported by the girls (Girls, M = 29.75
± 7.61 and Boys, M = 26.79 ± 6.13; Effect size: .43; 95% CI 28.57 – 30.91 and 25.85 –
27.73, respectively)
Hopping Speed
88.5
99.510
10.511
12 13 14 15
Age (years)
Seco
nds
Boys Girls
99
5.4. DISCUSSION
5.4.1. Performance on the AIS – Anthropometric Data
Improvement of motor ability varies with gender and age groups during adolescence
(Bloomfield, Blanksby & Ackland, 1990; Espenschade, 1940; Kim et al., 1999; Little et al.,
1997; Loko et al., 2000, 2003; Malina, 1978; Thomas & French, 1985; Tomkinson et al.,
2003; Viru et al., 1998; Volver & Selge, 1997; Volver et al., 2000). Thus, height, body mass
and BMI portrays subjects’ physiques as motor ability performances are correlated with
chronological age in adolescents (Viru et al., 1998; Volver & Selge, 1997; Volver et al.,
2000). Table 34 illustrates the means and standard deviations of heights and weights from
the data in this study, and those previously collected by the Malaysian Sports Council
(Majlis Sukan Negara, 1998). The pattern for heights and weights across age tends to
parallel that of Australian adolescents from both genders (Tomkinson et al., 2003). These
results also parallel those reported for Estonian girls (Loko, Aule, Sikkut, Ereline & Viru,
2000; Loko et al., 2003; Volver & Selge, 1997; Volver et al., 2000), and Japanese, Chinese,
Hong Kong and Taiwanese girls (Kim et al., 1999).
Table 34. Mean Heights and Weights ± SDs of the Malaysian Sports Council Data and
the Current Research.
Malaysian Sports Council Data Participants in this Study
Boys Girls Boys Girls
Age Mean SD Mean SD Mean SD Mean SD
Height 11 146.5 9.9 147.8 7.83 - - - -
(cm) 12 146.54 9.40 148.4 7.78 148.3 8.34 150.2 6.4
13 153.81 9.90 151.85 7.13 154.8 9.41 153.4 6.7
14 161.22 9.15 155.44 6.50 160.3 8.55 155.6 5.1
15 - - - - 165.1 6.63 156.4 6.6
Weight 11 39.8 10.32 39.7 8.57 - - - -
(kg) 12 39.64 10.26 40.88 9.47 39.96 10.09 45.78 13.70
13 43.67 10.34 42.96 8.61 46.60 14.14 46.20 13.99
14 49.41 10.93 45.37 8.14 47.17 11.36 48.53 9.95
15 - - - - 59.06 18.01 51.58 11.70
100
The 12-15 year old Malaysian adolescents scored lower absolute percentile scores for height
and weight when compared with Australian adolescents (Australian Sports Commission,
1998) (See Figures 9 and 10). Boys aged 12-14 years and girls aged 13-14 years recorded
lower mean BMIs than Australian adolescents; but the mean BMI scores for 15 year old
boys and girls, and 12 year old girls in this study, were larger than their Australian
counterparts (Tomkinson et al., 2003). The mean BMIs among 13 and 15 year old Malaysian
girls were slightly higher than Korean girls (Kim et al., 1999). However, despite some
higher and lower BMIs when compared with other populations, the Malaysians scored
between the 5th and 85th percentiles, thereby demonstrating a healthy weight status (de Onis,
Onyango, Borghi, Siyam, Nishida & Siekmann, 2007; Pon Lai Wan, Kandiah & Mohd Nasir
Mohd Taib, 2004).
101
Figure 9. The percentile patterns of height among Australian adolescents (Australian
Sports Commission, 1998) and Malaysian participants in this study (AG = Australian girls,
AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian Boys).
Height - 12 year olds
130
140
150
160
170
0 20 40 60 80 100
percentile
cm
AG AB MG MB
Height - 13 year olds
130
140
150
160
170
180
0 20 40 60 80 100
percentile
cm
AG AB MG MB
Height - 14 year olds
140
150
160
170
180
0 20 40 60 80 100
percentile
cm
AG AB MG MB
Height - 15 year olds
140
150
160
170
180
0 20 40 60 80 100
percentile
cm
AG AB MG MB
102
Figure 10. The percentile patterns of weight among Australian adolescents (Australian
Sports Commission, 1998) and Malaysian participants in this study (AG = Australian girls,
AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian Boys).
5.4.2. Performance on the AIS – Motor Performance Data
At all ages, the boys performed better than the girls in basketball throw, vertical jump and
the 40m sprint motor skills. To help further compare the Malaysian subjects’ scores in the
AIS tests with Australian norms, percentile scores were developed. The performance
patterns for the Malaysian sample on vertical jump and 40m sprint increased across age and
gender in a similar fashion to that reported for their same-aged Australian adolescent
Weight -12 year olds
20
30
40
50
60
70
0 20 40 60 80 100
percentile
kg
AG AB MG MB
Weight - 13 year olds
20
30
40
50
60
70
0 20 40 60 80 100
percentile
kg
AG AB MG MB
Weight - 14 year olds
20
30
40
50
60
70
80
0 20 40 60 80 100
percentile
kg
AG AB MG MB
Weight - 15 year olds
20
30
40
50
60
70
80
90
0 20 40 60 80 100
percentile
kg
AG AB MG MB
103
counterparts (Australian Sports Commission, 1994; Australian Sports Commission, 1998;
Majlis Sukan Negara, 1998; Tomkinson et al., 2003). However, normative values on the four
other items from the AIS battery were lower for the 12-15 year Malaysians than the
Australian norms (Australian Sports Commission, 1998). Graphical percentile comparisons
of participants in this study and the AIS norms (Australian Sports Commission, 1998) are
shown in Figures 11, 12, 13 and 14. It is probable that the lower normative scores of the AIS
tests among Malaysian adolescents in this study resulted from the Malaysians being shorter
and thinner than the Australian adolescents. Because somatic growth influences
performance, greater height and weight provide greater strength, speed and endurance as was
shown by Australian adolescents relative to subjects in this study (Beunen et al., 1988;
Beunen et al., 1992; Goslin & Burden, 1986; Planinsec, 2001; Thomas & French, 1985).
Strength and power are related to body shape, composition and proportionality, and posture
(Bloomfield et al., 1994). Moreover, height can influence performance via greater weight
and biomechanical advantage (Olgun & Gurses, 1984). Also, weight and blood volume
increase in line with the cube of height whereby size changes tend to produce differences in
the relationships between variables such as strength, weight, power output, acceleration and
work capacity (Watson, 1995). The relationships between the above components show that
individuals of various sizes are better equipped for different types of activity. Percentile
score comparisons indicated that Australian adolescents have physical attributes that could
give a performance advantage in selective tasks.
The performance differences between Australian and Malaysian adolescents demonstrated
that separate Malaysian normative scores are necessary if using the AIS instrument as a
mass screening test for talent identification in Malaysia. Also, further investigations of the
AIS test items are essential to signify their suitability for use in Malaysia. A comparative
study applied the AIS instrument to South African adolescents, and found the test battery
was unsuitable when recording results from different parts of the country (du Randt, 2000;
Viljoen et al., 2004). This was considered to be due to little opportunity for involvement in,
or exposure to, physical activity and/or sport, and that the South African subjects came from
low socio-economic (SES) conditions which might have contributed to the below average
performances. The latter is possible in the present study because stratified sampling was not
possible and the convenience sampling could have masked results expected perhaps from a
stratified sample. Another issue is the expectation of lower scores due to smaller stature,
decreased activity experiences and time in a healthy environment (e.g., Food volume and
type, rest, hours of sunlight exposure). Also, the nature, validity, reliability and
generality/specificity of the tests used are important. Across populations, tests of inherent
104
‘raw talent’ components might assist in comparative studies as they are more universally
applicable.
Figure 11. The percentile patterns for vertical jump results among Australian
adolescents (Australian Sports Commission, 1998) and Malaysian participants in this study
(AG = Australian girls, AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian
Boys).
Vertical jump - 12 year olds
10
20
30
40
50
0 20 40 60 80 100
percentile
cm
AG AB MG MB
Vertical jump - 13 year olds
10
20
30
40
50
60
0 20 40 60 80 100
percentilecm
AG AB MG MB
Vertical jump - 14 year olds
10
20
30
40
50
60
0 20 40 60 80 100
percentile
cm
AG AB MG MB
Vertical jump - 15 year olds
10
20
30
40
50
60
0 20 40 60 80 100
percentile
cm
AG AB MG MB
105
Figure 12. The percentile patterns for 40m sprint results among Australian adolescents
(Australian Sports Commission, 1998) and Malaysian participants in this study (AG =
Australian girls, AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian Boys).
40 m sprint - 12 year olds
5
6
7
8
9
10
11
12
0 20 40 60 80 100
percentile
seco
nds
AG AB MG MB
40 m sprint - 13 year olds
5
6
7
8
9
10
11
12
0 20 40 60 80 100
percentile
seco
nds
AG AB MG MB
40 m sprint - 14 year olds
4
5
6
7
8
9
10
11
0 20 40 60 80 100
percentile
seco
nds
AG AB MG MB
40 m sprint - 15 year olds
4
5
6
7
8
9
10
11
0 20 40 60 80 100
percentile
seco
nds
AG AB MG MB
106
Figure 13. The percentile patterns for the multistage fitness test results among
Australian adolescents (Australian Sports Commission, 1998) and Malaysian participants in
this study (AG = Australian girls, AB = Australian Boys, MG = Malaysian Girls, MB =
Malaysian Boys).
Multistage Fitness Test - 12 year olds
0
20
40
60
80
100
0 20 40 60 80 100
percentile
com
plet
e la
ps
AG AB MG MB
Multistage Fitness Test - 13 year olds
0
20
40
60
80
100
0 20 40 60 80 100
percentile
com
plet
e la
ps
AG AB MG MB
Multistage Fitness Test - 14 year olds
0
20
40
60
80
100
0 20 40 60 80 100
percentile
com
plet
e la
ps
AG AB MG MB
Multistage Fitness Test - 15 year olds
0
20
40
60
80
100
0 20 40 60 80 100
percentile
com
plet
e la
ps
AG AB MG MB
107
Figure 14. The percentile patterns for the basketball throw results among Australian
adolescents (Australian Sports Commission, 1998) and Malaysian participants in this study
(AG = Australian girls, AB = Australian Boys, MG = Malaysian Girls, MB = Malaysian
Boys).
Basketball Throw - 12 year olds
0
2
4
6
8
5 20 40 60 80 95
percentile
m
AG AB MG MB
Basketball Throw - 13 year olds
0
2
4
6
8
10
5 20 40 60 80 95
percentile
m
AG AB MG MB
Basketball Throw - 14 year olds
0
2
4
6
8
10
5 20 40 60 80 95
percentile
m
AG AB MG MB
Basketball Throw - 15 year olds
0
2
46
8
10
12
5 20 40 60 80 95
percentile
m
AG AB MG MB
108
5.4.3. Performance on the MAND Motor Skills
Overall, the mean Neuro-Developmental Index (NDI) for the Malaysian sample was higher
(NDI = 106.7) than that reported by McCarron (1982) among the USA normative sample
(NDI = 100). Analysis across age and gender indicated that Malaysian boys aged 12-15
years, and girls aged 12-14 years scored above the mean, but girls aged 15 years scored
below the USA, NDI mean score. The 13-year-old boys scored the highest NDI among
Malaysian adolescents. Fine motor task scores showed that all participants, except B15,
performed better than the normative scores from the USA.
In general the findings indicated that participants’ superior performances in fine motor tasks
provided the advantage which enabled the Malaysian scores to be greater than the USA
mean on the overall score of NDI, when compared with the gross motor tasks alone. The
reasons for good performances in the fine motor tasks were not examined and need further
study. Perhaps the need for Malaysian subjects to help with household tasks during
childhood indirectly improved their fine motor skills. Most participants were from average
socioeconomic levels (farmers, rubber tappers, palm oil plantation workers, factory workers
and government servants) where girls did household activities when the parents were out at
work, and the boys assisted parents on a farm or rubber/palm oil plantation. Within the
Malaysian sample the females outperformed the boys for beads-in-box and beads-on-rod. It
is possible that the girls experience in household chores provided them with an advantage
over the boys for these two fine motor skills. Additionally, the 13-year-olds significantly
outperformed the 12- and 14-year-olds in finger tapping. It is not apparent as to why this
finding came about.
Malaysian boys scored around the same as, and girls scored less than the USA average on
the gross motor tasks. The mean gross motor average score among girls for each age group
was below the USA norm. The largest variation was found for Malaysian G15 which was 15
points lower than the USA norm average. An examination of individual gross motor items
indicated that, generally, boys aged 12-15 years scored between low and average values of
8-10 less than scaled scores of USA norms on the two gross motor items (grip strength and
jumping task). However, Malaysian girls from the same age groups scored below average by
less than 5-8 than scaled scores of USA norms on the same two gross motor items. Scale
score differences were minimal for the other three gross motor tasks. The below average
scores by Malaysian girls, when compared with USA norms, suggested that different norm
scores for the grip strength and jumping tasks, relative to gender and age, are essential for
Malaysian adolescents.
109
The scale scores also indicated that the boys already showed a large range of scale scores
(score = 11) which differed from girls (score = 7) as early as 12 years old on the jumping
task; and 13 year olds on the hand strength task (scale score, boys = 9; girls = 7). However,
the table of scale scores of MAND test norms only provided separate scale scores of the grip
strength and jumping task between genders, and was for 14-18 year olds and young adults. A
previous study on accelerated improvement of explosive strength indicated that boys
demonstrated peak improvements between 13 and 16 years, while peaks for girls ranged
between 11 and 12 years old (Viru et al., 1998). There was a different rate and timing of
acceleration in improved explosive strength (Viru et al., 1998) and a large range of scaled
performances on two gross motor items found between gender in this study. Thus, a separate
normative scale score across gender and age, as early as 12 years old on the jumping task
and 13 years old on grip strength, appears necessary for Malaysian adolescents. Only then
can accurate evaluations be derived for individual motor ability performances when using
the MAND test.
Finally, within the Malaysian sample the male and female adolescents had varied
performances on the gross motor skills grip strength and finger-nose-finger. For the grip
strength task for both boys and girls their strength increased, with this increase being far
greater for the boys. It is possible that simple maturation factors in boys can account for this
difference. For the finger-nose-finger task the boys generally outperformed the girls. It is not
apparent as to why this finding came about. The boys were also superior in their jumping
and heel-toe performances compared to the girls. Finally, 14-year-olds significantly
outjumped the 12-year olds. It is possible that for the jumping performances simple
maturation factors in the boys and for age can account for these differences. With regards to
the heel-toe performances it is not apparent as to why this finding came about.
5.4.4. Performance on the BMC Motor Skills
For the motor skills assessing body balance, only performance on the Dynamic Balance was
found to show significant differences. In general the boys performed this motor better than
the girls.
The two movement coordination motor skills of the Shuttle Run and the Shuttle Run with
Object revealed that the boys outperformed the girls. An examination of Hopping Speed
revealed that despite outperforming the girls at age 12 the boys performance deteriorated
with an increase in age while the girls improved their perfromance the older they got.
110
Finally, the results for the Quadrant Jump indicated that the number of correct jumps for the
girls increased with age and that from age 13 the girls outperformed the boys.
Overall, the younger boys of 12-13 years old performed better in the dynamic balance,
shuttle-run-without-object, shuttle-run-with-object, quadrant jump when compared with girls
of the same ages. However, the 15 year old girls performed better in hopping speed and
quadrant jump than boys at the same age. The reason for the better performances on these
two motor skills shown by G15 is unclear. The lower BMI of girls could be due to being
thinner than the boys and it was easier for them to project and absorb body weight, produce
minimal contact duration and maintain balance required for hopping and quadrant jumps.
5.5. SUMMARY
Malaysia has a unique culture and social system via three major ethnic groups, Malay,
Chinese and Indians, who live together harmoniously. In the educational system, physical
education (PE) classes are conducted in same-gender groups and are preferably conducted
by a teacher of the same gender. As students wear uniforms to school, they need to change
into suitable PE attire for class. Also, the Malaysian education system places much emphasis
on academic examination performances, from which PE is excluded. Hence, the focus and
participation in PE classes is less valued and sometimes neglected. Therefore, a description
of motor ability performances is important to illustrate the performance levels among
Malaysian adolescents.
Overall, Malaysian adolescents in this study demonstrated lower absolute performances in
comparison to other populations in MAND and AIS tests. This might be due to lack of large-
scale public health/education promotion on the importance of motor ability, fitness and sport
for Malaysian children, adolescents and adults; and they are not motivated into exercise and
sport. The lower motor abilities among girls could be influenced by socio-cultural factors in
Malaysian society, which usually draws them into domestic tasks rather than vigorous
participation in sports. The resultant limited sporting opportunities would reduce the chances
for girls to explore and excel in the motor ability performances required in elite sport.
The wide range in variability of motor task scores across gender and age in this study was
comparable with previous results (Branta et al., 1984; Espenschade, 1940; Malina, 1978;
Thomas & French, 1985). Evidence indicates that performance changes are influenced by
somatic growth and maturation (Beunen et al., 1988; Beunen et al., 1992; Planinsec, 2001;
Thomas & French, 1985); environment (Birrer & Levine, 1987; Malina, 1978; Thomas &
111
French, 1985); social, cognitive and other personal factors (Sallis & Hovell, 1990).
Generally, these factors have contributed to better performances among boys (Nelson et al.,
1986; Thomas, 2000; Thomas & French, 1985) than girls (Little et al., 1997; Nelson et al.,
1986; Thomas, 2000), as occurred in this study. It can be concluded that physical growth,
maturation, environment and social factors do influence the motor ability performance of
Malaysian adolescents as measured by the AIS, MAND and BMC.
The above average scores on the NDI of MAND tests, based on USA norms, suggests a need
to develop normative data for the MAND test specific to Malaysian adolescents. It is
possible that the greater manual requirements of Malaysian living provide an advantage in
this domain. Norms are based upon the status quo, as gender and age factors influence
performance on several items of gross motor tasks, as shown in this study. Different norm
scores which are specific to gender and age are essential from as early as 12 years old. In
the MAND test, the norm scores are specific to gender and age, but were started only at the
age of 14 for grip strength and jumping. The development of such norms would reflect the
present condition of the Malaysian population.
This chapter has provided descriptive information on 330 Malaysian adolescents on the AIS,
MAND and BMC motor skill test batteries. The results presented here could be used in
establishing norms for Malaysian age groups, albeit limited to a relatively small sample size
(N = 330), and 12-15 year old subjects not randomly selected. Then, it could provide a
baseline of information for identifying athletic talent because, overall, the tests detected both
gender and age differences in performance. They incorporated recognised basic TI measures
via the AIS instrument; and also included fine motor, gross motor, balance and movement
coordination skill tests via the MAND and BMC. The inclusion of the MAND and BMC
with the AIS for TI among Malaysians was due to suggestions made that the AIS on its own
may inappropriate for use with populations in cultures markedly different to that found in
Australia. Thus, the broader nature of the MAND and BMC were hoped to tap into the
motor skills relevant to Malaysians. This study suggested that they did but not sufficiently so
to demonstrate that they will help TI in Malaysia. To examine this further, one needs to find
whether the tests can reliably discriminate between individuals. Hence, the discriminatory
ability of a motor skill instrument made up of the combined AIS+BMC motor skills was
explored in two ways i) to classify the adolescents into three motor coordination groups
derived by the MAND, and ii) to classify the adolescents into three motor ability groups
derived from a framework out of the current research. This framework is GMA.
112
CHAPTER 6
DISCRIMINANT ANALYSIS OF THE COMBINED AIS+BMC MOTOR SKILL SET
A combined AIS+BMC motor skill set underwent discriminant analysis to find a reduced set
of motor skills that could reliably discriminate between the three motor ability groups. The
groups were derived firstly from the MAND which is a valid and reliable diagnostic tool for
neuromuscular development (McCarron, 1982). Although the MAND is best suited to a
basic level recognition of motor problems within individuals, it was considered to be a good
basic assessment of motor coordination. McCarron (1982) provided standardised
measurements with cut-off values for different levels of disability; based on a distribution
with a mean of 100 and a standard deviation of 15. This was because McCarron considered
that disability begins 1 standard deviation below the mean. However, the top end of the
distribution was also of key interest in the current study. Thus, participants falling 1 standard
deviation below the mean were considered poorly coordinated, and those between 1 standard
deviation below or above the mean were considered normally coordinated. Finally, those
participants greater than1 standard deviation above the mean were considered to have high
levels of motor coordination.
Because the MAND best recognised the motor problems end of the skill continuum,
attention was directed to a framework considered more applicable to sport. This framework
was derived from the pairing of the AIS and BMC motor skill instruments used to test for a
‘g’ in motor ability. Specifically, individual factor scores from the higher-order factor
analysis testing a motoric ‘g’ were identified for each participant. Based on a distribution
with a mean of 0 and a standard deviation of 1, the participants were then categorised into
three groups of motor ability, similar the procedure as described above for the MAND.
6.1. GROUP CLASSIFICATION BASED ON SCORES ON THE MAND
Using the Neuromuscular Development Index (NDI) scores derived from the MAND, the
participants were initially classified into three groups of motor coordination – poor motor
coordination, normal motor coordination and high motor coordination. The NDI is a general
measure of the MAND motor skills or a motor quotient and is comparable to IQ (McCarron,
1982). Subsequently, the NDI is based on a distribution where the Mean is 100 and the
Standard Deviation is 15. Within McCarron’s (1982) framework, scores falling more than
113
one standard deviation from the mean are considered not normal. The current research also
adopted this approach. Thus, participants with an NDI score < 85 for the MAND were
categorised as poor; those participants with an NDI score from 85 to 115 were classified as
normal, and those participants with an NDI score > 115 were classified as high. The number
of participants and percentages classified according to gender, age and level of coordination
are presented in Table 31.
As seen in Table 35, 10 participants were dropped from the original 330 subjects due to
missing data. The missing data appeared to be randomly scattered throughout the groups and
predictors. Additionally, some of the cells were too small for discriminant analysis. Hair et
al. (1998) recommended a minimum of 20 cases per group for analysis purposes, and for the
adolescent boys an n = 7 was unacceptable. This left a sample of 320 for analysis purposes
(29 - poor coordination, 223 - normal coordination, and 68 - high coordination).
Table 35. Number and Percentage of Participants in the Three Motor Coordination
Groups.
Age
(years)
Poor Normal High
Boys Girls Boys Girls Boys Girls Total %
12 3 - 35 31 16 6 91 28.44
13 1 8 31 41 18 5 104 32.50
14 3 7 27 26 9 13 85 26.56
15 - 7 15 17 1 - 40 12.50
TOTAL 7 22 108 115 44 24 320 100
Statistical analyses
Stepwise estimation was used to derive the discriminant function. This type of analytic
approach reveals the best set of motor skills that can discriminate between the three groups.
Then, the discriminant functions were examined to note any motor skills of importance that,
due to collinearity issues, were not present in the stepwise findings. A potency index was
also calculated to indicate the relative importance of a motor skill to the discriminant
function (Hair et al., 1998). The fit of the discriminant analysis was then assessed using a
jackknife classification using prior probabilities to account for the unequal sizes of the
groups. The jackknife classification procedure was employed because the sample was unable
to be split to allow for cross validation of the discriminant findings. The jackknife
114
classification procedure estimates the discriminant model by leaving out one observation and
then predicting that case with the estimated model. As this is done in turn for each
observation, thus, that observation never influences the discriminant model that predicts its
classification (Hair et al., 1998). Hence, jackknifed classification gives a more realistic
estimate of predictors with the ability to separate the groups (Tabachnick & Fidell, 2007).
Finally, an examination of the adolescents who were misclassified was undertaken to
understand the nature of these individuals. For the misclassification examination, the
standard predicted membership was based on the standard classification procedure, not the
jackknife procedure. Thus, the misclassified groups’ n may differ from that reported for the
jackknife classification procedure.
It was intended to to perform discriminant analysis separately on the boys and the girls.
However, owing to the Poor coordination group of the boys failing to meet the
recommended minimum 20 cases per group (Hair et al., 1998), only the full sample results
are presented here. To remove the possible confounding effect of chronological age and
gender differences, the raw scores of the AIS and BMC motor skills were standardised
within-gender-by-age classification. They were then transformed into T-scores based on
means and standard deviations, for each gender and age group. These normalised T-scores
then were used in the discriminant analysis. To see the results of a discriminant analysis
performed separately on the adolescent girls please refer to APPENDIX J on the CD.
6.2. RESULTS – ALL PARTICIPANTS
The results of the stepwise estimation revealed six motor skills that could discriminate
between the three groups. Specifically, the Shuttle-Run-With-Object entered on the first step
Wilks Lambda = .71, F(2, 317) = 63.83, p < .001, the Balance-Eyes-Open test on the second
step Wilks Lambda = .65, F(4, 632) = 37.67, p < .001, the Basketball Throw test on the third
step Wilks Lambda = .60, F(6, 630) = 30.60, p < .001, the Balance-Eyes-Closed test on the
fourth step Wilks Lambda = .57, F(8, 628) = 25.22, p < .001, the Hopping Speed test on the
fifth step Wilks Lambda = .55, F(10, 626) = 21.68, p < .001, and the Dynamic Balance test
on the sixth step Wilks Lambda = .54, F(12, 624) = 18.96, p < .001.
Two canonical discriminant functions were computed for the AIS+BMC (see Table 36).
Specifically, the first function produced a Wilks Lambda = .54, with a Chi-square (12) =
195.50, p < .001, and the second function produced a Wilks Lambda = .91, with a
115
Table 36. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canonical Correlations, Eigenvalues and Group Centroids for the
Three Motor Coordination Groups.
Discriminant Function
First Second
SW Value PI SW Value PI
Shuttle Run With Object .53 .76 .50 -.32 -.22 .01
Shuttle Run NI .71 .44 NI -.11 .00
Hopping Speed .35 .67 .39 -.12 -.03 .00
Zigzag Run NI .51 .23 NI .04 .00
Basketball Throw .41 .49 .21 -.19 -.17 .00
Balance Eyes Closed .31 .44 .17 .15 .36 .02
40m Sprint NI .43 .16 NI .12 .00
Multistage Fitness Test NI .34 .10 NI .06 .00
Hopping-in-Square NI .26 .06 NI .22 .01
Quadrant Jump NI .25 .05 NI .16 .00
Vertical Jump NI .23 .05 NI .05 .00
Balance Eyes Open .13 .29 .07 .71 .77 .08
Dynamic Balance -.03 .27 .06 .56 .53 .04
Canonical Correlation .64 .30
Eigenvalue .69 .10
Group Centroids
Poor -1.10 -.91
Normal -.33 .17
High 1.54 -.16
Note. SW: Standardised weights. NI: Not included in the stepwise solution. Value: Structure
correlations with correlations greater than .50 in bold. PI: Potency Index.
Chi-square (5) = 30.23, p < .001. The canonical R2s for the two functions were .41 for the
first function and .09 for the second function. The two discriminant functions accounted for
about 87% and about 13%, respectively, of the between-group variability. An examination
116
of the unstandardised canonical discriminant functions evaluated at group means reveals that
the first function maximally separates the Highly coordinated group from both the Normal
and Poorly coordinated groups. The second function discriminates between the Normal
group and the Poor group with the High group in between.
An examination of the structure correlations for the discriminant analysis revealed that two
motor skills not reported in the stepwise estimation - the shuttle run and the zigzag run -
appeared to have a substantial effect on discriminating between the three coordination
groups. Thus, for the first discriminant function, the structure correlations suggest that the
best motor skills for discriminating between the high motor coordination group and the other
two motor coordination groups are: Shuttle-Run-with-Object, Shuttle Run, Hopping Speed
and Zigzag Run. The means for these motor skills revealed that the High group recorded
better performances than either the Normal or Poor coordination groups: Shuttle-Run-with-
Object (Mean = 9.58, SD = .97 vs Mean = 11.06, SD = 1.27 and Mean = 11.59, SD = 1.17,
respectively), the Shuttle Run (Mean = 9.40, SD = .97 vs Mean = 10.57, SD = 1.33 and
Mean = 11.27, SD = 1.07, respectively), the Hopping Speed (Mean = 8.11, SD = 1.01 vs
Mean = 10.46, SD = 2.23 and Mean = 11.36, SD = 3.01, respectively), and the Zigzag Run
(Mean = 11.81, SD = .88 vs Mean = 13.24, SD = 1.43 and Mean = 13.72, SD = 1.50,
respectively). The discriminatory power of these tests appears to be relatively good given
their respective potency indices.
In the second discriminant function, the best motor skills for discriminating the Normal
coordination group from the other two coordination groups were: Balance-Eyes-Open and
Dynamic Balance. The means for these motor skills revealed that the Normal group recorded
better performances than the Poor coordination group: Balance-Eyes-Open (Mean = 114.09,
SD = 14.27 vs Mean = 101.10, SD = 20.43), Dynamic Balance (Mean = 20.87, SD = 4.59 vs
Mean = 16.67, SD = 4.68), and recorded poorer performances than the High coordination
group: Balance-Eyes-Open (Mean = 114.09, SD = 14.27 vs Mean = 118.16, SD = 10.30),
and Dynamic Balance (Mean = 20.87, SD = 4.59 vs Mean = 22.18, SD = 5.06). However,
according to their respective potency indices, the discriminatory power of these two motor
skills is relatively low (.08 and .04, respectively).
The jackknife classification procedure examined how well the motor skills could assess
group membership prediction. This classification analysis revealed that 251 (78.44%) of the
participants were classified correctly, when compared with 173 (53.92%) who would be
correctly classified by chance alone. However, using sample proportions as prior
probabilities, it appears that only the Normal coordination group were more likely to be
117
correctly classified (88.8%), with a small proportion of the Normal group being classified as
either Poor (3.6%) or High (7.6%). The High coordination group reported 66.2% correct
classifications, with 30.9% being classified as Normal and 2.9% as Poor. Finally, the Poor
coordination group had 27.6% correct classifications, with 69% being classified as Normal,
and 3.4% as High. Thus, the classification rate of around 78% was achieved, despite a
disproportionate number of participants being classified as Normal.
Finally, an examination of the adolescents who were misclassified revealed the following
(see Table 37). Of the twenty Poor motor coordination individuals who were misclassified,
19 were misclassified as Normal. These individuals, with the exception of the two Shuttle
runs and the 40m Sprint, performed the motor skills to a higher standard than their correctly
classified Poor cohorts, with a significant performance improvement for Balance-Eyes-Open
(p < .001). One Poor motor coordination adolescent was classified as High. This individual
performed all of the motor skills to a higher standard than the correctly classified Poor
cohorts. The 15 Normal motor coordination individuals who were misclassified as High
were able to perform all the motor skills to a higher standard than their correctly classified
Normal cohorts. Significant performance improvements were found for the Shuttle-Run-
With-Object and Shuttle-Run-Without-Object, Hopping Speed, Zigzag Run, Multistage
Fitness Test, Quadrant Jump and Balance-Eyes-Open (p < .001). The seven Normal motor
coordination participants, who were misclassified as Poor except for the Basketball Throw
test, performed all of the motor skills to a lower standard than their correctly classified
Normal cohorts; but with a significant performance decrement for the Balance-Eyes-Open
test (p < .002). For the 21 High motor coordination individuals who were misclassified as
Normal, except for the Balance-Eyes-Open test, motor skills were performed to a lower
standard than their correctly classified High cohorts. Significant performance decrements
were found for the Shuttle Run-With-Object, Shuttle-Run-Without-Object, Basketball
Throw and Vertical Jump (ps < .003). The two High motor coordination individuals who
were misclassified as Poor, with the exception of the two Shuttle Run skills, the Zigzag Run
and the Basketball Throw, motor skills were performed to a lower standard than their
correctly classified High cohorts, with a significant performance decrement for the Balance-
Eyes-Closed (p < .002).
118
Table 37. Profiling Correctly Classified and Misclassified Observations in the Three-
Group Discriminant Analysis for All Participants.
Mean Scores t test
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
Poor
(n = 9)
N
(n = 19)
H#
(n = 1)
N
H
N
H
N
H
Shuttle Run With Object a 11.62 11.65 10.05 -0.03 1.57 .06 NA .949 NA
Shuttle Run 11.53 11.13 10.30 0.40 1.23 .94 NA .358 NA
Hopping Speed a 13.38 10.62 9.03 2.76 4.35 2.42 NA .023 NA
Zigzag Run 14.09 13.67 10.63 0.42 3.46 .92 NA .368 NA
Basketball Throw a 3.99 4.31 6.00 -0.32 -2.01 -1.19 NA .246 NA
Balance Eyes Closed a 23.44 39.11 32.00 -15.67 -8.56 -2.06 NA .052 NA
40m Sprint 8.86 8.69 8.19 0.17 0.67 .37 NA .717 NA
Multistage Fitness Test 10.89 12.68 8.00 -1.79 2.89 -1.10 NA .281 NA
Hopping-in-Square 39.33 45.74 56.00 -6.41 -16.67 -1.53 NA .138 NA
Quadrant Jump 23.00 26.45 36.50 -3.45 -13.50 -1.13 NA .269 NA
Vertical Jump 21.56 23.42 27.00 -1.86 -5.44 -.61 NA .544 NA
Balance Eyes Open a 75.22 113.00 108.00 -37.78 -32.78 -8.81 NA .001 NA
Dynamic Balance a 14.56 17.58 21.00 -3.02 -6.44 -1.64 NA .113 NA
Normal
(n = 201)
P
(n = 7)
H
(n = 15)
P
H
P
H
P
H
Shuttle Run With Object a 11.11 11.54 10.11 -.43 1.00 -.87 5.92 .384 .001
Shuttle Run 10.70 11.13 9.64 -.43 1.06 -1.15 6.56 .253 .001
Hopping Speed a 10.56 12.37 7.78 -1.81 2.78 -2.15 12.95 .033 .001
Zigzag Run 13.26 14.11 11.78 -.85 1.48 -1.60 4.03 .111 .001
Basketball Throw a 4.59 4.63 5.19 -.04 -.60 -.08 -3.24 .938 .004
Balance Eyes Closed a 51.29 23.43 77.07 27.86 -25.78 2.83 2.41 .018 .029
119
Table 37 continued.
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
P H P H P H P H
40m Sprint 8.23 9.00 7.35 -.77 .88 -1.61 2.72 .109 .007
Multistage Fitness Test 12.42 9.71 17.87 2.71 -5.45 1.62 4.55 .106 .001
Hopping-in-Square 47.03 41.29 54.27 5.74 -7.24 1.64 -1.92 .106 .074
Quadrant Jump 27.34 24.21 34.60 3.13 -7.26 1.28 -4.24 .200 .001
Vertical Jump 25.54 22.86 26.40 2.68 -.86 .94 -.81 .347 .428
Balance Eyes Open a 115.59 59.43 119.47 56.16 -3.88 5.85 -4.42 .001 .001
Dynamic Balance a 20.75 18.43 23.53 2.32 -2.78 1.37 -1.77 .173 .097
High
(n = 45)
P
(n = 2)
N
(n = 21)
P
N
P
N
P
N
Shuttle Run With Object a 9.26 8.74 10.37 .52 -1.11 1.17 -4.12 .248 .001
Shuttle Run 9.11 8.76 10.08 .35 -.97 1.17 -4.10 .247 .001
Hopping Speed a 7.88 8.01 8.60 -.13 -.72 -.21 -2.79 .835 .007
Zigzag Run 11.68 11.61 12.10 .07 -.42 .12 -1.78 .905 .080
Basketball Throw a 5.98 6.05 4.55 -.07 1.43 -.07 6.47 .942 .001
Balance Eyes Closed a 80.00 10.50 62.57 69.50 17.43 3.64 2.32 .001 .023
40m Sprint 7.32 8.23 7.95 -.91 -.63 -2.00 -3.27 .051 .002
Multistage Fitness Test 15.02 13.50 13.95 1.52 1.07 .40 .78 .694 .436
Hopping-in-Square 50.49 44.50 50.95 5.99 -.46 .84 -.21 .406 .834
Quadrant Jump 31.49 29.25 30.81 2.24 .68 .42 .34 .677 .734
Vertical Jump 29.36 23.50 23.43 5.86 5.93 1.28 3.68 .207 .001
Balance Eyes Open a 119.87 60.50 120.00 59.37 -.13 5.65 -.68 .111 .499
Dynamic Balance a 22.00 18.00 22.95 4.00 -.95 1.15 -.71 .254 .482
Note. a =Variables included in the stepwise estimation. P = Poor, N = Normal, H = High. NA
= Not available. A Bonferroni correction adjusted the p value to 0038. Significant
differences are in bold. #, raw scores presented for this individual.
120
6.3. DISCUSSION – ALL PARTICIPANTS
The stepwise estimation revealed six motor skills that could maximally separate the three
motor coordination groups – Shuttle-Run-with-Object, Balance-Eyes-Open, Basketball
Throw, Balance-Eyes-Closed, Hopping Speed, and the Dynamic Balance. Although this is
the best set of motor skills reported, the discriminant functions were also examined to find
where discrimination would occur if all 13 motor skills were included in the model. An
examination of the discriminant functions revealed two motor skills for further consideration
– Shuttle Run and Zigzag Run.
Of the two discriminant functions derived from the analysis, only the first was considered
given the magnitude of the canonical structure coefficients, the potency indices and the
canonical correlations. The first function indicated the motor skills that can maximally
separate the High motor coordination group from the Normal and Poor coordination groups.
Thus, when considering all of the motor skills as a package, the Shuttle-Run-with-Object,
the Shuttle Run, Hopping Speed and Zigzag Run form the best set to separate the High
motor coordination group from the other two. The mean performances of the three
coordination groups on these four motor skills indicated that the High motor coordination
group consistently outperformed the other two groups. Despite the second discriminant
function reporting that two motor skills best discriminated the Normal motor coordination
group from the other two, the function properties were too small for consideration.
When assessing the fit of the discriminant model, the predictive accuracy level of the
discriminant functions was examined. Using jackknife classification, the functions were
reasonably able to classify the adolescents. Specifically, the hit ratio was 78%, which is
considerably higher than the 54% who would be correctly classified by chance alone. The
Normal group had the best correct classification hit ratio with 89% of Normals being
classified correctly, with 4% classified as Poor and 7% classified as High. The High group
had a correct classification hit ratio of 66%, with 31% being classified as Normal and 3% as
Poor. Finally, the Poor coordination group only had a correct hit ratio of 27%. The
remaining Poor motor coordination individuals were either misclassified as Normal (69%) or
High (3%). What is noteworthy in these findings is the disproportionate number of cases
being classified as Normal. For instance, almost a third of the High coordination group, and
over two-thirds of the Poor coordination group, were misclassified as Normal. Hence,
several subjects who performed the MAND motor skills quite poorly, and were subsequently
categorised as Poor in basic motor coordination; were able to perform the AIS+BMC motor
skills at a higher level than their correctly classified cohorts. Conversely, there were
121
individuals who performed the MAND motor skills very well and were subsequently
categorised as High in basic motor coordination. But, over a third of them performed the
AIS+BMC skills at a level lower than their correctly classified cohorts.
Examining the misclassifications supports such a view. The Poor motor coordination
individuals who were misclassified as Normal, as a group, performed the motor skills to a
higher standard than their correctly classified Poor cohorts; with the exception of the two
Shuttle runs and the 40m Sprint. In addition, they held the Balance-Eyes-Open motor skill
significantly longer than their correctly classified Poor cohorts. The only other misclassified
Poor motor coordination adolescent was classified as High. This individual performed all
motor skills to a higher standard than the correctly classified Poor cohorts. The Normal
motor coordination individuals who were misclassified as High, were able to perform all of
the motor skills to a higher standard than their correctly classified Normal cohorts.
Significant performance improvements were found for the Shuttle-Run-With and Shuttle-
Run-Without-Object, Hopping Speed, Zigzag Run, Multistage Fitness Test, Quadrant Jump
and Balance-Eyes-Open. However, the Normal motor coordination individuals, who were
misclassified as Poor with the exception of the Basketball Throw test, performed all of the
motor skills to a lower standard than their correctly classified Normal cohorts; but with a
significant performance decrement for the Balance-Eyes-Open. Finally, for the High motor
coordination individuals, who were misclassified as Normal, except in the Balance-Eyes-
Open test, performed the motor skills to a lower standard than their correctly classified High
cohorts. There were significant performance decrements for the Shuttle-Run-With-Object,
Shuttle-Run-Without-Object, Basketball Throw and Vertical Jump. The High motor
coordination individuals, who were misclassified as Poor except for the two Shuttle Run
motor skills, Zigzag Run and Basketball Throw; performed the motor skills to a standard
lower than their correctly classified High cohorts. However, there was a significant
performance decrement for the Balance-Eyes-Closed. Thus, the misclassifications found
here make sense in terms of performance. Those individuals misclassified to a level higher
generally performed the AIS+BMC motor skills to a higher level than their correctly
classified cohorts, and those individuals misclassified to a level lower generally performed
the AIS+BMC motor skills to a lower level than their correctly classified cohorts.
These findings are an important reminder about individual differences in motor skill
performance and that performance in specific types of motor skills does not necessarily
translate to similar levels of performance in different types of motor skills. Indeed,
practitioners need to be careful about accepting performance scores at face value for a
particular set of motor skills. This is especially so if these motor skills are designed for a
122
different purpose from that for which they are being used. The MAND categorised
adolescents into three motor coordination groups which could be at odds with its original
intention. The MAND is a valid and reliable diagnostic tool for neuromuscular development
(McCarron, 1982). The motor skills within the MAND are more suited to a basic level
recognition of motor problems. It was used here because it was believed that these skills also
provide a good basic assessment of motor coordination, or fine and gross motor skills
(McCarron, 1982). However, there were a large number of misclassifications for both the
Poor and High motor coordination groups. Perhaps some individuals who did poorly on the
MAND motor skills, and very well in the AIS+BMC motor skills, found the AIS+BMC
skills easier to perform. Also, the MAND motor skills might not have been as stimulating to
some individuals, and this reflected in their MAND performances, but not in the AIS+BMC
performances. Conversely, some individuals performed the MAND motor skills to a very
high standard which was not translated across to their AIS+BMC performances. The reasons
for such large misclassifications in the Poor and High coordination groups remains unclear,
and awaits future investigation.
6.4. GROUP CLASSIFICATION BASED ON THE MOTORIC ‘g’
From the higher-order factor analyses of the AIS+BMC for all participants, individual factor
scores on the ‘g´ factor were derived. The participants were then ranked from high to low in
terms of their ‘g´. Following McCarron’s (1982) method in using the standard deviation as
the cut-off point for determining the groups, the participants were then categorised into three
motor ability groups. Those individuals with factor scores falling 1 standard deviation below
the mean were in the Low motor ability group, and individuals falling between ± one
standard deviation from the mean were in the Normal motor ability group. Finally, those
participants with factor scores falling 1 standard deviation above the mean were in the High
motor ability group. Thus, participants who scored in the lowest 10% of ‘g’ were categorised
as having Poor levels of motor ability. Participants who scored between 11% and 89% of ‘g’
were classified as having Normal levels of motor ability, and the top 10% of ‘g’ were
considered to have High levels of motor ability.
The same 320 participants were again used in the following discriminant analysis due to
missing data (see Table 38 for the breakdown according to age and gender). The missing
data appeared to be randomly scattered throughout the groups and predictors.
123
Table 38. Numbers & Percentages of Participants in the Three Motor Ability Groups.
Age
(years)
Low Normal High
Boys Girls Boys Girls Boys Girls Total %
12 3 5 48 29 3 3 91 28.44
13 3 6 44 42 3 6 104 32.50
14 3 9 33 29 3 8 85 26.56
15 2 1 11 20 3 3 40 12.50
TOTAL 11 21 136 120 12 20 320 100
It was intended to perform discriminant analysis separately on the boys and the girls.
However, owing to both the Low and High motor ability groups for the boys failing to meet
the recommended minimum 20 cases per group (Hair et al., 1998), only the full sample
results are presented here. This left three motor ability groups for discriminant analysis (32
Low motor ability, 256 Normal motor ability and 34 High motor ability). To remove the
possible confounding effect of chronological age and gender differences, the raw scores of
the AIS and BMC motor skills were standardised within gender-by-age classification. They
were then transformed to T-scores based on means and standard deviations, for each gender
and age group. These normalised T-scores then were used in the discriminant analysis. To
see the results of a discriminant analysis performed separately on the adolescent girls, refer
to APPENDIX H on the CD
124
6.5. RESULTS – ALL PARTICIPANTS
The results of the stepwise discriminant analysis revealed that 10 of the motor skills entered
into the discriminant function. However, the Shuttle Run test that entered on the third step
was subsequently removed on step 10, leaving nine motor skills for consideration.
Specifically, Dynamic Balance entered on the first step Wilks Lambda = .72, F(2, 317) =
62.82, p < .001, the Balance-Eyes-Open on the second step Wilks Lambda = .57, F(4, 632) =
52.14, p < .001, the Shuttle Run on the third step Wilks Lambda = .46, F(6, 630) = 49.10, p <
.001, the Quadrant Jump on the fourth step Wilks Lambda = .42, F(8,628) = 42.15, p < .001,
the Hopping-in-Square on the fifth step Wilks Lambda = .39, F(10,626) = 37.80, p < .001,
the Balance-Eyes-Closed on the sixth step Wilks Lambda = .37, F(12, 624) = 33.53, p <
.001, the Hopping Speed on the seventh step Wilks Lambda = .35, F(14, 622) = 30.28, p <
.001, the 40m Sprint on the eighth step Wilks Lambda = .34, F(16, 620) = 27.36, p < .001,
the Basketball Throw on the ninth step Wilks Lambda = .33, F(18, 618) = 25.04, p < .001,
the Shuttle Run was removed on the tenth step Wilks Lambda = .34, F(16, 620) = 27.71, p <
.001, and the Vertical Jump entered on the eleventh step Wilks Lambda = .33, F(18, 618) =
25.31, p < .001.
Two canonical discriminant functions were calculated (see Table 39). The first discriminant
function produced a Wilks Lambda = .33, with a Chi-square (18) = 345.75, p < .001, and the
second produced a Wilks Lambda = .89, with a Chi-square (8) = 38.24, p < .001. The
canonical R2s for the two discriminant functions was .79 for the first function and .34 for the
second function. The two functions accounted for about 93% and 7%, respectively, of the
between-group variability. An examination of the unstandardised canonical discriminant
functions evaluated at group means reveals that the first function maximally separates the
High motor ability group from Poor motor ability group, with the Normal group in between.
The second discriminant function maximally separates the Normal motor ability group from
both the High and Poor motor ability groups.
An examination of the structure correlations for the discriminant analysis did not reveal
additional motor skills that had a substantial effect on discriminating between the three
coordination groups beyond that indicated by the stepwise estimation. Additionally, for the
first discriminant function, none of the motor skill tests exceeded the r cut-off point of .50.
125
Table 39. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canonical Correlations, Eigenvalues and Group Centroids for the
Three Motor Ability Groups.
Discriminant Function
First Second
SW Value PI SW Value PI
Dynamic Balance .31 .49 .22 -.09 .08 .00
Hopping Speed .31 .47 .20 -.35 -.16 .00
Quadrant Jump .38 .44 .18 .14 .20 .00
40m Sprint .19 .41 .16 .33 .19 .00
Hopping-in-Square .31 .40 .15 .38 .38 .01
Balance Eyes Closed .26 .36 .12 .38 .20 .00
Shuttle Run NI .35 .11 NI .04 .00
Zigzag Run NI .32 .10 NI .06 .00
Shuttle Run With Object NI .31 .09 NI .02 .00
Basketball Throw .18 .26 .06 .28 .26 .00
Vertical Jump .17 .25 .06 -.27 -.15 .00
Multistage Fitness Test NI .17 .03 NI .03 .00
Balance Eyes Open .40 .38 .13 -.77 -.69 .03
Canonical Correlation .79 .34
Eigenvalue 1.67 .13
Group Centroids
Poor -3.00 .68
Normal .03 -.18
High 2.74 .76
Note. SW: Standardised weights. NI: Not included in the stepwise solution. Value:
Structure correlations with correlations greater than .50 in bold. PI: Potency Index.
However, the Dynamic Balance test was almost strong enough (r = .49) to discriminate the
High motor ability group from both the Normal and Poor motor ability groups. An
examination of the mean Dynamic Balance performances for the ability groups revealed that
the High motor ability group had longer balance times (Mean = 25.69, SD = 4.34) than the
126
Normal group (Mean = 20.93, SD = 4.20) and the Poor group (Mean = 14.56, SD = 4.05).
For the second discriminant function, Balance-Eyes-Open appeared to discriminate the
Normal motor ability group (Mean = 115.48, SD = 11.76) from both the High (Mean =
120.00, SD = 0.00) and Poor motor ability groups (Mean = 93.94, SD = 25.48). However,
the relative potency indices for the motor skills were poor for both functions.
The jackknife classification analysis revealed that 293 (91.6%) of the participants were
classified correctly, compared with 211.2 (66%) who would be correctly classified by
chance alone. However, using sample proportions as prior probabilities, it appears that the
Normal motor ability group as more likely to be correctly classified (96%) with 246
Normals classified correctly as Normal. Of the remaining 10 individuals, 5 were
misclassified in the Poor motor ability group (2%) and 5 were misclassified in the High
motor ability group (2%). Twenty-four of the High motor ability individuals were classified
correctly (75%), with the remaining eight individuals misclassified as Normal (25%). For
the Poor motor ability group, 23 were classified correctly (72%) and the remaining 9 were
misclassified as Normal (18%). Thus, the classification rate of around 92% was achieved
despite a disproportionate number of individuals misclassified as Normal.
Finally, an examination of those adolescents who were misclassified revealed that all eight
misclassified Poor motor ability adolescents were misclassified as Normal (see Table 40).
With the exception of the Dynamic Balance, 40m Sprint, Shuttle Run, Shuttle-Run-with-
Object, Zigzag Run and the Multistage Fitness Test; the misclassified adolescents performed
the motor skills to a higher standard than their correctly classified Poor ability cohorts; with
a significant performance improvement for the Balance-Eyes-Open (p < .001). The three
Normal motor ability individuals, who were misclassified as High except for the Shuttle
Run, Shuttle Run with Object and Multistage Fitness Test; were able to perform all motor
skills to a higher standard than their correctly classified Normal cohorts. But, significant
performance improvements were found for the Hopping-in-Square motor skill (p < .001).
The four Normal motor ability individuals, who were misclassified as Poor except for
Hopping Speed, Quadrant Jump, Shuttle Run, Shuttle-Run-With-Object, Zigzag Run,
Basketball Throw and Multistage Fitness Test; performed all of the motor skills to a lower
standard than their correctly classified Normal cohorts. All of the misclassified High motor
ability adolescents were misclassified as Normal. With the exception of Hopping Speed,
40m Sprint, Shuttle Run, Shuttle-Run-with-Object, Zigzag Run and the Multistage Fitness
Test; these individuals performed the motor skills to a lower standard than their correctly
classified High cohorts.
127
Table 40. Profiling Correctly Classified and Misclassified Observations in the Three-
Group Discriminant Analysis for All Participants.
Mean Scores t test
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
Poor
(n = 24)
N
(n = 8)
H
(n = 0)
N
H
N
H
N
H
Dynamic Balance a 14.83 13.75 - 1.08 - .65 - .521 -
Hopping Speed a 14.01 12.32 - 1.69 - 1.36 - .184 -
Quadrant Jump a 20.52 24.25 - -3.73 - -2.05 - .049 -
40m Sprint a 9.07 9.54 - -0.47 - -1.04 - .305 -
Hopping-in-Square a 38.58 39.13 - -0.54 - -.15 - .885 -
Balance Eyes Closed a 27.04 27.63 - -0.58 - -.09 - .932 -
Shuttle Run 11.77 12.15 - -0.37 - -.83 - .415 -
Zigzag Run 14.55 14.95 - -0.40 - -.80 - .429 -
Shuttle Run With Object 12.06 12.55 - -0.49 - -.83 - .415 -
Basketball Throw a 4.18 4.29 - -0.11 - -.23 - .822 -
Vertical Jump a 19.08 23.63 - -4.54 - -1.03 - .335 -
Multistage Fitness Test 9.75 8.13 - 1.63 - 1.37 - .181 -
Balance Eyes Open a 86.46 116.38 - -29.92 - -5.48 - .001 -
Normal
(n = 249)
P
(n = 4)
H
(n = 3)
-
P
H
t-value
P
H P H
Dynamic Balance a 20.90 19.00 26.33 1.90 -5.43 .90 -2.23 .367 .026
Hopping Speed a 9.90 9.46 8.16 0.44 1.73 .46 1.59 .645 .112
Quadrant Jump a 27.96 28.38 37.00 -0.42 -9.04 -.13 -2.51 .893 .013
40m Sprint a 8.11 8.12 7.38 -0.01 0.73 -.01 1.10 .991 .274
Hopping-in-Square a 47.34 43.00 62.33 4.34 -15.00 1.02 -3.06 .308 .002
Balance Eyes Closed a 54.12 19.75 81.33 34.37 -27.21 ` 5.94 -1.50 .005 .134
Shuttle Run 10.40 9.41 9.47 0.99 0.93 2.03 1.65 .043 .101
128
Table 40 continued.
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
P H P H P H P H
Zigzag Run 12.92 12.64 11.71 0.29 1.21 .43 1.57 .668 .119
Shuttle Run With Object 10.75 9.45 10.95 1.30 -0.20 2.10 -.27 .037 .785
Basketball Throw a 4.73 6.35 5.87 -1.62 -1.13 -2.81 -1.70 .005 .090
Vertical Jump a 25.96 23.50 27.67 2.46 -1.71 .70 -.43 .482 .670
Multistage Fitness Test 12.93 15.00 11.33 -2.07 1.60 -.96 .64 .340 .523
Balance Eyes Open a 116.11 72.75 120.00 43.36 -3.89 8.17 -.65 .001 .519
High
(n = 24)
P
(n = 0)
N
(n = 8)
P
N
P
N
P
N
Dynamic Balance a 26.21 - 24.13 - 2.08 - 1.18 - .246
Hopping Speed a 7.88 - 7.23 - .65 - 1.95 - .061
Quadrant Jump a 38.10 - 33.31 - 4.79 - 1.83 - .077
40m Sprint a 7.12 - 6.97 - .15 - .57 - .573
Hopping-in-Square a 60.46 - 53.88 - 6.58 - 1.69 - .101
Balance Eyes Closed a 95.08 - 74.75 - 20.33 - 1.76 - .089
Shuttle Run 9.48 - 9.30 - .19 - .61 - .544
Zigzag Run 11.49 - 11.63 - -.14 - -.37 - .716
Shuttle Run With Object 9.94 - 9.51 - .43 - 1.43 - .164
Basketball Throw a 5.73 - 5.05 - .69 - 1.38 - .177
Vertical Jump a 29.75 - 26.88 - 2.88 - 1.05 - .303
Multistage Fitness Test 17.00 - 19.25 - -2.25 - -0.88 - .387
Balance Eyes Open a 120.00 - 120.00 - 0.00 - NA - NA
Note. a =Variables included in the stepwise estimation. P = Poor, N = Normal, H = High.
NA = Not available. A Bonferroni correction was used to adjust the .05 significance value to
.0038. Significant differences are in bold.
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6.6. DISCUSSION – ALL PARTICIPANTS
The stepwise estimation revealed nine motor skills that could maximally separate the three
motor ability groups - Dynamic Balance, Balance-Eyes-Closed, Quadrant Jump, Hopping-
in-Square, Balance-Eyes-Closed, Hopping Speed, 40m Sprint, Basketball Throw and
Vertical Jump. Although this is the best set of motor skills reported, the discriminant
functions were also examined to see where discrimination would occur if all 13 motor skills
were included in the model. An examination of the discriminant functions did not reveal
additional motor skills that had a substantial effect on discriminating between the three
motor ability groups beyond that indicated by the stepwise estimation.
Two discriminant functions were derived from the analysis and, given the magnitude of the
canonical structure coefficients, the potency indices and the canonical correlations for both
functions, the first function was deemed slightly more important than the second. The first
function indicated the motor skills that can maximally separate the High coordination group
from the Poor group, with the Normal group in between. However, when one considers all
of the motor skills as a package, none stood out. The Dynamic Balance test was almost
strong enough (r = .49) to discriminate the High motor ability group from both the Normal
and Poor groups. The mean performances of the three motor ability groups on this motor
skill indicated that the High ability group outperformed the other groups by exhibiting a
longer balance time. The second discriminant function indicated that the best motor skill for
discriminating the Normal motor ability group from the other two groups was Balance-Eyes-
Open. The performance mean for this motor skill revealed that the Normal group recorded
better performances than the Poor ability group, and lower performances than the High
motor ability group. However, according to its potency index, the discriminatory power of
the Balance-Eyes-Open is low.
When assessing the fit of the discriminant model, the predictive accuracy level of the
discriminant functions was examined. Using jackknife classification, the functions were
reasonable in their ability to classify the adolescents. Specifically, the hit ratio was 92%,
which is considerably higher than the 66% who would be correctly classified by chance
alone. The Normal ability group had the best correct classification hit ratio with 96% of
Normals being classified correctly, with 2% misclassified as Poor and 2% misclassified as
High. The High motor ability group had a correct classification hit ratio of 75%, with 25%
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being misclassified as Normal. Finally, the Poor motor ability group had a correct hit ratio of
72%. The remaining Poor motor ability individuals were misclassified as Normal (28%).
Once again, a disproportionate number of cases were classified as Normal. However, the
percentages were not as high as found for the MAND framework. Using GMA scores to
create the ability groups, only 25% of the High motor ability group, and 28% of the Poor
motor ability group were misclassified as Normal. Thus, some adolescents who, when based
on their ‘g’ scores, were classified as having Poor motor ability performed some of the
AIS+BMC motor skills to a performance level higher than their correctly classified cohorts.
Conversely, adolescents who, based on their ‘g’ scores, were classified as having High
motor ability, performed the AIS+BMC motor skills at a performance level lower than their
correctly classified cohorts.
An examination of the misclassifications supports such a view. For those Poor motor ability
individuals who were misclassified as Normal, with the exception of Dynamic Balance, 40m
Sprint, Shuttle Run, Shuttle-Run-with-Object, Zigzag Run and Multistage Fitness Test,
performed the motor skills at a higher standard than their correctly classified Poor ability
cohorts. Also, they performed the Balance-Eyes-Open motor skill significantly longer than
their correctly classified Poor ability cohorts. The Normal motor ability individuals who
were misclassified as High; were able, with the exception of the Shuttle Run, Shuttle-Run-
with-Object and Multistage Fitness Tests, to perform all of the motor skills at a higher
standard than their correctly classified Normal cohorts; but with significant performance
improvements for the Hopping-in-Square motor skill (p < .001). The Normal motor ability
individuals who were misclassified as Poor, with the exception of the Hopping Speed,
Quadrant Jump, Shuttle Run, Shuttle-Run-with-Object, Zigzag Run, Basketball Throw and
Multistage Fitness Test; performed all of the motor skills at a lower standard than their
correctly classified Normal cohorts. Finally, the High motor ability individuals who were
misclassified as Normal, with the exception of Hopping Speed, 40m Sprint, Shuttle Run,
Shuttle-Run-with-Object, Zigzag Run and Multistage Fitness Test; performed the motor
skills to a lower standard than their correctly classified High cohorts. Again, it appears that
the misclassifications found here make sense in terms of performance. Those misclassified
to a higher level generally performed the AIS+BMC motor skills at a higher level than their
correctly classified cohorts, and those misclassified to a level lower generally performed the
AIS+BMC motor skills to a lower level than their correctly classified cohorts.
These findings are intriguing given that the ‘g’ score was derived from the performances of
the motor skills making up the AIS+BMC. The specific reasons as to why there were
misclassifications for the Poor and High motor ability groups are unclear. It is possible that
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some adolescents whose ‘g’ scores placed them in either the Poor or High ability groups did
not translate those scores across to the AIS+BMC performances as derived by the
discriminant functions. However, it is more likely that the best set of motor skills derived by
the discriminant analysis are quite good at discriminating between the three ability groups,
but fall short with particular individuals. Thus, it is important for practitioners to examine
the nature of the misclassifications individually to determine the nature of these individuals.
6.7. GENERAL DISCUSSION
Previous research has focused on discriminating athletes according to sport characteristics
derived from physical tests (Leone et al., 2002; Proctor & Ruhling, 1981), anthropometric
tests (Housh, Thorland, Johnson & Tharp, 1984a; Housh, Thorland, Johnson, Tharp & Cisar,
1984b; Housh et al., 1984c; Pienaar et al., 1998) and physiological characteristics (Smith &
Thomas, 1991). Those studies showed that elite athletes have different characteristics based
on the type of sport in which they are involved. However, those studies also suggest that the
different characteristics of participants were further developed and influenced by intensive
training. The focus of the current research was to find a reduced set of motor skills from the
AIS+BMC motor skill set that could discriminate between three motor coordination/ability
groups and assess classification into these groups. Initially, the MAND (McCarron, 1982)
was used to derive three motor coordination groups. The MAND is a valid and reliable
diagnostic tool for neuromuscular development, and the motor skills within the MAND are
suited to a basic level recognition of motor problems within an individual, and believed to be
a good basic assessment of basic motor coordination. Then, another approach used ‘g’ scores
derived from the pairing of the AIS and BMC motor skill instruments. Specifically,
individual factor scores from the higher-order factor analysis were identified for each
participant, and these were used to categorise the adolescents into three motor ability groups.
When utilising the MAND to categorise the adolescents, six AIS+BMC motor skills were
found to maximally separate the full sample of adolescents into the MAND’s three motor
coordination groups. These motor skills were Shuttle-Run-with-Object, Balance-Eyes-Open,
Basketball Throw, Balance-Eyes-Closed, Hopping Speed and Dynamic Balance. The
discriminant functions provided a reasonable classification hit ratio of 78%. However, the
misclassifications for the High and Poor motor coordination groups were particularly high.
The discriminant analysis of female adolescents revealed three AIS+BMC motor skills that
maximally separated these adolescents into the MAND’s three motor coordination groups.
These motor skills were Balance-Eyes-Closed, Balance-Eyes-Open and Shuttle-Run-with-
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Object. The discriminant functions provided a reasonable classification hit ratio of 71%.
However, misclassifications for High and Poor motor coordination groups were quite high.
When utilising the motoric ‘g’ factor scores to categorise the adolescents, nine AIS+BMC
motor skills were found to maximally separate the full sample of adolescents into the three
motor ability groups. These motor skills were Dynamic Balance, Balance-Eyes-Open,
Quadrant Jump, Hopping-in-Square, Balance-Eyes-Closed, Hopping Speed, 40m Sprint,
Basketball Throw and Vertical Jump. The discriminant functions provided a good
classification hit ratio of 92%. Thus, although the motoric ‘g’ categorisation provided better
classification indices when compared with those reported for the MAND, it suggested that
the sport derived categorisation procedure was more accurate. However, it may not be
strictly correct to use the same 13 motor skills that created the ‘g’ scores, and then try to find
the best sub-set of the same motor skills to reliably discriminate the 3 motor ability groups
derived from those same ‘g’ scores. This was pursued to see if a framework grounded in
sport (i.e., the AIS+BMC) would provide better separation than one grounded in disability
(i.e., the MAND). The results appear to indicate this was so, but the finding needs to be
treated with caution.
Although not as high as with the MAND categorisation, there were still significant
misclassifications for the High and Poor motor ability groups. What should be noted about
the misclassifications is how extensive they were for the Poor and High motor coordination
groups under the MAND framework than when compared with the GMA framework. Why
this is so is unclear. Perhaps ‘g’ does not underlie all skills in the same individual. It was
believed that the MAND assessed basic aspects of motor coordination and could be used to
categorise the adolescents into three motor coordination groups. However, it is more
probable that the MAND better assesses what it was designed to assess, namely,
neuromuscular problems rather than basic motor coordination. In the current sample, the
MAND appears to be assessing aspects of postural control, fine manipulative skills, and
control and amplitude, rather than fine and gross motor skills. A task analysis of the MAND
postural control factor suggests that it is similar to the postural control factor found for the
AIS+BMC. However, the other two MAND factors of fine manipulative skills, and control
and amplitude, are specifically assessing an ability to manipulate objects with the hand/s.
There are no comparative motor skills within the AIS+BMC. In fact, there was little match
between motor skills used to create the MAND groups and the type of motor skills used to
predict group membership. However, in the ‘g’ discriminant analyses, the motor skills used
to create the GMA groups were the same motor skills used to predict group membership.
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Thus, these large discrepancies could also point to a lack of fit between the type of group
(derived from the MAND) and type of motor skills being used to try and predict group
membership. So, the better classification hit ratios for the ‘g’ over the MAND could just be
an artefact of the positive relationship between the type of group and the types of motor
skills predicting group membership. Future research needs to examine this relationship
further by using the AIS+BMC to create motor ability groups; and then use motor skills not
assessed by the AIS+BMC, but are similar in nature, to see how well those motor skills
predict AIS+BMC group membership.
The examination of the misclassifications for both sets of analyses revealed two things. In all
instances where misclassification resulted in individuals being predicted to a lower group, as
a group, these individuals performed a large number of the motor skills to a lesser standard
than their correctly classified cohorts. Conversely, in all instances where misclassification
resulted in individuals being predicted to a higher group, such individuals performed a large
number of the motor skills to a higher standard than their correctly classified cohorts. The
nature of these performance differences within the sub-groups indicates that, for some
adolescents, their performances on specific motor skills were masked by their performances
on other motor skills. Thus, at a global level, it is possible that certain individuals could be
overlooked for further development. Therefore, practitioners need to be aware and cautious
of a single figure of ability and how that figure might represent any individual’s athletic
potential. As can be seen with the misclassified subjects, individual differences in
performing specific motor skills can be masked by a single figure based on their overall
performance.
What was constant in these discriminant analyses was the high correct classification hit ratio
for the Normal groups. Discriminant analysis is quite sensitive to sample size and larger
groups have a disproportionately higher chance of classification (Hair et al., 1998). The
classifications appear to support this view with the Normal group being the largest group
and also recorded the highest correct classification hit ratio. However, the methodology
creating the three groups was considered to be appropriate, and was able to sample the
extreme ends; in particular, the top end of the distribution, using a standardised measurement
approach (McCarron, 1982). Even though this would provide a large middle group, the size
of the groups representing the middle and both ends of this distribution should accurately
reflect findings that would be found in the normal population. It was expected that talented
individuals would be more likely to exist one standard deviation above the mean in motor
skill performances. Thus, this approach better reflected what naturally occurs in the general
population. A proportion of the Normal population could have been selected that, in terms of
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n, would have suited better the statistical requirements of discriminant analysis, but would
not reflect the real world (Hair et al., 1998; Tabachnick & Fidell, 2007). Despite this, it
should be acknowledged that the findings from the discriminant analyses seem to have some
utility in identifying Normal performers given that the correct classification of the Normal
groups was reasonable.
The motor skills that were consistently reported in the stepwise discriminant estimations
were Balance-Eyes-Open and Balance-Eyes-Closed (all four analyses), Dynamic Balance
and Hopping Speed (three analyses), and Quadrant Jump, Hopping-in-Square, Basketball
Throw and Shuttle-Run-with-Object (two analyses). Motor skills assessing static balance,
dynamic balance and postural control are important and these findings support balance as
being important in movement skill proficiency (Burton & Miller, 1998; Proctor & Ruhling,
1981). Balance is a basic motor ability in any physical circumstance (Willgoose, 1961) and
is frequently included in motor ability tests (Burton & Miller, 1998). The discriminative
power of balance found in this study demonstrates that balance ability is an important
fundamental skill (Rarick et al., 1976).
Past research has found young girls to have a slight advantage in balance tasks (Hands &
Larkin, 2004; Roberton, 1984), and that boys performed moderately better after puberty
(Thomas & French, 1985). Unfortunately, this was not examined due to the small cell sizes
for the adolescent boys, and needs to be remedied in future research. However, the findings
here seem to indicate that balance, and other qualities helpful for balance such as postural
control, appear to be important discriminating components for the Malaysian adolescents.
Previous research also has indicated that hopping tasks show strong discriminative power
among the Poorer and Normal spectrum of coordination in children (Henderson & Sugden,
1992; Johnston, Crawford, Short, Smyth & Moller, 1987; Larkin & Revie, 1994). The
hopping tasks (hopping-in-square and hopping speed) maximally separated the motor ability
groups. Finally, research classifying young soccer players into elite and sub-elite groups,
reported one of the best discriminative motor skills was the shuttle run (Reilly et al., 2000).
The present study also recorded a similar finding in the MAND discriminant with the
Shuttle-Run-with-Object motor skill. The basic findings in this study suggest that motor
skills assessing balance and postural control should be included in any mass screening TI
instrument for Malaysians. However, one should be mindful that the AIS+BMC only
assesses the two fundamental movement skills of coordination and balance (Burton &
Miller, 1998). In order to develop a more rounded mass screening instrument, motor skills
assessing other fundamental movement skills need to be included.
135
In summary, the use of the stepwise estimation enabled delineation of the best set of motor
skills. The discriminant functions also were able to classify the adolescents into the motor
ability groups reasonably well for the MAND framework and even better for the ‘g’
framework. However, the classification indices indicated that the analyses seem to assist in
identifying Normal performers rather than High performers. Unfortunately, given the
number of participants and methodology employed to create these motor ability groups, the
discriminant findings could not be cross-validated. Thus, if the same categorisation
methodology is to be used by others, they will need to consider carefully the number of
participants required for cross-validation. The results also highlight the need for practitioners
to take care with how they view both individuals and groups that have been derived from a
single overall performance score or rating. As found in the factor analyses, it is important to
understand what the motor instrument being used is assessing in the population under study,
and to bear that in mind when interpreting group performance.
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CHAPTER 7
SUMMARY, CONCLUSIONS & RECOMMENDATIONS FOR FURTHER STUDY
This research set out to examine the GMA construct and provide initial talent identification
testing of Malaysian adolescents. Development of culturally specific TI assessments and
normative values based on individual motor ability was deemed to be important because
children from different countries have recorded different results on similar test instruments.
Several countries have developed their own TI instruments and achieved excellent sporting
improvements and results. However, the developmental details of these instruments are
rarely published in detail. Thus, it was necessary to investigate the foundations upon which
TI instruments are based for children and adolescents; as talent and skill develop mutually
with motor ability and maturation. The 330 participants, aged 12-15 years, were tested on
the McCarron Assessment of Neuro-Muscular Development (MAND-McCarron, 1982), the
Australian Talent Identification Test (AIS - Australian Sports Commission, 1998); and a
Balance and Movement Coordination Test (BMC) that was developed for the current
research.
7.1. SUMMARY OF RESEARCH
This project had several aims, the first of which was to identify the underlying motor
abilities of the three motor skill instruments used in the current research to understand the
nature of what these instruments were assessing in a Malaysian setting. Secondly, the
research investigated whether a ‘g’ in motor ability could be derived from the combined
AIS+BMC motor skill set of motor skills. The next aim was to examine the motor
performances by 330 Malaysian adolescents on the motor skills from the three motor skill
instruments employed here (i.e., the MAND, AIS and BMC motor skill instruments).
Finally, an examination was undertaken to find a reduced set of motor skills from the
combined AIS+BMC motor skills set that reliably discriminated and classified Malaysian
adolescents into three motor ability groups. Categorisation into these motor ability groups
took two forms. The first utilised performances on the MAND resulting in adolescents being
categorised into Poor, Normal and High motor coordination groups. The second
categorisation method utilised an individual’s ‘g’ factor score to place them into Low,
Normal and High levels of motor ability. Table 41 presents a summary of the findings from
the studies reported here.
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Table 41. Summary Table of Findings.
Factor Analyses
Component 1 Component 2 Component 3
All Participants:
MAND
‘Postural Control’
Heel-toe
Balance MAND
Finger-nose-finger
Jumping
‘Bimanual Dexterity
Beads on rod
Beads in box
Nut and bolt
‘Muscle Power
Grip strength
Finger tapping
All Participants:
AIS
‘Explosive power’
40m sprint
Multistage fitness test
Vertical jump
Basketball throw
.
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Table 41 continued.
Component 1 Component 2 Component 3
All Participants:
BMC
‘Movement Coordination’
Shuttle Run
Shuttle Run With Object
Hopping Speed
Zigzag Run
‘Postural Control’
Dynamic Balance
Hopping-In-Square
Quadrant Jump
‘Static Balance’
One-foot Balance With
Eyes Open
One-foot Balance With
Eyes Closed
‘g’ Higher-order Analyses
Component 1 Component 2 Component 3 Component 4 Higher-order Factor
Boys:
AIS+BMC
‘Movement Coordination’
Shuttle Run
Shuttle Run With Object
Hopping Speed
Zigzag Run
‘Kinesthetic Integration’
One-Foot Balance With
Eyes Open
Multistage Fitness Test
‘Postural Control’
Dynamic Balance
Quadrant Jump
Hopping-In-Square
‘Explosive power’
Vertical Jump
Basketball Throw
‘g’
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Table 41 continued.
Component 1 Component 2 Component 3 Higher-order Factor
Girls:
AIS+BMC
‘Movement Coordination’
Shuttle Run
Shuttle Run With Object
40m Sprint
Hopping Speed
Zigzag Run
Multistage Fitness Test
Vertical Jump
‘Postural Control’
Hopping-In-Square
Dynamic Balance
Basketball Throw
‘Static Balance’
One-foot Balance With
Eyes Open
One-foot Balance With
Eyes Closed
‘g’
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Table 41 continued.
Descriptives/ANOVAs
AIS MAND BMC
Anthropometry
The boys and girls height, weight, and BMI
increased with age.
AIS - Motor Performance
Basketball Throw: In general the older boys
threw the basketball further than any of the
groups, in particular the two eldest boy
groups.
Vertical Jump: Overall the boys jumped
higher than the girls. The 15-year-olds
jumped higher than any oth the othe age
groups.
Fine Motor Tests
Beads In Box: In general the girls
outperformed the boys.
Beads On Rod: The girls outperformed the
boys.
Finger Tapping: The 13-year-olds
outperformed the 12- and 14-year-olds.
Body balance tests
Dynamic Balance: The jumping performance
for the boys was better than the girls.
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Table 41 continued.
AIS cont. MAND cont. BMC cont.
40m Sprint: Overall the boys were faster
than the girls.
Gross Motor Tests
Grip Strength: In general the older boys and
girls increased with age. The eldest boy
group were particularly strong.
Finger-nose-finger: The boys and girls had
varied performance across the age groups.
Of note was the age group of 12 for both
boys and girls who performed particularly
well.
Movement coordination tests
Shuttle Run: The times for the boys were better
than the girls.
Hopping Speed: The girls improved with an
increase in their age. The opposite trend
appeared to be the case for the boys. However,
the B12s were faster than the G12s.
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Table 41 continued.
MAND cont. BMC cont.
Jumping: The boys recorded better jumping
performance compared to the girls. The 14-
year-olds recorded better jumping
performance compared to the 12-year-olds.
Heel-toe: The boys had better and more
stable performances than the girls.
Shuttle Run with Object: The times for the boys
were better than the girls.
Quadrant Jump: The number of correct jumps
for the girls increased with age and that from
age 13 the girls outperformed the boys.
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Table 41 continued.
Discriminant Analyses
MAND
Framework
Stepwise Findings Canonical R2 Structure Findings Classification Percentages Misclassified As (n)
All Participants:
AIS+BMC
- Shuttle Run
With Object
- Balance Eyes
Open
- Basketball
Throw
- Balance Eyes
Closed
- Hopping Speed
- Dynamic
Balance
First: .41
Second: .09
- Shuttle Run
With Object
- Shuttle Run
- Hopping Speed
- Zigzag Run
Normal: 89%
High: 66%
Poor: 27%
Poor: N = 7; H = 2
Normal: P = 19; H = 21
High: P = 1; N = 15
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Table 41 continued.
‘g’ Framework Stepwise Findings Canonical R2 Structure Findings Classification Misclassified As (n)
All Participants:
AIS+BMC
- Dynamic
Balance
- Balance Eyes
Open
- Quadrant Jump
- Hopping-in-
Square
- Balance Eyes
Closed
- Hopping Speed
- 40m Sprint
- Basketball
Throw
First: .63
Second: .15
No tests strong
enough (i.e., < .50).
Normal: 96%
High: 75%
Low: 72%
Low: N = 5
Normal: L = 9; H = 8
High: N = 5
Note. ‘g’ = motoric ‘g’. First = First Discriminant Function, Second = Second Discriminant Function. MAND Framework - P = Poor Motor
Coordination Group, N = Normal Motor Coordination Group, H = High Motor Coordination Group, ‘g’ Framework - L = Low Motor Ability Group,
N = Normal Motor Ability Group, H = High Motor Ability Group.
145
Initially, it was necessary to identify the underlying constructs of the motor skill instruments
used in the current research. Earlier research acknowledges that different factor constructs
will emerge from different age groups and different levels of motor difficulty (McCarron,
1982; Rarick, Dobbins & Broadhead, 1976). So, it is imperative to understand what that
instrument is assessing, relative to the population under investigation. Factor analysis on
motor skill instruments among all participants demonstrated that the three components
underlying the MAND were postural control, bi-manual dexterity and muscle power. One
component underlying the AIS was labelled explosive power. The three components
underlying the BMC were movement coordination, postural control and static balance. On
the surface it appears that the MAND, AIS and BMC are competent instruments in assessing
motor skills at a basic level. But, when it comes to understanding what it is that these
instruments are measuring from a more general perspective, they need to be carefully
examined since it appears that what they are assessing could change with the population
under investigation. This was borne out in the current research, as the hypothesised factor
structures in each case were not upheld in the Malaysian sample. Thus, Burton and Miller’s
(1998) recommendation to examine the psychometric properties of an instrument appears
warranted.
The current research also investigated the concept of GMA through the use of higher-order
factor analysis. Past research has reported differences between boys and girls in their
performances of fundamental movement skills (Seefeldt & Haubenstricker, 1982; Thomas &
French, 1985; Thomas, Michael & Gallagher, 1994). Despite this, Burton and Miller (1982)
noted that, some research which examined motor ability, typically ignores the possibility of
gender differences at this higher level. Thus, in recognition of these gender differences, the
current research tested for the existence of a ‘g’ separately in boys and girls. The results of
the first-order factor analysis indicated that movement coordination and postural control
were found for both girls and boys, along with some form of balance ability. The type of
balance being assessed was static balance for the girls and a more general form of balance
for the adolescent boys called kinaesthetic integration. The final motor ability that was only
found for the adolescent boys was explosive power. This suggests that, when assessing some
form of motor skill, power appears to be more relevant for adolescent boys. These
differences in the types of motor ability found for the adolescent boys and girls extends
previous research noting gender differences at the motor skill level, by including gender
differences at the motor ability level. Therefore, researchers need to be aware that a motor
skill instrument may be assessing different motor abilities according to the gender being
examined at the time. Despite finding gender differences at motor ability level, the higher-
order factor analysis of the girls and boys first-order components both extracted a single
146
motoric ‘g’. The finding of ‘g’ in this manner supports the hierarchical nature of Burton and
Rodgerson’s (2001) taxonomy – that a ‘g’ in motor ability can be inferred by performance
on either movement skills or movement skill sets. However, the author in this study
considers this ‘g’ to be a ‘g’ that is associated with the motor skills assessed by the
AIS+BMC. It is highly likely that other ‘g’s could be found from different motor skill
instruments. Indeed, by correlating ‘g’s derived from different motor skill instruments could
be an avenue for future research to help clarify the existence of GMA. Finally, whilst the
results of the higher-order factor analysis that were based on these first-order component
structures did not provide definitive evidence for the existence of GMA, neither did it deny
the existence of a motoric ‘g’.
The motor ability test performances by 330 Malaysian adolescents on motor tasks were then
examined, and these varied with age and gender. Participants increased in height, weight and
BMI across gender and age. Results also demonstrated no significant interactions between
gender and age on fine motor skills of the MAND. However, significant interactions
between gender and age were shown on the MANDs gross motor skills of grip strength, and
finger-nose-finger, with varied performances reported for the boys and girls across the age
groups. For the gross motor skills of jumping and heel-and-toe the boys outperformed the
girls. A gender-by-age interaction was also reported for the AIS motor skill of basketball
throw with the older boy and girl age groups throwing further; particularly the 14- and 15-
year-old boys. The boys also outperformed the girls for the AIS motor skills of vertical jump
and 40m sprint. Finally, a significant interaction between gender and age was reported for
the BMC motor skill of hopping speed. This revealed that although boys outperformed girls
at age 12 they deteriorated with an increase in age while the girls improved hopping speed as
they became older. The two movement coordination motor skills of the shuttle run and the
shuttle-run-with-object revealed that the boys outperformed the girls. Finally, the results for
the quadrant jump indicated that the number of correct jumps for the girls increased with age
and that from age 13 the girls outperformed the boys.
The ability of the combined AIS+BMC motor skills to reliably discriminate and classify
Malaysian adolescents into three groups based on their motor performance, was the final
focus of the current research. Initially, the MAND (McCarron, 1982) was used to categorise
the adolescents into three motor coordination groups. Then, the adolescents were categorised
into three motor ability groups by utilising factor scores derived from the motoric ‘g’ found
in the higher-order factor analyses. Using stepwise discriminant estimations, specific
clusters of motor skills were found to maximally separate the groups derived from these two
categorisation methods. However, the motor skills that were consistently reported across
147
both sets of analyses were Balance-Eyes-Open and Balance-Eyes-Closed, Dynamic Balance
and Hopping Speed, and Quadrant Jump, Hopping-in-Square, Basketball Throw and Shuttle-
Run-with-Object. Thus, the motor skills assessing static balance, dynamic balance and
postural control appear to help classify the adolescents. This demonstrates that balance has
an important role in movement skill proficiency (Burton & Miller, 1998; Proctor & Ruhling,
1981) and that the discriminative power of balance found here also demonstrates that
balance ability is an important fundamental motor skill (Rarick et al., 1976).
Finally, the overall classification index was reasonable at around 70% when using
performance on the MAND to categorise the adolescents, and even better at around 90%
using factor scores on the motoric ‘g’ to categorise the adolescents. Despite these positive
findings, there were a significant number of misclassifications. These revealed individuals
being predicted into a lower group having performed a large number of the motor skills to a
lesser standard than their correctly classified cohorts. Conversely, those individuals being
predicted into a higher group performed a large number of the motor skills at a standard
higher than their correctly classified cohorts. Thus, at a global level, it is possible that certain
individuals could be overlooked for further athletic development and this is a concern when
developing a rigorous TI program. Therefore, researchers need to be aware and cautious of a
single figure of ability and how that figure is used as representative of an individual’s
athletic potential. Additionally, it also needs to be acknowledged that the specific
classification findings from the discriminant analyses seem to have some utility in
identifying Normal performers. The results indicated that the correct classification of the
Normal groups was reasonable and, for the High groups, the correct classification was not
impressive. Finally, the correct classification for the Poor/Low groups was quite
unimpressive. Thus, the motor skills found to reliably discriminate the groups appear to be
more relevant to Normal performers, rather than the High performers, which was a focus of
the research.
7.2. LIMITATIONS
Even though the findings reinforce the importance of effective motor skill instrumentation in
TI, there were some limitations inherent in the research that should be acknowledged.
A limitation in the current research was the sample size. Although very good initial
descriptive data were found for this Malaysian sample, the sample itself was considerably
smaller than that examined by the Malaysian Sports Council. This needs to be kept in mind
when comparing the findings from this research to past Malaysian research. In addition, it
was not possible to validate either the factor analyses or the discriminant analyses findings
due to the small sample size. Also, gender could not be examined in great detail and past
148
research has noted gender differences at the motor skill level. Hence, the findings presented
here regarding these analyses need to keep this in mind.
When examining the concept of GMA, only two of the motor skill instruments were used;
namely, the AIS and the BMC. The MAND was ignored. Thus, the motoric ‘g’s found in the
higher-order factor analyses was a ‘g’ associated with the AIS and BMC. The inclusion of
the MAND and other motor skill instruments in future analyses which test for the existence
of GMA, and confirm or deny its existence. There is a need to examine other motor skill
instruments which assess not only the same motor abilities found in the current research, but
also motor skill instruments assessing other aspects of Burton and Miller’s (1998)
foundations of movement skill.
The methodology employed to create the motor performance groups for the discriminant
analyses used a standardised measurement approach (McCarron, 1982). One problem
resulting from this methodology concerns the size of the motor performance groups. Due to
the small number of males in one group, discriminant analysis examining possible gender
differences could not be done. Therefore, the current study could only provide general
information in this regard and gender needs to be considered in future research. The
methodology also created relatively large Normal groups which resulted in a high correct
classification and hit ratio for Normals. Given that discriminant analysis is quite sensitive to
sample size, perhaps future research could use a proportional approach to group sizes in
terms of n. Such an approach would create groups better suited to the statistical requirements
of discriminant analysis, but would not reflect the real world (Hair et al., 1998; Tabachnick
& Fidell, 2007). But, the methodology in the current study was employed because it better
reflected what naturally occurs in the general population.
7.3. STRENGTHS
Despite these limitations, the present research also had some strong points. The study sought
to examine the motor skill performances of Malaysian adolescents in an attempt to find
motor skills that could be used in a basic screening instrument/s to identify athletic talent.
The analyses found a small set of motor skills that appear to be important for inclusion in a
basic screening motor skill instrument for Malaysians. It is clear that motor skills assessing
static balance, dynamic balance and postural control should be included in a mass screening
test. However, only 13 motor skills assessing explosive power, movement coordination,
postural control and static balance were examined. Future investigations need to examine
other motor skill instruments assessing different motor abilities to provide a more rounded
screening instrument.
149
It was also found that the motor skill performances of the adolescents were sufficiently
different from those of other countries to warrant the development of norms specific to
Malaysia. These are important for their performances to be compared to standards that are
meaningful to Malaysians. Given the marked cultural differences in the importance of
physical activity and sport in Malaysia, compared with some Western societies, basing
Malaysian performance standards on Western performance standards is inappropriate.
However, it should be noted that a considerable amount of effort on the part of Malaysians is
needed for them to develop appropriate norms for a wider range of motor skills than those
presented here.
Finally, the concept of GMA was examined and results of the higher-order factor analyses
indicated that a ‘g’ in motor ability exists. The finding of ‘g’ in this manner supports the
hierarchical nature of the Burton and Rodgerson’s (2001) taxonomy. That is, a ‘g’ in motor
ability can be inferred by performance on either movement skills or movement skill sets.
However, the ‘g’ referred to here is associated with the motor skills assessed by the AIS and
BMC motor skill instruments. It is possible that other ‘g’s will result from different motor
skill instruments. Subsequent examination of these ‘g’s will progress research towards
clarifying the existence of GMA.
7.4. CONCLUSIONS
On the basis of the findings in this study it can be concluded that:
1. Gender and age influence motor skill performances among Malaysian adolescents.
2. Malaysian adolescent motor skill performances are different from those of other
countries, based upon comparisons made to USA norms on the MAND test, and with
Australian adolescents on the AIS test percentile scores.
3. The underlying constructs of motor skill instruments used in the current research were
identified for this Malaysian sample.
4. Since some of the motor skill instrument constructs were found to be different from their
original sources, it is necessary to examine the underlying structures of motor skill
instruments if they are being used in populations different from where they were
originally sourced.
5. Higher-order factor analyses of the AIS+BMC instrument found a ‘g’ in motor ability
for the boys and girls.
6. Discriminant function analysis was useful in highlighting a sub-set of the AIS+BMC
motor skills for a mass screening instrument for TI.
150
7.5. RECOMMENDATIONS FOR FURTHER STUDY
1. This was the first time that the MAND instrument was implemented on a Malaysian
sample, and valuable normative data were obtained. However, further research
utilising other motor skill instruments is needed to establish the reliability, validity,
and suitability of these instruments for the Malaysian population.
2. Burton and Miller (1998) have noted 11 foundations of movement skill and two of
these- movement coordination and balance- were examined in the current research.
The current research found that the motor skill instruments used here only assessed
four motor ability components – explosive power, movement coordination, postural
control and static balance. However, a broader range of motor skills assessing other
foundations of movement skill should be examined in order to develop a more
rounded instrument for the mass screening phases of TI.
3. Morphology, perceptual motor skills and psychology are elements that contribute to
skilled athletic performance, and all these elements need to be examined when
identifying talented athletes. A multidimensional testing instrument consisting of
such elements would strengthen the likelihood of identifying talented athletes.
4. The current research tested for a ‘g’ in motor ability. Future research needs to
confirm the ‘g’ associated with the combined AIS+BMC motor skill set and extend
this examination to include other motor skill instruments.
151
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1
APPENDIX A
Summary of Motor Ability Test Batteries
The first reported of motor ability test battery was in 1912. Since then, many researches have been
conducted in the area of motor ability instruments from different perspectives of research
methodology and data analysis. Around 1930 to 1940, research activities in developing motor ability
test batteries were increased based on the strength of the general motor ability concept. However, ever
since specificity of motor ability theory had emerged, the investigation on related matter was slowing
down. Below is the summary of motor ability test instruments reported according to the year of testing
development.
Generally, the first half of the reported motor ability batteries was focused more on assessing normal
populations. With the growing awareness of the demand of motor assessment across populations, the
second half of the reported motor ability batteries was inclusive of assessment of individuals with
special needs.
1. Test & Items Sigma Delta Psi Test [Sigma Delta Psi, (1912) in (Clarke and
Clarke 1987) pg. 199] 100-yd dash, 120-yd low hurdles, Running high jump, Running
broad jump, Shot put, Rope climb, Basketball distance throw,
Football distance punt, 100-yd swim, One-mile run, Tumbling,
Posture and Scholarship
Year 1912
Factor
Claimed/
Measured
Not reported
Age & Sex Not reported
Reliability &
Validity Not reported
Remarks Early test of the general motor ability type (Clarke and Clarke
1987)
2. Test & Items Garfield Motor Ability Test (Garfield 1924)
100 yard dash, picking up paper, leg strength, leg strength, hand
2
strength, tricks, steadiness and tapping.
Year 1924
Factor
Claimed/
Measured
Speed of voluntary movement, accuracy of voluntary movement,
control of involuntary movement, strength, and motor adaptability
Age & Sex College women
Reliability &
Validity Not reported
Remarks To determine the relationship between motor ability and
intelligence quotient (Irvine 1951)
3. Test & Items Brace Motor Ability Test (Brace 1927)
A series of twenty individual stunts
Year 1927
Factor
Claimed/
Measured
Agility, Balance, Control, Flexibility, Agility and balance,
Strength, Strength and control
Age & Sex 8 to 48 years old, male and female
Reliability &
Validity Reliability range from 0.66 to 0.82 on individual stunts; 0.90 on
whole test reported by (National Research Council of the Research
Section 1950)
Remarks Pioneers study that measure native motor ability. Strongly
criticized on its content validity whether the stunts truly measure
innate skill (Philips and Wendler 1950).
4. Test & Items Cozens Test of General Athletic Ability (Cozens 1929)
Dip, baseball throw for distance, football punt for distance,
standing broad jump, bar snap, dodging, and quarter mile run
Year 1929
Factor
Claimed/
Measured
Age & Sex College men
Reliability &
Validity Reliability 0.97 Validity 0.77
3
Remarks Inclusive study exploiting the fundamental elements in motor
ability (Larson 1941).The test has no proven value as a measure of
motor ability (Philips and Wendler 1950).
5. Test & Items Johnson Test of Motor Educability (Johnson 1932) Straddle jump, stagger skip, stagger jump, forward skip, front roll,
jumping half turns – right or left, back roll, jumping half turns –
right or left alternately, front and back roll alternately, front and
back roll combinations and jumping full turns.
Year 1932
Factor
Claimed/
Measured
Native neuro-muscular coordination
Age & Sex From 11 years old to adult groups for both sexes
Reliability &
Validity Reliability 0.97 Validity 0.67
Remarks Probably best test of general motor educability in use at that time
(McCloy 1938). Claimed that elements of skill are learned because
they are the product of experience and environmental conditions.
6. Test & Items Motor Ability Test for College Women (Alden, Horton et al. 1932) Outdoor test: 50-y dash, jump and reach, bend high hang, baseball
throw, through the window ladder, trunk bend Indoor test: 40 yard maze run, trunk bend, ball change, jump and
reach
Year 1932
Factor
Claimed/
Measured
Speed, Strength of legs, Abdominal strength, Arm and shoulder
coordination
Age & Sex College women
Reliability &
Validity Reliability range from 0.63 to 0.87 And validity range from 0.37 to 0.70
Remarks Authors supposed that the test items were valid to measure general
motor ability.
4
7. Test & Items Humiston Test of Motor Ability (Humiston 1937) Alden dodge test;, roll over on mat;, run and climb over box;, run,
turn in circle and continue between barriers;, climb ladder;, throw
ball, catch it;, and run twenty yards.
Year 1937
Factor
Claimed/
Measured
Running, jumping, equilibrium, getting over obstacles, dodging and
hand-eyes coordination
Age & Sex College women
Reliability &
Validity Reliability 0.91 and validity 0.81
Remarks A series of items combined in sequence and run against time
8. Test & Items Kistler Test of Motor Ability (Kistler 1937) Battery 1 Dodge run, burpee test, shot-put Battery 2 General motor capacity, Rogers strength index, shuttle run,
standing broad jump Battery 3 Standing board jump, burpee test, shuttle run
Year 1937
Factor
Claimed/
Measured
Age-height-weight-build, strength, skill, health, personality,
educability and motor ability
Age & Sex Junior and senior high school
Reliability &
Validity Not reported
Remarks Battery 3 considered as the best test suited for all-round use.
9. Test & Items Iowa Revision of the Brace Motor Ability Test (McCloy 1937)
Ten stunts in two test batteries of five items. Stunts selected from
Brace Motor Ability Tests
Year 1937
5
Factor
Claimed/
Measured
Motor Educability
Age & Sex Elementary grades 4-6, junior and senior high school
Reliability &
Validity Not reported
Remarks A combination of previously established tests for the measurement
of general motor capacity with separate batteries for each of the
sexes. Also classified as Motor Educability
10. Test & Items The Minnesota Ability Test (Graybeal 1937; Bovard and Cozens
1938) Medicine ball throw, a ball catch, standing broad jump, forward
rolls and hurdles
Year 1937
Factor
Claimed/
Measured
Age & Sex College women
Reliability &
Validity
Remarks Item of hurdles is more specific to specialised skill (Irvine 1951)
11. Test & Items Metheny Revision of The Johnson Test (Metheny 1938) Front roll, back roll, jumping half turns, jumping full turn.
Year 1938
Factor
Claimed/
Measured
Speed of movement, strength to handle one’s own weight, Motor
Educability
Age & Sex From 11 years old to adult groups for both sexes
Reliability &
Validity Reliability 0.97 Validity 0.93 (boys) and 0.87 (girls)
Remarks Simplified from The Johnson Test. Four items boys and three items
for girls.
6
12. Test & Items Powell and Howe Motor Ability Tests (Powell and Howe 1938;
Powell and Howe 1939) a. Three-part of motor ability battery Broad jump, hurdles, scrambles a. Four-part of motor ability battery Broad jump, hurdles, scrambles, velocity throw
Year 1938
Factor
Claimed/
Measured
Power and strength, speed, and coordination
Age & Sex High school girls
Reliability &
Validity Reliability – Three-part of motor ability battery 0.985, Relative validity considered eligible through subjective and
objective measures
Remarks The four-part motor ability battery was for diagnosis purposes
while the three-part motor ability battery was to test general motor
ability.
13. Test & Items Scott Test of Motor Ability (Scott 1939; Scott 1943)
Battery 1 Basketball throw, Dash, Broad Jump, Wall pass Battery 2 Obstacle Race, Basketball throw, Broad jump Battery 3 Basketball throw, dash, broad jump
Year 1939
Factor
Claimed/
Measured
Strength, motor educability and ability, skill
Age & Sex College women
Reliability &
Validity Reliability ranges from 0.61 to 0.91 on individual test items Validity 0.91 (Battery 1), 0.86 (Battery 2) and 0.90 (battery 3)
Remarks The test had been developed using correlation on various
combinations situations of tests. On a basis that individuals are
different in their innate capacity or potential for acquiring various
skill and previous training and experience in motor activities.
7
14. Test & Items Carpenter Test of Motor Educability (Carpenter 1940; Carpenter
1942) Age, height and weight, left and right grips, 6 Brace-type tests, 5
Johnson-type tests, Sargent jump, Burpee test, Standing broad
jump, run and under, run and over, run and sit, hop, 30-y dash, ball
throw and putting the 4 pound shot
Year 1940
Factor
Claimed/
Measured
Strength, power, agility and motor educability
Age & Sex Primary grade level
Reliability &
Validity Reliability ranges from 0.77 to 0.90 on individual items Validity 0.82
Remarks General motor capacity and general motor ability had been used to
extract the individual general motor achievement quotient.
15. Test & Items Larson Test of Motor Ability (Larson 1941)
Indoor test: Dodging run, Bar snap, Chinning, Dips, Vertical jump Outdoor test: Baseball throw for distance, Chinning, Bar snap, Vertical jump.
Year 1941
Factor
Claimed/
Measured
Gross body coordination and agility, dynamic strength, motor
educability and motor explosiveness
Age & Sex College men
Reliability &
Validity Reliability 0.86 Validity 0.97 (indoor) 0.98 (outdoor)
Remarks The first reported test that had been developed using the factor
analysis technique. The test did not predict or indicate specific
qualities but rather valuable to indicate ability in the basic elements
underlying sports skills.
16. Test & Items Barrow Motor Ability Test (Barrow 1954)
Battery 1
8
Standing Broad Jump, Softball Throw, Zigzag Run, Wall Pass,
Medicine Ball Put, 60-Yard Dash Battery 2 ( Indoor) Standing broad jump, medicine ball put, zigzag run
Year 1954
Factor
Claimed/
Measured
Power, Arm-shoulder coordination, Agility, Hand-eye
Coordination, Strength, Speed
Age & Sex Junior and senior high school boys and college men
Reliability &
Validity Reliability range from 0.83-0.93
Remarks
17. Test & Items The Western Motor Ability Test (Campbell and Tucker 1967;
Yuhasz 1967), Agility Run, Basketball Throw, Broad Jump, Wall Toss
Year 1967
Factor
Claimed/
Measured
No factors claimed
Age & Sex Thirteen years old and above for both sexes.
Reliability &
Validity Reliability range from 0.89 to 0.95 on individual items (Yuhasz,
1967). Validity not reported
Remarks The original source is not available.
18. Test & Items Cratty Six-Category Gross Motor Test (Cratty 1969) Six item test - Body perception, gross agility, balance, locomotor agility, ball
throwing and ball tracking
Year 1969
Factor
Claimed/
Measured
Age & Sex 4 to 24 years old
Reliability &
9
Validity
Remarks Two separate six item tests for children with movement problem
and perceptual-motor impairment.
19. Test & Items Test of Motor Proficiency (TMP) (Gubbay 1975) Eight fine and gross motor items.
Year 1975
Factor
Claimed/
Measured
Age & Sex 8 to 12 years old
Reliability &
Validity
Remarks
20. Test & Items Bruininks-Oseretsky Test of Motor Performance (BOT) (Bruininks
1978) Gross motor Subtests: Running Speed and Agility, Balance, Bilateral Coordination,
Strength Gross and Fine Motor Subtest: Upper-limb Coordination Fine Motor Subtests: Response Speed, Visual-Motor Control, Upper-limb Speed and
Dexterity
Year
Factor
Claimed/
Measured
Running Speed and Agility Balance, Bilateral Coordination, Strength, Upper-limb
Coordination, Response Speed, Visual-Motor Control and Upper-
limb Speed and Dexterity.
Age & Sex 4.5 to 14.5 years old
Reliability &
Validity Test-retest reliability range from 0.68 to 0.86 on individual
composites for grade six. Content and construct validity have been
developed.
10
Remarks Most extensively used in adapted physical education, occupational
therapy and physical therapy (Burton and Miller 1998). Factor
analysis show that 70% of the total common factor items variance
accounted under one factor suggesting a general motor
development (Bruininks 1978) and general motor ability (Kraus,
Bruininks et al. 1981). However, due to validity issue and others
(Hattie and Edwards 1987), the popularity of the test has been
questioned (Burton and Miller 1998).
21. Test & Items Basic Motor Ability Tests-Revisied (BMAT-R) (Arnheim and
Sinclair 1979) Bead stringing, throwing, marble transfer, flexibility, standing
broad jump, rising to stand, and static balance total of eleven items.
Year 1979
Factor
Claimed/
Measured
Eye-hand coordination, finger dexterity, hand speed, flexibility, leg
power, agility, static balance, arm strength and eye-foot
coordination.
Age & Sex 4 to 12 years old
Reliability &
Validity
Remarks
22. Test & Items The Leuven Motor Ability Test (Renson, Beunen et al. 1980)
Stick balance, plate tapping, sit and reach, vertical jump, leg lifts,
arm pull, bent arm hang, 50m shuttle run, 1 min step test
Year 1980
Factor
Claimed/
Measured
Functional strength, static strength, explosive strength, trunk
strength, flexibility, running speed, speed of limb movement, eye-
hand coordination, pulse recovery,
Age & Sex 13 to 18 years
Reliability &
Validity
Remarks Used only among Belgian adolescence. Even though the test
claimed to be a motor ability test, it is more suitable to be
categorized as a motor fitness test as strength and endurance are
emphasized.
11
23. Test & Items Basic Gross Motor Assessment (BGMA) (Hughes and Riley 1981)
Gross motor tasks: Balance, jump, hop, skip, throw, yo-yo and ball handling total of
nine tasks.
Year 1981
Factor
Claimed/
Measured
Age & Sex 5.5 to 12.5 years old
Reliability &
Validity
Remarks
24. Test & Items Test of Gross Motor Development (TGMD) (Ulrich 1985; Ulrich
2000) Locomotor: Gallop, hop, horizontal jump, leap, run, skip and slide Object-control: Stationary bounce, catch, kick, two-hand strike, and overarm throw
Year 1985 & 2000
Factor
Claimed/
Measured
Age & Sex 3 to 10 years old
Reliability &
Validity
Remarks
25. Test & Items Movement Assessment Battery for Children Test and checklist
(MABC Test) (Henderson & Sugden, 1992) Manual dexterity, ball skills and balance - total of eight tasks
Year 1992
Factor
Claimed/
12
Measured
Age & Sex 4 to 12 years old
Reliability &
Validity
Remarks
References
Alden, F. D., Horton, M. O., & Caldwell, G. M. (1932). A Motor Ability for University Women for
the Classification of Entering Students into Homogenous Group. Research Quarterly, 3, 85-
120.
Arnheim, D. D.& Sinclair, W. A. (1979). The clumsy child. St. Louis, C. V. Mosby.
Barrow, H. M. (1954). "Tests of Motor Ability for College Men." Research Quarterly, 25, 253-260.
Bovard, J. F., & Cozens, F. W. (1938). Tests and measurements in physical education. Philadelphia,
Saunders.
Brace, D. K. (1927). Measuring motor ability: A scale of motor ability tests. New York, A. S. Barnes
and Company.
Bruininks, R. H. (1978). Bruininks-Oseretsky Test of Motor Proficiency. Minnesota, American
Guidance Service.
Burton, A. W. & Miller, D. E. (1998). Movement skill assessment. Illinios, Human Kinetics.
Campbell, W. R., & Tucker, N. M. (1967). An introduction to tests & measurement in physical
education. London, G. Bell & Sons Ltd.
Carpenter, A. (1940). "Test of Motor Educability for the First Three Grades." Child Development, 11,
293-299.
Carpenter, A. (1942). "The Measurement of General Motor Capacity and General Motor Ability in
The First three Grades." Research Quarterly, 13, 444-465.
Clarke, H. H., & Clarke, D. H. (1987). Application of measurement to physical education. Englewood
Cliffs, New Jersey, Prentice-Hall.
Cozens, F. W. (1929). The measurement of general athletic ability in college men. Eugene, University
of Oregon Press.
Cratty, B. J. (1969). Perceptual motor behavior and educational processes. Springfield, IL, Charles
C. Thomas.
Garfield, E. J. (1924). "Measurements of Motor Ability." Archives of Psychology, 9, 62.
Graybeal, E. (1937). Measurement of outcomes in physical education. Minnesota, University
Minnesota Press.
13
Gubbay, S. S. (1975). The clumsy child: A study of developmental apraxic and agnosic ataxia.
London, Saunders.
Hattie, J., & Edwards, H. (1987). "A Review of the Bruininks-Oseretsky Test of Motor Proficiency."
British Journal of Educational Psychology, 57, 104-113.
Henderson, S. E.,& Sugden, D. A. (1992). Movement assessment battery for children. Sidcup, Kent,
England, Therapy Skill Builders.
Hughes, J. E., & Riley, A. (1981). "Basic Gross Motor Assessment: Tool for use with Children
Having Minor Motor Dysfunction." Physical Therapy, 61, 503-511.
Humiston, D. A. (1937). "A Measurement of Motor Ability in College Women." Research Quarterly,
8, 181-185.
Irvine, C. M. (1951). The critical evaluation of general motor ability tests in physical education.
Human Movement and Exercise Science. Perth, University of Western Australia.
Johnson, G. B. (1932). "Physical Skill Tests for Sectioning Classes into Homogenous Units." The
Research Quarterly, 3, 128-136.
Kistler, J. W. (1937). "The Establishment of Bases for Classification of Junior and Senior High
School Boys into Homogenous Groups for Physical Education." Research Quarterly, 8, 11-
18.
Kraus, H., Bruininks, R. H., et al. (1981). "Structure of Motor Abilities in Children." Perceptual and
Motor Skills, 52, 119-129.
Larson, L. A. (1941). "A Factor Analysis of Motor Ability Variables and Tests, with Tests for College
Men." Research Quarterly, 12, 499-517.
McCloy, C. H. (1937). "An analytical Study of the Stunt Type Test as a Measure of Motor
Educability." Research Quarterly, 8, 46-55.
McCloy, C. H. (1938). Tests and measurement in health and physical education. New York, F. S.
Crofts and Company.
Metheny, E. (1938). "Studies in the Johnson Test as a Test of Motor Educability." Research
Quarterly, 9, 105-114.
National Research Council of the Research Section (1950). Measurement and evaluation materials in
health, physical education, and recreation. Washington, D. C., American Association for
Health, Physical Education, and Recreation. A Department of the National Education
Association.
Philips, M. and A. J. Wendler (1950). General Motor Skills. Measurement and evaluation materials in
health, physical education, and recreation. N. R. C. O. T. R. Section. Washington, D.C.,
American Association for Health, Physical Education, and Recreation. A Department of the
National Education Association.
Powell, E. and E. C. Howe (1938). "Wellesley College Studies in Hygiene and Physical Education -
Test and Analysis of General Motor Ability." Research Quarterly - Supplement, 9, 49-66.
14
Powell, E. and E. C. Howe (1939). "Motor Ability Tests for High School Girls." Research Quarterly,
10, 81-88.
Renson, R., G. Beunen, et al. (1980). Description of Motor Ability Tests and Anthropometric
Measurements. In Somatic and Motor Development of Belgian Secondary Schoolboys. Norm
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Leuven, Leuven University Press.
Scott, M. G. (1939). "The Assessments of Motor Abilities of College Women through Objective
Tests." Research Quarterly, 10, 63-83.
Scott, M. G. (1943). "Motor Ability Tests for College Women." Research Quarterly, 14, 402-405.
Ulrich, D. A. (1985). Test of gross motor development. Austin Tex, PRO-ED.
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Yuhasz, M. S. (1967). The Western Motor Ability Test, Physical Fitness Research Laboratory, The
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education. W. R. Campbell and N. M. Tucker (eds.). London, G. Bell & Sons Ltd.
15
APPENDIX B
LIST OF TABLES
Table 1. Guilford’s Two Dimensional System (Guilford, 1958) ................................................................... 15 Table 2. Fleishman’s Structure of Motor Ability (Fleishman,1964) ............................................................. 15 Table 3. Harrow Taxonomy of the Psychomotor Domain (Harrow, 1972) .................................................. 16 Table 4. Basic Abilities of Motor Performance Domain (Baumgartner & Jackson, 1975) .......................... 17 Table 5. Gentile’s Taxonomy of Movement Tasks (Gentile, 1987) ............................................................... 17
LIST OF FIGURES
Figure 1. Classification System for General Motor Ability, Motor Fitness &Physical Fitness (Clarke, 1967). Figure 2. Movement Skill Taxonomy (Burton & Miller, 1998) ......................................................................... Figure 3. Four Levels of Movement Skill Taxonomy & General Motor Ability (Burton & Rodgerson, 2001)
LIST OF HUMAN MOVEMENT TAXONOMY
Table 1. Guilford’s Two Dimensional System (Guilford, 1958)
Part of Type of Ability
body
involved
Strength Impulsion Speed Static
Precision
Dynamic
Precision
Coordination Flexibility
Gross General
strength
General
reaction
time
Static
balance
Dynamic
balance
Gross bodily
coordination
Trunk Trunk
strength
Trunk flexibility
Limbs Limb
strength
Limb-trust Arm
speed
Arm
steadiness
Arm aiming Leg flexibility
Hand Tapping Hand
aiming
Hand dexterity
Finger Finger
speed
Finger dexterity
Table 2. Fleishman’s Structure of Motor Ability
Physical Proficiency Psychomotor Abilities
1. Extent flexibility 1. Control precision
2. Dynamic flexibility 2. Multi-limb coordination
3. Explosive strength 3. Response orientation
4. Static strength 4. Reaction time
5. Dynamic strength 5. Speed of arm movement
6. Trunk strength 6. Rate control
7. Gross body coordination 7. Manual dexterity
8. Gross body equilibrium 8. Finger dexterity
9. Cardiovascular endurance 9. Arm-hand steadiness
10. Wrist-finger speed
11. Aiming
16
Physical Fitness
Motor Fitness
General Motor Ability
Arm-eye
coordination
Muscular
power
Agility Muscular
strength
Muscular
endurance
Circulo-
endurance
Flexibility Speed Foot-eye
coordination
Organic soundness and proper nutrition
Figure 1. Classification System for General Motor Ability, Motor Fitness and Physical Fitness
(Clarke, 1967).
Table 3. Harrow Taxonomy of the Psychomotor Domain (Harrow, 1972)
1.00 Reflex movements
1.10 Segmental reflexes
1.20 Intersegmental reflexes
1.30 suprasegmental reflexes
2.00 Basic-fundamental movements
2.10 Locomotor movements
2.20 Non-locomotor movements
2.30 Manipulative movements
3.00 Perceptual abilities
3.10 Kinaesthetic discrimination
3.20 Visual discrimination
3.30 Auditory discrimination
3.40 Tactile discrimination
3.50 Coordinated abilities
4.00 Physical Abilities
4.10 Endurance
4.20 Strength
4.30 Flexibility
4.40 Agility
5.00 Skilled movements
5.10 Simple adaptive skill
5.20 Compound adaptive skill
5.30 Complex adaptive skill
6.00 Non-discursive communication
17
6.10 Expressive movement
6.20 Interpretive movement
Table 4. Basic Abilities of Motor Performance Domain (Baumgartner & Jackson, 1975)
A Muscular strength
1. Arm strength
2. Leg strength
B Muscular power
1. Arm power
2. Leg power
C Muscular endurance
1. Arms and shoulder girdle
2. Abdominal muscles
3. Cardiorespiration
D Basic movement pattern
1. Running speed
2. Running agility
3. Jumping
4. Throwing ability
Table 5. Gentile’s Taxonomy of Movement Tasks (Gentile, 1987)
Performer
Stable Transport
Environment No
manipulation
Object
manipulation
No
manipulation
Object
manipulation
Stationary Inter-trial
variability
1 2 3 4
No
variability
5 6 7 8
Moving Inter-trial
variability
9 10 11 12
No
variability
13 14 15 16
18
Functional movement skills
Specialized movement
skills
Fundamental movement skills
Early movement
milestone
Motor abilities
Foundations of movement skills
Figure 2. Movement Skill Taxonomy (Burton & Miller, 1998)
Movement Skills
Movement Skill Sets
Movement Skill
Foundations
General Motor Ability
Figure 3. Four Levels of Movement Skill Taxonomy & General Motor Ability (Burton & Rodgerson,
2001)
References
Baumgartner, T. A. & Jackson, A. S. (1975). Measurement for Evaluation in Physical Education. Hopewell,
New Jersey: Houghton Mifflin Company.
Burton, A. W. & Miller, D. E. (1998). Movement Skill Assessment. Illinios: Human Kinetics.
Burton, A. W. & Rodgerson, R. W. (2001). New Perspectives on the Assessment of Movement Skills and Motor
Abilities. Adapted Physical Activity Quarterly, 18, 347-365.
Clarke, H. H. (1967). Application of Measurement to Health and Physical Education. Englewood Cliffs, New
Jersey: Prentice-Hall, Inc.
Fleishman, E. A. (1964). The Structure and Measurement of Physical Fitness. New Jersey: Prentice-Hall.
19
Gentile, A. M. (1987). Skill Acquisition: Action, Movement, and Neuromotor Processes. In J. H. Carr, R. B.
Shepherd, A. M. Gordon, A. M. Gentile & J. M. Held (Eds.), Movement Science: Foundations for
Physical Therapy in Rehabilitation. Rockville, MD: Aspen Publishers.
Guilford, J. P. (1958). A System of Psychomotor Abilities. American Journal of Psychology, 71, 164-174.
Harrow, A. J. (1972). A Taxonomy of the Psychomotor Domain a Guide for Developing Behavioral Objectives.
New York: David McKay Company, INC.
APPENDIX C
The selection of the motor skills for the balance and movement coordination test is based on
information below:
1. Malaysian Sports Council (Majlis Sukan Negara, Malaysia) conducted a talent identification and
development program in conjunction with hosting the Commonwealth Games 1998. A talent
identification and fitness testing handbook was published by Institut Sukan Negara in 1998
(Institute of Sport) containing the testing procedures, the normative data of 11 – 14 years
adolescents and normative data of Malaysian National athletes. Testing conducted and normative
data obtained among Malaysian school children are:
1. Body measurements – height, arm span, sitting height, body mass
2. Strength & power – vertical jump, weight throw
3. speed – 40m sprint
4. Agility – agility hexagon
5. endurance – 800m run
Listed testing as indicate above shown that the component of balance ability is not included. In
addition, task analysis on the listed testing also employed that there are less element of
propulsion, running action and ability to change directions, while moving and executing the
manoeuvres continuously that specify movement coordination.
On the other hand, two balance items (blind stork and rotating stork) were conducted among
Malaysian athletes (as listed in the table below). However, the normative data obtained at the 50
percentile score on the conducted balance test (blind stork and rotating stork) were varies (Blind
Stork: Female, minimum = 17.8s, maximum = 94.09s; Male, minimum = 11.97s, maximum =
106.19s and Rotating Stork: Female, minimum = 24.37s, maximum = 360.49s; Male, minimum =
50.97s, maximum = 342.99s).
20
Malaysian athletes 50 percentile score Female Male
Blind stork (s) Rotating stork
(s) Blind stork (s) Rotating stork
(s) artistic gymnastics, 94.09 24.37 97.10 65.12 rhythmic gymnastics 29.11 212.08 - - badminton, 17.8 203.4 19.6 81.0 bowling, 72.2 81.4 35.03 59.63 boxing, - - - - cricket, - - - - cycling, 17.52 76.73 37.95 50.97 diving, 44.05 43.61 106.19 257.00 hockey, 36.97 71.14 64.46 171.69 lawn bowling, 37.38 48.70 23.09 71.10 middle distance runners 23.00 36.50 23.46 79.85 netball, 42.12 38.70 - - sepaktakraw, - - - - racewalking, 18.03 84.46 11.97 153.75 rugby, - - - - shooting, 45.25 45.61 31.70 37.97 silat olahraga, - - - - squash 61.84 360.49 47.20 342.99 weight lifting - 36.3 30.3
Therefore, investigation focusing on the balance and motor coordination is conducted in this
research due to:
1. the range of scores on balance tests were varies among athletes and
2. minimal aspect of body propulsion and rapid change directions while moving and executing
the tasks continuously on the selected test items
2. There is very limited data pertaining to children’s health and physical fitness in Malaysia and only
limited findings have been reported or published (Singh, Singh & Larmie, 2004). Research and
published data by Singh, Singh and Larmie (2004) focused on health-related fitness components
tests and none on skill-related components test (the balance and motor coordination test).
21
Reference: Singh, R., Singh, M., Larmie, E. T. (2004). An Exercise Intervention Package on
Health-Related Physical Fitness in Malaysian Secondary School Boys. In Chin, M., Hensley,
L. D., Cote, P., Chen, S. (2004). Global Perspectives in the Integration of Physical Activity,
Sport, Dance and Exercise Science in Physical Education: From Theory to Practice, In 2nd
International Conference for Physical Educators (ICPE 2004). The Hong Kong Institute of
Education.
3. Research and published data by Jawis, Singh, Singh & Yassin (2005) on sepak takraw players on
3age categories (15, 18 & 23 years old) also focused on health-related fitness.
Reference: Jawis, M. N., Singh, R., Singh, H. J., Yassin, M. N. (2005). Anthropometric and
Physiological Profiles of Sepak Takraw Players. British Journal Sports Medicine, 39: 825-829
The variations of score ranges on balance tests as well as minimal aspect of body propulsion and rapid
direction changes directed this research to focus on the balance and movement coordination
components. In addition, this research provided an opportunity to widen research and publish data on
the skill-related components among Malaysian adolescents.
APPENDIX D
PENERANGAN RINGKAS
PROSEDUR PENTADBIRAN UJIAN DAN BORANG SKOR
Ujian McCarron Assessment of Neuromuscular Development (MAND Test)
Bahagian ini memberi penerangan ringkas berkenaan dengan prosedur pentadbiran ujian McCarron
Assessment of Neuromuscular Development (MAND). Penerangan protocol ujian yang lebih
komprenhesif boleh diperolehi di dalam manual ujian.
McCarron Assessment of Neuromuscular Development (MAND) mengandungi sepuluh jenis tugasan
motor. Lima tugasan dikategorikan sebagai Tugasan Motor Halus dan lima lagi dikategorikan sebagai
Tugasan Motor Kasar. Berikut adalah senarai tugasan:
Tugasan Motor Halus Tugasan Motor Kasar Manik dalam kotak Kekuatan gengaman tangan Manik dalam batang rod Pergerakan jari-hidung-jari Ketukan jari Lompatan Nat dan skru Jalan tumit-hujung jari Rod gelungsor Imbangan satu kaki
22
TUGASAN MOTOR HALUS
1. Manik dalam kotak
Tugasan ini memerlukan peserta untuk mengubah manik satu persatu dari kotak yang penuh dengan
manik ke dalam kotak yang kosong (kotak standard) dengan menggunakan satu tangan sepantas yang
mungkin dalam 30 saat. Penguji menunjukcara tugasan dan memberikan arahan kepada peserta.
Ulangi prosedur yang sama untuk tangan yang lain. Peserta dibenarkan membuat latihan awal jika
perlu.
Arahan:
1. “Dengan mengunakan tangan kanan, ambil manik dalam kotak satu persatu dan pindahkan di
dalam kotak kosong yang disediakan. Lakukan tugasan sepantas yang boleh sehingga arahan
berhenti. Ingat, hanya satu manik dalam satu masa. Sekiranya manik yang anda ambil jatuh,
biarkan dan teruskan aktiviti memindahkan manik ke dalam kotak yang disediakan.”
2. “Dengan mengunakan tangan kiri, ambil manik dalam kotak satu persatu dan pindahkan di
dalam kotak kosong yang disediakan. Lakukan tugasan sepantas yang boleh sehingga arahan
berhenti. Ingat, hanya satu manik dalam satu masa. Sekiranya manik yang anda ambil jatuh,
biarkan dan teruskan aktiviti memindahkan manik ke dalam kotak yang disediakan.”
Rekod hanya jumlah manik yang diletakkan ke dalam kotak yang kosong dalam masa yang diberikan.
Skor untuk tugasan ini adalah jumlah manik yang berjaya diubah oleh kedua-dua tangan dengan betul.
2. Manik dalam batang rod
Di dalam tugasan ini, peserta dikehendaki untuk memasukkan manik kayu berbentuk silinder ke
dalam rod. Tangan tidak dominan memegang bahagian bawah rod dengan teguh manakala tangan
dominan akan memasukkan manik ke dalam rod satu persatu sepantas mungkin dalam 30 saat. Kedua-
dua tangan tidak diletakkan di atas meja dan bebas bergerak untuk mengubah manik. Ulangi prosedur
yang sama dengan mata ditutup. Peserta dibenarkan melakukan latihan awal sekiranya perlu.
Arahan: “Ambil manik satu persatu dan masukkan ke dalam batang rod seperti ini. Lakukan sepantas
yang boleh sehingga saya beritahu untuk berhenti. Pastikan untuk memegang batang rod,
lengan anda jauh sedikit dari badan.”
23
Rekodkan jumlah manik yang berjaya dimasukkan ke dalam rod dalam masa yang diberikan. Skor
untuk tugasan ini adalah jumlah semua manik yang berjaya dipindahkan dengan betul dengan mata
terbuka dan mata tertutup.
3. Ketukan Jari
Peserta dikehendaki untuk menepuk jari telunjuknya keatas dan ke bawah dalam masa 10 saat. Jari
telunjuk hendaklah menyentuh gelang getah apabila digerakkan ke atas dan menyentuh lantai papan
apabila digerakkan ke bawah. Ketinggiang gelang getah hendaklah berada pada paras yang sama
dengan kedudukan jari telunjuk ketika jari selari dengan platform. Ulangi prosedur ini dengan tangan
yang sebelah lagi. Peserta dibenarkan melakukan latihan awal jika perlu.
Arahan: “Gengam tangan anda, tetapi biarkan ibu jari dan jari telunjuk terkeluar. Sekarang gerakkan
jari telunjuk anda ke atas dan ke bawah supaya menyentuh papan dan getah pengikat.
Lakukan gerakan ketukan jari sepantas yang boleh. Gerakkan hanya jari telunjuk.”
Perhati dan rekodkan pelakuan motor ini (rentak sentuhan, gerakan tangan dan jari yang lain, lebihan
gerakan di lengan dan setuhan jari yang tidak lengkap). Skor tugasan ini adalah jumlah keseluruhan
sentuhan yang lengkap dan pemarkahan perlakuan motor yang diperhatikan untuk kedua-dua tangan.
4. Nat dan skru
Di dalam tugasan ini, peserta dikehendaki untuk memusing set besar skru ke dalam nat sepantas yang
boleh. Tangan dominan yang memegang skru akan memusing manakala tangan tidak dominan
memegang nat yang tidak bergerak. Kedua-dua tangan berada di hadapan peserta tanpa diletakkan di
meja atau di pangkuan. Ulangi prosedur yang sama untuk set skru yang kecil.
Arahan: “Pegang nat dengan tangan ini (tangan non-dominan) dan pusingkan skru (tangan dominan)
ke dalam nat. Pusingkan skru sepantas yang boleh hingga ke hujung.”
Skor tugasan individu dikira dengan menolak tempoh masa yang di ambil untuk memusingkan skru
ke hujung nat dari skor 100. Rekodkan skor dengan mencampurkan kedua-dua skor untuk set skru
kecil dan besar.
5. Rod gelunsor
24
Tugasan ini mengkehendaki peserta mengerakkan penyepit gelungsor seperlahan yang boleh di dalam
rod gelungsor dengan menggunakan otot lengan dan tangan. Peserta berdiri ketika melakukan tugasan
dengan rod gelungsor berada di paras pinggang dan ±30 sm dari badan. Kedua-dua tangan perlu
melakukan tugasan ini. Tangan kiri akan melakukan pergerakan dari arah kiri ke kanan manakala
tangan kanan akan melakukan pergerakan dari arah kanan ke kiri.
Arahan: “Selama ini, anda telah disuruh untuk melakukan tugasan motor sepantas yang boleh. Kali
ini, kita akan melakukan sesuatu yang berlainan. Saya mahu anda melakukan aktiviti ini
seperlahan yang boleh. Ingat, lebih perlahan lebih baik. Gerakkan penyepit gelungsur
seperlahan yang boleh, seperti ini.”
Rekod masa yang digunakan untuk memggerakkan penyepit di rod gelunsor di antara dua penghujung
rod. Peserta mungkin akan mengambil masa yang lama untuk menyempurnakan tugasan kerana
semakin lambat pergerakan semakin baik skor. Walaubagaimana pun skor maksimum 30 saat
diberikan kepada setiap tangan. Rekodkan pemarkahan tingkahlaku pergerakan ke atas kadar
pergerakan, ganguan, perubahan kepala-badan, dan pergerakan badan berlebihan juga dikira.
Jumlahkan skor kedua-dua tangan untuk memperolehi jumlah skor keseluruhan tugasan.
TUGASAN MOTOR KASAR
1. Kekuatan tangan
Tugasan ini mengkehendaki peserta memegang alat dynamometer dengan lengan tegak ke hadapan
pada paras bahu dan mengengam pemegang alat dynamometer sekuat yang boleh. Setiap tangan perlu
melakukan dua percubaan dan bergilir tangan antara percubaan. Skor terbaik di antara dua percubaan
direkodkan dalam kilogram.
Arahan: “Gengam pemegang alat ini sekuat yang boleh”
2. Pergerakan jari-hidung-jari
Di dalam tugasan ini, peserta dikehendaki menyentuh hujung extensi jari tangan sebelah lagi dengan
jari telunjuk dan bergerak untuk menyentuh hujung hidung dalam tempoh 10 saat atau lebih kurang
lima kali ulangan sentuhan. Peserta mengulangi prosedur yang sama untuk kedua-dua belah tangan
dengan mata terbuka dan mata tertutup menjadikan jumlah tugasan ini mengandungi empat
percubaan.
25
Arahan: “ Ini bukanlah ujian kepantasan; Cuma relaks dan lakukan yang terbaik. Letakkan tangan kiri
anda tegak ke hadapan dan tunjukkan jari anda ke dinding. Sekarang, tunjukkan hanya jari
anda ke dinding sebelah kanan. (Jari telunjuk tangan kiri mengarah ke kanan dan menunjuk
ke sebelah kanan dinding). Dengan jari telunjuk kanan anda, sentuh hujung hidung anda
dan hujung jari anda seperti ini.”
Skor tingkahlaku pergerakan lengan, jari telunjuk pada tangan yang diextensikan, titik sentuhan,
bengkokan siku dan sentuhan adalah direkodkan. Jumlah keseluruhan skor adalah jumlah skor
pemarkahan sentuhan jari-hidung-jari dilakukan ketika mata terbuka dicampurkan dengan skor ketika
mata tertutup untuk kedua-dua belah tangan kiri dan kanan.
3. Lompatan
Peserta dikehendaki melompat sejauh yang boleh dengan kedua-dua kaki berada di belakang garisan.
Kualiti tingkah laku pergerakan iaitu henjutan kaki, pengunaan tangan dan lengan, imbangan badan
dan mendarat adalah dinilai. Jumlah keseluruhan skor adalah dengan mencampurkan jarak lompatan
dengan skor tingkah laku pergerakan yang diperhatikan.
Arahan: “Dengan kedua-dua belah kaki, lompat ke hadapan sejauh yang boleh.”
4. Jalan tumit-hujung jari
Di dalam tugasan ini, peserta dikehendaki untuk berjalan ke hadapan di atas garisan tegak sepanjang
10 kaki dengan meletakkan tumit (kaki hadapan) di hadapan jari kaki (kaki belakang) berterusan dan
sebaliknya apabila bergerak mengarah ke belakang. Peserta boleh memakai kasut bertapak rata, atau
berkaki ayam, atau berstokin untuk melaksanakan tugasan dengan tangan diletakkan di pinggang.
Arahan:
1. Ke hadapan “Relaks dan lakukan tugasan ini sebaik yang boleh. Letakkan kedua-dua tangan
anda di pinggang dan jalan di atas garisan dengan meletakkan tumit anda di hadapan jari kaki,
menyentuh tumit dengan jari kaki di setiap langkah. Mulakan dari sini (hujung tali pita) dan
jalan sehingga ke sebelah hujung sana.”
2. Ke belakang “Kali ini, jalan mengundur ke belakang. Letakkan kedua-dua tangan anda di
pinggang dan jalan di atas garisan dengan meletakkan jari kaki anda di belakang tumit, jari
kaki dengan tumit bersentuhan di setiap langkah.”
26
Tingkah laku pergerakan diperhatikan ke atas bahagian lengan, kaki, jarak tumit dan jari kaki, gerakan
dan posisi kaki diberi pemarkahan dan direkodkan. Jumlah keseluruhan skor adalah skor pergerakan
berjalan ke hadapan dicampur dengan skor pergerakan ke belakang.
5. Imbangan satu kaki
Peserta dikehendaki mengekalkan imbangan badan ketika berdiri di atas satu kaki selama maksima
masa 30 saat. Peserta dibenarkan untuk mengerakkan lengan dan tangan untuk mengekalkan
imbangan. Percubaan dilaksanakan untuk kedua-dua belah kaki kiri dan kanan ketika mata terbuka
dan tertutup. Mulakan catatan masa apabila satu kaki di angkat dari lantai dan hentikan masa apabila
peserta mula untuk melompat, atau mengangkat kaki atau tangan menyentuh lantai. Percubaan kedua
akan diberikan kepada peserta sekiranya peserta gagal untuk mengekalkan imbangan untuk sekurang-
kurangnya 10 saat.
Arahan:
1. Mata terbuka “Berdiri di atas satu kaki selama yang boleh sehingga saya beritahu anda untuk
berhenti”.
2. Mata tertutup “Kali ini, berdiri di atas satu kaki dengan mata tertutup. Ingat, anda mesti
pejamkan mata anda”.
Rekodkan masa imbangan dalam saat. Jumlah keseluruhan skor adalah skor masa imbangan kaki kiri
dan kaki kanan ketika mata dibuka dicampurkan dengan masa imbangan kaki kiri dan kaki kanan
ketika mata ditutup.
27
McCarron (MAND) Assessment of Neuromuscular Development
Kad Skor NDI __________
NAMA : ________________________________________ TARIKH : _____________
TARIKH LAHIR : ____________________ UMUR KETIKA UJIAN : _____________
JANTINA :Lelaki /Perempuan TANGAN DOMINAN: Kanan /Kiri
KAKI DOMINAN: Kanan /Kiri
TINGGI : __________BERAT : ______DIAGNOSIS: ___________________________
MANIK DALAM
KOTAK (jumlah manik dalam
30 saat)
Kanan ____ Kiri ____ Jumlah
____ Skala skor _____
MANIK DALAM
ROD (gunakan manik
berbentuk silinder
sahaja – jumlah
manik dalam 30 saat)
Mata Terbuka
____ Mata Tertutup
____ Jumlah
____ Skala skor _____
KETUKAN JARI
(gunakan lampiran
skor)
Kanan ____ Kiri ____ Jumlah
____ Skala skor _____
NAT DAN SKRU (masa dalam saat
untuk
menyempurnakan
tugasan)
Besar 100 - ___ = ____
Kecil 100 - ___ = ____
Jumlah
____ Skala skor _____
ROD GELUNGSUR
(gunakan lampiran
skor)
Skala skor _____
Sub-Jumlah _____
PURATA MOTOR HALUS _____
28
KEKUATAN
GENGAMAN (terbaik
dari dua percubaan
untuk setiap tangan)
Kanan ____ Kiri ____ Jumlah
___ Skala skor _____
JARI-HIDUNG-JARI (gunakan lampiran skor)
Mata Terbuka
____ Mata Tertutup
____ Jumlah
___ Skala skor _____
LOMPATAN
(gunakan lampiran
skor)
Jumlah
___ Skala skor _____
JALAN TUMIT-
HUJUNG JARI
(gunakan lampiran
skor)
Ke hadapan _____
Ke belakang _____
Jumlah
___ Skala skor _____
IMBANGAN SATU KAKI (masa dalam saat sehingga 30 saat)
Mata terbuka Kanan ____ Kiri ____ Jumlah
___
Mata tertutup Kanan ____ Kiri ____ Jumlah
___
Jumlah
___ Skala skor _____
Sub-Jumlah _____
PURATA MOTOR KASAR _____
KESELURUHAN PURATA MOTOR _____
29
KAD SKOR MAND
NAMA : ________________________________
LAMPIRAN PROTOKOL
KETUKAN JARI
KANAN KIRI
A. Rithma ________ ________
B. Pergerakan tangan tak berkenaaan ________ ________
C. Pergerakan tangan yang berlebihan ________ ________
D. Kesempurnaan jarak pergerakan ________ ________
E. Jumlah ketukan ________ ________
JUMLAH ________ ________
ROD GELUNGSUR
KANAN KIRI
A. Pertukaran kadar kelajuan _______ ________
B. Gangguan _______ ________
C. Perpindahan pergerakan kepala-badan _______ ________
D. Pergerakan badan tak berkenaan _______ ________
E. Masa diambil _______ ________
JUMLAH _______ ________
JARI-HIDUNG-JARI
MATA TERBUKA MATA TERTUTUP
KANAN KIRI KANAN KIRI
A. Kelicinan pergerakan tangan _______ _______ _______ ________
B. Ketetapan jari telunjuk _______ _______ _______ ________
C. Titik sentuhan _______ _______ _______ ________
D. Bengkokan siku _______ _______ _______ ________
E. Sentuhan/penekanan _______ _______ _______ ________
JUMLAH _______ JUMLAH ________
30
LOMPATAN
LOMPAT 1 LOMPAT 2 LOMPAT 3
A. Henjutan _____ _____ _____
B. Kegunaan tangan _____ _____ _____
C. Imbangan badan _____ _____ _____
D. Mendarat dengan lutut dibengkokan _____ _____ _____
E. Jarak mendarat _____ _____ _____
JUMLAH _____ _____ _____
JALAN TUMIT-JARI KAKI
KE HADAPAN KE BELAKANG
A. Posisi tangan _______ ________
B. Kaki di atas tali pita _______ ________
C. Jarak tumit-jari kaki _______ ________
D. Kelicinan pergerakan _______ ________
E. Keselarian kedudukan kaki _______ ________
JUMLAH _______ ________
31
PROTOKOL PEMARKAHAN UJIAN JARI-HIDUNG-JARI
(Pemerhatian masa 10 saat untuk setiap percubaan)
MATA
TERBUKA
MATA
TERTUTUP
Kanan Kiri Kanan Kiri
A Pergerakan tangan 4. Arah gerakan tangan adalah licin 2. Pergerakan tangan sedikit tidak sekata atau
bergetar
1. Gerakan tangan mengelirukan dan terhenjut-
henjut
B
Jari telunjuk pada tangan yang didepakan kehadapan 4. Kekal tetap
2. Sedikit bergoyang dan bergetar
1. Jelas bergoyang dan bergetar
C
Titik sentuhan 4. Titik sentuhan di hujung hidung dan di hujung
jari telunjuk yang didepakan
2. Terlepas salah satu titik sentuhan di hujung
hidung dan di hujung jari telunjuk yang
didepakan
1. Terlepas di kedua-dua titik sentuhan di hujung
hidung dan di hujung jari telunjuk yang
didepakan
D
Bengkokan siku (pergerakan kedalam) 4. Lengan kekal didepakan 2. Siku sedikit bengkok (kurang dari 30º) 1. Bengkok siku yang jelas (lebih daripada 30º)
32
E
Tekanan sentuhan 4. Menyentuh dengan lembut pada hujung jari
yang didepakan kehadapan dan hujung hidung
2. Kelihatan menekan jari telunjuk pada tangan
yang didepakan ke hadapan atau menekan
hujung hidung sekali atau dua kali
1. Kelihatan menekan jari telunjuk pada tangan
yang didepakan ke hadapan atau menekan
hujung hidung tiga kali atau lebih
Jumlah
33
PROTOKOL PEMARKAHAN UJIAN LOMPATAN
(Pergerakan badan berdasarkan kepada keseluruhan pergerakan yang diperhatikan ketika ketiga-tiga
lompatan dilakukan)
A Lompatan 4. Lompatan seimbang keudara dari kedu-duaa belah kaki 2. Lompatan yang janggal ke udara, lebih menggunakan sebelah kaki
untuk melompat. 1. Lompatan jangga, keupayaan melompat keudara terhad.
B Penggunaan tangan 4. Tangan membantu dengan sedikit pergerakan ke hadapan dan
kembali ke sisi 2. Sedikit bantuan tangan yang bergerak dengan lemah 1. Tangan dikeraskan; tidak membantu gerakan
C Imbangan badan 4. Mendarat stabil, pusat graviti di tengah (tetap di kedudukan
mendarat) 2. Mendarat tidak stabil tetapi berupaya menmperolehi imbangan 1. Mendarat tidak stabil; mengambil langkah ke hadapan atau ke
belakang atau menggunakan tangan untuk mengelak dari jatuh.
D Mendarat dengan lutut dibengkokkan 4. Mendarat dengan licin dengan kedua-dua belah kaki serentak dengan
sedikit bengkok pada lutut untuk menyerap hentakan
2. Sedikit mendarat dengan kaku, bengkokan lutut yang terhad 1. Mendarat dengan lutut yang kaku; badan bergegar ketika mendarat
E Jarak lompatan Jarak direkodkan yang terjauh dari tiga percubaan
Jumlah
34
PROTOKOL PEMARKAHAN UJIAN JALAN TUMIT-HUJUNG JARI
(Individu berjalan pada jarak 10 kaki)
Ke
Hadapan Ke
Belakang A Pergerakan tangan/badan
4. Kedua-dua tangan kekal di pinggul 2. Mengerakkan satu tanggan dari pinggul 1. Mengerakkan kedua-dua tanggan dari pinggul
B
Kaki 4. Mengekalkan kedua-dua kaki di atas pita garisan 2. Kaki diubahsuai dari garisan sekali atu dua kali (Bila
kurang dari separuh dari pita dpijak, kaki dikira
sebagai tidak memijak di atas pita garisan)
1. Kaki diubahsuai dari garisan melebihi dari tiga kali
C Jarak tumit dan hujung jari 4. Kaki diletakkan lebih kurang satu inci dari hujung
jari 2. Kaki diletakkan lebih dari satu inci dari hujung jari
sekali sekala
1. Kaki diletakkan lebih dari satu inci dari hujung jari
tiga kali atau lebih
D Pergerakan 4. Berjalan licin ke hadapan 2. Sedikit berhenti ketika pergerakan kehadapan 1. Perubahan berat ke hadapan dan ke belakang ketika
berjalan
E Keselarian tapak 4. Kedua-dua kaki selari dengan pita 2. Langkah yang betul, tetapi kemudian berputar pada
arah (20º atau lebih) dari garisan
1. Langkah yang berputar pada arah (20º atau lebih) dari
garisan
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Keselarian tapak Putaran
Jumlah
20º
PROTOKOL PEMARKAHAN UJIAN KETUKAN JARI
(Pemerhatian untuk tempoh masa 10 saat untuk setiap tangan)
KANAN KIRI
A Ritma ketukan 4. Sekata, ritma ketukan yang konsisten 2. Gangguan ketukan sekali sekala, tetapi berupaya kembali
konsisten
1. Berubah-ubah, ketukan tidak beritma
B Pergerakan tangan tidak berkenaan 4. Bergerak hanya jari telunjuk, gengaman tangan kekal 2. Ibu jari bergerak berlebihan 1. Ibu jari dan jari lain bergerak berlebihan
C Lebihan pergerakan tangan
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4. Pergelangan tangan atau atas lengan kekal tidak bergerak
ketika ketukan 2. Kadang-kadang (sekali dua) pergerakan pergelangan tangan
atau atas lengan untuk ‘membantu’ ketukan
1. Kerap (tiga kali atau lebih) pergerakan pergelangan tangan
atau atas lengan untuk ‘membantu’ ketukan D Kesempurnaan jarak
4. Jari telunjuk menyempurnakan jarak ketukan dari lantai ke
ketinggian gelang getah
2. Kadang-kadang (sekali dua) pergerakan yang tidak lengkap
antara lantai dan gelang getah
1. Kerap (tiga kali atau lebih)pergerakan yang tidak lengkap
antara lantai dan gelang getah
E Jumlah ketukan jari yang sempurna dalam sepuluh saat Jangan kira pergerakan yang tidak sempurna atau sentuhan yang
disebabkan pergerakan pergelangan tangan atau lengan
Jumlah
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PROTOKOL PEMARKAHAN UJIAN ROD GELUNGSOR
(Pemerhatian ketika pergerakan tangan kiri dan kanan)
Jarak individu berdiri lebih kurang satu kaki dari rod gelungsur pada paras ketingian pinggang.
KANAN KIRI
A Pergerakan henjutan-impulsif (pertukaran kadar kelajuan) 4. Pergerakan lunjuran yang sekata dan berterusan 2. Pertukaran pergerakan penyepit gelungsur; pertukaran
kadar kelajuan yang ketara 1. Pertukaran pergerakan penyepit gelungsur; pertukaran
kadar kelajuan yang ketara disertai dengan pergerakan
berciri henjutan dan erotan
B Ganguan 4. Lakuan tugasan tanpa ganguan (mata fokus pada penyepit
rod ketika tugasan gelungsuran)
2. Ganguan dengan rangsangan luar (mata bergerak dari
fokus sekali ketika tugasan gelungsuran)
1. Ganguan dengan rangsangan luar (mata bergerak dari
fokus dua kali atau lebih ketika tugasan gelungsuran)
C Pemindahan kepala-badan 4. Kepala dan badan kekal sementara mata mengikuti
penyepit gelungsor; pergerakan mata selari dengan
pergerakan penyepit gelungsur
2. Turutan pergerakan mata yang terhad dengan mengerakan
kepala atau mengubah kedudukan badan sedikit untuk
mengikuti pergerakan penyepit gelungsur
1. Pergerakan badan ketika mengikuti pergerakan penyepit
rod; badan atau kepala;juga mata bergerak melebihi
garisan tengah badan
D Pergerakan badan berlebihan 4. Postur badan rehat dan kekal, pergerakan hanya pada
tangan yang melakukan tugasan
2. Pergerakan berlebihan pada tangan dan kaki sekali ketika
melakukan tugasan
1. Pergerakan berlebihan pada tangan dan kaki dua kali atau
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lebih ketika melakukan tugasan E Kelajuan pergerakan (sehingga 30 saat). Rekodkan masa yang
diambil untuk mengerakkan penyepit gelungsor ke hujung
rod. Skor maksima untuk setiap tangan ialah 30 saat. Apabila
kelajuan pergerakan adalah lima saat atau kurang, rekodkan
skor “1”untuk setiap pemerhatian pergerakan di atas (A, B, C
dan D).
Jumlah
39
UJIAN IIMBANGAN DAN KOORDINASI PERGERAKAN (Balance And Movement
Coordination Test – BMC Test)
Ujian 1: Imbangan satu kaki dengan kedua tangan ditinggikan di atas kepala
Objektif: Untuk mengukur imbangan statik pelaku berdiri di atas satu kaki sementara tangan
memegang kayu rod/pembaris di atas kepala. Imbangan ini dilakukan untuk setiap kaki ketika mata
terbuka dan tertutup.
Alatan dan Material: Jam randik, pembaris 30sm, dan lantai yang rata dan kukuh.
Prosedur: Tangan tegak di atas kepala dan memegang pembaris pada jarak seluas bahu. Berdiri di
atas satu kaki dan angkat satu kaki pada sudut lebih kurang 60 hingga 90 darjah. Dengan mata
terbuka, kekalkan imbangan selama yang boleh sehingga diberitahu untuk berhenti. Ulangi aktiviti
yang sama dengan menukar kaki sokongan. Ujian ini diikuti dengan imbangan untuk setiap belah kaki
dengan mata tertutup. Hentikan masa apabila pelaku melompat, mengelungsur, mengubah kaki
imbangan, kaki yang diangkat menyentuh lantai, tangan terlepas ketika memegang pembaris, atau
selepas pelaku dapat mengekalkan imbangan sehingga 60 saat. Sekiranya percubaan pertama kurang
dari 10 saat, ulangi percubaan dan masa terbaik dikira sebagai skor.
Arahan: “Apabila bersedia, angkat sebelah kaki anda dan imbang badan anda selama yang boleh
ketika tangan berada tegak di atas kepala.”
Pemarkahan: Rekodkan masa dalam saat. Jumlah skor dikira dari jumlah masa imbangan dalam saat
untuk setiap kaki ketika mata terbuka dan tertutup.
Mata terbuka Mata tertutup Jumlah (saat)
Kaki kiri
Kaki kanan
Jumlah
Ujian 2: Imbangan dinamik – Lompat sisi
Objektif: Untuk mengukur imbangan dinamik pelaku ketika melompat sisi.
Alatan dan material: Jam randik, pita penanda untuk menanda satu garisan tegak di atas lantai
Prosedur: Pelaku berdiri dengan kedua-dua belah kaki berada di sisi pita penanda garisan tegak di
atas lantai. Pada arahan “mula”, pelaku melompat sisi ke kiri dan kanan penanda garisan tegak dalam
tempoh 10 saat dengan kedua-dua belah kaki sepantas yang boleh. Pelaku mestilah melompat dengan
kedua-dua belah kaki. Lompatan dengan sebelah kaki adalah tidak dibenarkan (Sila lihat Gambarajah
1).
40
Pemarkahan: Jumlah lompatan ketika melompat dengan kedua-dua belah kaki dalam masa 10 saat
direkodkan. Lompatan yang dilakukan dengan hanya menggunakan sebelah kaki atau lompatan yang
menyentuh garisan adalah tidak dikira.
Ujian Imbangan Dinamik
Jumlah Lompatan
lompatan sisi
Gambarajah 1: Ilustrasi Ujian Imbangan Dinamik
Ujian 3: Lari ulangalik – dengan objek dan tanpa objek.
Objektif: Untuk mengukur keupayaan pelaku mengkoordinasi dan mengawal pergerakan badan
disebabkan oleh perubahan arah pergerakan.
Alatan dan material: Jam randik, tali penanda untuk menanda dua garisan selari sejauh 5 m, dua
blok kayu 5 x 5 x 10 cm yang diletakkan di belakang garisan penamat.
Prosedur:
Tugasan 1: Pelaku berdiri dibelakang garisan mula. Pada arahan “mula”, pelaku lari ke arah blok
kayu di garisan penamat, dengan satu kaki menyentuh garisan luar, ambil kayu blok,
dan lari semula ke garisan mula dan letakkan blok kayu di belakang garisan. Sekali lagi,
pelaku lari dan ulangi prosess yang sama seperti di atas dan akhirnya lari sepantas yang
boleh ke garisan penamat (Sila lihat Gambarajah 2).
Tugasan 2: Ulangi larian seperti yang diterangkan di atas tanpa mengambil apa-apa objek dengan
rehat dibenarkan diantara tugasan.
Pemarkahan: Tempoh masa yang digunakan dalam saat
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Tugasan 1 Tugasan 2
Saat
Garisan penamat
garisan mula
Gambarajah 2: Ilustrasi Ujian Lari Ulang Alik
Ujian 4: Lompatan satu kaki – setempat dan kepantasan
Objektif: Untuk mengukur keupayaan pelaku mengkoordinasi dan mengawal pergerakan badan
disebabkan oleh perubahan arah pergerakan.
Alatan dan material: Jam randik, 2 kon, pita penanda
Prosedur:
Tugasan 1: Lompat satu kaki setempat – perlaku melompat berterusan dengan satu kaki sebanyak
yang boleh dalam kawasan seluas 50 sm persegi dalam masa 10 saat. Kira setiap
lompatan dan berhenti mengira sekiranya mana-mana bahagian kaki memijak atau
terkeluar dari garisan atau pelaku berhenti melompat. Ulangi tugasan untuk kaki yang
satu lagi.
Tugasan 2: Melompat untuk kepantasan –pelaku berdiri di belakang garisan mula. Pada arahan
‘mula’ pelaku melompat dengan satu kaki ke garisan penamat (jarak 10 m) sepantas
yang boleh. Hentikan masa apabila kaki lompatan mencecah garisan penamat. Ulangi
tugasan untuk kaki yang satu lagi dengan rehat diberikan antara tugasan.
Pemarkahan:
Tugasan 1: Jumlah bilangan lompatan dalam masa yang diberikan.
Tugasan 2: Masa yang diperlukan untuk menyempurnakan tugasan.
Kaki kiri Kaki kanan Jumlah
1 2 3
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Tugasan 1 (bilangan lompatan)
Tugasan 2 (saat)
Ujian 5: Larian zig-zag – arah kiri dan kanan
Objektif: Untuk mengukur keupayaan pelaku mengkoordinasi dan mengawal pergerakan badan
disebabkan oleh perubahan arah pergerakan.
Alatan dan material: Jam randik, 5 kon.
Prosedur: Letakkan lima kon dalam satu garisan lurus dengan jarak 1.5 m antara kon (lihat
Gambarajah 3a dan 3b). Pelaku berdiri di belakang garisan mula. Pada arahan ‘mula’, pelaku lari ke
kiri kon pertama, kemudian ke kanan kon kedua dan seterusnya secara selang-seli dan selepas kon ke
lima, pusing dan ulangi larian selang-seli di antara kon hingga ke garisan penamat sepantas yang
boleh. Rekodkan masa untuk menyempurnakan tugasan dalam saat. Untuk tugasan kedua, ulangi
tugasan pertama tadi dari garisan mula yang lain (untuk mengubah arah larian). Gambarajah 3a adalah
pergerakan bermula dari sebelah kanan peserta manakala Gambarajah 3b adalah pergerakan bermula
dari sebelah kiri peserta.
Pemarkahan: Masa dalam saat
Arah kanan Arah kiri
Larian Zig-zag (saat)
3a
Arah kanan
10 meter
garisan mula/penamat garisan mula/penamat
Gambarajah 3: Ilustrasi Ujian Lari Zigzag
3b Arah kiri
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Ujian 6: Lompat Kudron –berbeza arah
Objektif: Untuk mengukur keupayaan pelaku mengkoordinasi dan mengawal pergerakan badan
disebabkan oleh perubahan arah pergerakan.
Alatan dan material: Jam randik, pita penanda
Prosedur: Bentukan dua garisan melintang di atas lantai (lihat Gambarajah 4a dan 4b). Pelaku berdiri
di posisi mula. Pada arahan “mula”, pelaku melompat dengan kedua-dua belah kaki ke petak kudron
1, 2, 3 dan 4 dan kembali ke petak 1, mengikut urutan. Pelaku meneruskan lompatan sehingga arahan
berhenti. Kira jumlah lompatan. Untuk tugasan kedua, ulangi lompatan dari garisan mula dengan
urutan lompatan pada arah yang berbeza (Lihat Gambarajah 4b).
Pemarkahan: Skor adalah jumlah bilangan kaki mendarat di petak kudron yang betul dalam 10 saat.
Tolak separuh (0.5) mata setiap kali kaki mendarat di atas atau terkeluar dari garisan. Skor terbaik di
kira sebagai skor untuk ujian ini.
Percubaan 1 Percubaan 2 Percubaan
terbaik
Jumlah kaki
mendarat
dengan betul
Jumlah
kesilapan Jumlah kaki
mendarat
dengan betul
Jumlah
kesilapan
Arah A
Arah B
Gambarajah 4a – Arah A Gambarajah 4b – Arah B
3 2 2 3
1 4 4 1
Mula Mula
Gambarajah 4: Illustrasi Ujian Lompat Kudron
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BATTERI UJIAN UNTUK KENALPASTI BAKAT –PRESTASI MOTOR (Australian Talent
Identification Test – AIS Test)
Ujian 1: Baling / Lontar Bola Keranjang
Tujuan : Tugasan lontar bola keranjang di bentuk untuk mengukur kekuatan bahagian atas badan.
Alatan: Bola keranjang saiz 7, pita ukur 15 meter
Prosedur:
1. Pelajar duduk dengan kaki melunjur ke hadapan manakala punggung, belakang badan dan kepala
bersandar ke dinding.
2. Pelajar membuat hantaran bola paras dada ke hadapan sejauh yang boleh. Hantaran sebelah
tangan atau hantaran paras bahu adalah tidak dibenarkan.
3. Pastikan pelajar mengekalkan kepala, bahu dan punggung bersentuhan dengan dinding ketika fasa
lajak dan bola di baling hanya menggunakan otot tangan dan bahu.
4. Benarkan dua percubaan untuk setiap pelajar.
Pemarkahan: Rekodkan jarak balingan terjauh yang menghampiri 5cm (ukur dari tempat jatuhnya
bola pada lantunan pertama)
Percubaan Jarak 1
2
Skor terbaik: ___________
Ujian 2: Lompat Menegak
Tujuan: untuk mengukur keupayaan melompat pada arah menegak.
Alatan: Serbuk kapur (bedak atau tepung juga boleh digunakan), dinding belapis dengan ukuran
ketinggian dari 150 hingga 350 cm (tepat untuk 1 cm)
Prosedur:
1. Pelajar mencelupkan hujung jari tangan dominan ke dalam serbuk kapur.
45
2. Tangan yang tidak digunakan diletakkan di pinggul.
3. Pelajar berdiri di sebelah sisi dominan berhampiran dengan dinding berlapis dengan tangan tegak
ke atas dan menyentuh dinding berlapis dengan jari tengah untuk memberi kesan pada titik
tertinggi. Kaki pelajar berkeadaan rata menyentuh lantai dan tangan ditegakkan setinggi yang
boleh. Rekodkan titik yang ditanda pada 1 sm yang terhampir (Titik A).
4. Tangan pelajar berkedudukan tetap seperti di atas (tangan dominan diangkat tegak dan tangan
tidak dominan diletakkan di atas pinggang) ketika pelajar mencangkung. Pelajar boleh memilih
ketinggian cangkungan dan dibenarkan ‘menghenjut’ sekiranya perlu. Pelajar tidak dibenerkan
untuk menghayun tangan untuk menambah momentum.
5. Pelajar melompat ke atas dari posisi ini dengan tangan ditegakkan ke atas untuk menyentuh
dinding pada paras tertinggi yang boleh (Titik B).
6. Benarkan dua percubaan untuk setiap pelajar.
Pemarkahan: Rekodkan sentuhan tertinggi ketika berdiri (titik A) dalam sentimeter terhampir.
Rekodkan ketinggian yang di tanda oleh jari ketika lompatan (titik B). Tolak titik B dengan titik A
untuk memperolehi jarak lompatan menegak dalam sentimeter.
Percubaan Titik A Titik B Skor (titik B- titik A) 1
2
Skor terbaik: ___________
Ujian 3: 40 Meter Pecut
Tujuan: Untuk mengukur keupayaan pecutan
Alatan: Jam randik, kon (10), 40m trek larian – tegak dan sama rata. Sekiranya menggunakan padang
rumput, pastikan ia kering.
Prosedur:
1. Tanda trek larian 40 meter dengan meletakkan skitel/kon pada jarak setiap10 meter antara satu
dengan yang lain. Pelajar bermula dengan posisi berdiri dengan kaki hadapan berada di atas
garisan.
2. Penjaga masa mestilah berdiri di garisan penamat dengan satu tangan dinaikkan tinggi dan
memberikan arahan ‘sedia’ dan menurunkan tangan secepat mungkin sebagai tanda permulaan
46
larian (tidak perlu memanggil mula). Pada masa tangan diturunkan, penguji memulakan masa jam
randik yang dipegang oleh tangan sebelah lagi.
3. Berhentikan masa jam randik apabila badan pelajar melepasi garisan penamat. Galakkan pelajar
untuk berlari sepantas yang boleh.
4. Benarkan dua percubaan untuk setiap pelajar.
Pemarkahan: Rekodkan masa yang diambil pada percubaan yang terbaik pada 0.1 saat yang
terhampir
Percubaan Masa (saat) 1
2
Percubaan terbaik: ___________
Ujian 4: Larian Ulang-Alik- Ujian Kecergasan Pelbagai Peringkat
Tujuan: Ujian lari ulang alik (ujian kecergasan pelbagai peringkat) digunakan untuk mengukur
kecergasan aerobik.
Alatan: Keset/CD untuk larian ulang alik, radio kaset/pemain cakera padat , pita penanda, permukaan
rata berukuran 20 m dan bertanda, jam randik, kon (4) dan borang skor lari ulang-alik.
Prosedur:
1. Periksa kepantasan kaset dengan menggunakan kalibrasi satu minit dan ubahsuaikan jarak larian
jika perlu (diterangkan dalam keset dan manual ujian).
2. Ukur jarak 20 m dan tanda dengan pita penanda dan kon.
3. Mainkan pita kaset. Arahkan pelajar untuk berlari kearah bertentangan dan letakkan satu kaki
dibelakang garisan untuk menunggu bunyi ‘beep’. Sekiranya pelajar tiba awal dari bunyi, pusing
dan tunggu sehingga bunyi ‘beep’ dan lari ke garisan bertentangan dalam masa yang ditetapkan
dan tunggu untuk bunyi seterusnya.
4. Di setiap penghujung minit, jarak masa antara beep berkurangan bermakna kepantasan larian
meningkat secara berperingkat-peringkat.
5. Pastikan pelajar sampai ke garisan setiap masa dan tidak berpusing sebelum garisan. Ingatkan
pelajar untuk sampai ke garisan dan berpusing berbanding lari dan berpusing yang mana ini akan
mengambil masa yang lebih panjang.
47
6. Setiap pelajar meneruskan larian selama yang mampu mengikut bunyi/masa yang ditetapkan di
dalam kaset. Pelajar terkeluar dari larian apabila pelajar ketingalan dua tapak ke garisan dari
bunyi ‘beep’ sebanyak dua kali.
Pemarkahan: Rekod tahap terakhir dan peringkat larian ulang alik yang berjaya disempurnakan.
Tahap terakhir dan peringkat larian ulang alik ___________:_________________
48
APPENDIX E
49
50
51
52
53
APPENDIX F
A Brief Description of Test Administration Procedure and Score Sheets
McCarron Assessment of Neuromuscular Development (1982) Test
This section provides a brief description of the McCarron Assessment of Neuromuscular
Development (MAND) test administration. A more comprehensive description of the test protocols is
illustrated in the test manual.
McCarron Assessment of Neuromuscular Development (MAND) consists of ten types of motor tasks.
Five tasks are categories under Fine Motor Tasks and the other five tasks are categories under Gross
Motor Tasks. Below is the list of the tasks:
Fine Motor Tasks Gross Motor Tasks Beads in box Hand strength Beads on rod Finger-nose-finger movements Finger tapping Jumping Nut and bolt Heel-toe tandem walking Rod Slide Standing on one foot
FINE MOTOR TASKS
1. Beads in Box
This task requires the participant to move beads individually from one full box to an empty box
(standard box) with one hand as fast as possible in 30 seconds. The tester demonstrates the tasks and
gives the instructions to the participant. Repeat the procedure with the other hand. The participant can
have an initial practice if necessary.
Instructions:
1. “With your right hand, take one bead at a time from the full box and put it in the empty box.
Go as fast as you can until I tell you to stop. Remember, take only one bead at a time. If you
drop a bead, just let it go and keep on taking beads from the box.”
2. “With your left hand, take one bead at a time from the full box and put it in the empty box.
Go as fast as you can until I tell you to stop. Remember, take only one bead at a time. If you
drop a bead, just let it go and keep on taking beads from the box.”
54
Record only the number of beads that placed into the empty box individually in the time given. The
score for this task is the total of the number of beads correctly transferred by both hands.
2. Beads on Rod
In this task, the participant is required to thread the wooden cylinders beads into a rod. The non-
preferred hand holds the bottom of the rod firmly while the preferred hand places the beads
individually as fast as possible on the rod in 30 seconds. Both hands should not rest on the table and
free to move to position the beads. Repeat the procedure with the eyes closed. The participant can
have an initial practice if necessary.
Instructions: “Take one bead at a time and place it on the rod like this. Go as fast as you can until I tell
you to stop. Be sure to hold the rod with your arms held a little away from your body.”
Record the number of beads that placed on the rod individually in the time given. The score for this
task is the total of the number of beads correctly transferred with eyes open and closed.
3. Finger Tapping
The participant is required to tap index finger up and down in 10 s. The index finger must touch a
rubber band when moved upward and touch the wooden board when moved downward. The rubber
band should be at the level of index finger when the index finger is parallel to the platform. Repeat the
procedure with the other hand. The participant can have an initial practice if necessary.
Instructions: “Make a fist, but keep your thumb and index finger out. Now move your index finger up
and down so it touches the board and then the rubber band. Do this as fast as you can. Move
only the index finger.”
Observe and record the motor behaviours (tapping rhythm, extraneous hand movements, overflow of
arm movement and inconsistent complete finger tapping) and record the number of taps in the
protocol sheets. The score for this task is the total of the number of the correct taps and observational
ratings for both hands.
4. Nut and Bolt
In this task, the participant is required to turn a large set of bolt into a nut as quickly as possible. The
preferred hand that hold the bolt does the turning while the non-preferred hand that hold the nut is
remain stationary. Both hands are at front without resting on the table or lap. Repeat the procedure
with the small set of bolt into the nut.
Instructions: “Hold the nut in this hand (non preferred) and turn the bolt (with preferred hand) into
the nut. Turn the bolt as fast as you can and turn it all the way down.”
55
Individual task score is derived by subtracting the time required (in seconds) to completely turn the
bolt into the nut from a score of 100. Record the scores by summing those for the two different sizes.
5. Rod slide
This task requires the participant to continuously move a peg to the body as slowly as possible on the
slide apparatus by using the hand and arm muscle. The participant is in standing position to perform
the task with the apparatus at the waist level and approximately 30 cm away. The task is performed by
both hands, left to right by the left hand and right to left by right hand.
Instructions: “Thus far, you have been asked to do the motor tasks as fast as you can. This time, we
are going to do something different. I want you to do this task as slowly as you can.
Remember, the slower, the better. Move the pegs as slowly as you can, like this.”
Record the time required to move the peg on the rod slide between the two ends of the apparatus. The
participant may take longer time to complete the task as the slower the performance the better the
score. However a maximum score of 30 seconds for each hand is allowed. Behavioural movement
rating score of rate of movement, distractibility, head-body shifting, and extraneous body movements
are also included. Sum both right and left hand score to get the total score for this task.
GROSS MOTOR TASKS
1. Hand Strength
This task requires the participant to holds the dynamometer with the arm out straight in front at
shoulder level and squeeze the dynamometer as hard as possible. Each hand has two trials and
alternating between trials. The best of two trials for each hand is recorded in kilograms.
Instructions: “Squeeze the handle as hard as you can.”
2. Finger-Nose-Finger Movements
In this task, the participant is required to touch his/her index finger on the tip of extended finger of the
other hand and move to contact the tip of nose in10 seconds interval or approximately five nose
contacts. The subject repeat the task using both right and left hands for the movement with eyes open
and eyes closed making this task consists of four trials.
Instructions: “This is not a speed test; just relax and do the best you can. Put your left arm straight out
in front of you and point your finger to the wall. Now point just your finger to the wall on
the right. (The left index finger is at right angles to the arm and points to the right or
working side of the body). With the index finger of the right hand, touch the tip of your nose
and then the tip of the extended finger like this.”
56
Behavioural ratings of arm movement, index finger on the extended hand, contact points, bending of
elbow and indenting are recorded. Total score is the sum of scores with eyes open and eyes closed.
3. Jumping
The participant jumps for a maximum distance with both feet together behind the restraining line for
this task. Qualitative behaviours that are spring of legs, use of arms, trunk balance, and landing are
assessed. Total score is the sum of the distance jumped and rating observed behaviours.
Instructions: “With both feet together, jump across the room as far as you can.”
4. Heel-Toe Walk
In this task, the participant is required to walk forwards on a 10 feet straight line by placing the heel of
the forward foot in front of the toes of the rear foot and vice versa for movement backwards. The
participant is required to wear flat shoes, barefooted or in stocking to perform the tasks and hands
resting on the hips.
Instructions:
1. Forward “Relax and do this task as carefully as you can. Put both your hands on your hips and
walk on the line placing your heel directly in front of your toes, touching heel to toe on each
step. Start here (one end of the tape) and walk all the way to the other end.
2. Backward “This time, walk backwards. Keep both hands on your hips and walk on the line
placing your toe directly behind the heel, touching toe to heel on each step.”
Behavioural movement of arm, feet, heel toe distance, progression and feet placement are observed,
rated and recorded. Total score is the sum of the forward and backward walk.
5. Standing On One Foot
The participant is required to keep balance while stand on one foot for a maximum period of 30 s. The
participants are allowed to move their arms to maintain balance. Trials are conducted for both right
and left legs with eyes opened and closed. Start the time once one leg is lifted off the floor and stop
when the participant begins to hop, or either the lifted leg or hands touch the floor. A second trial will
be given to the participant if participant unable to maintain the balance for more than 10 s.
Instructions:
1. Eyes open “Stand on one foot as long as you can or until I tell you to stop.”
2. Eyes closed “This time, stand on one foot with your eyes closed. Remember, you must keep
your eyes closed tight.”
Record the time of balance in seconds. The total score is the sum of time of left and right legs balance
with eyes open and closed.
57
McCarron (MAND) Assessment of Neuromuscular Development
Score Sheet NDI __________
NAME : ________________________________________ DATE : _____________
DATE OF BIRTH : ____________________ AGE OF TESTING : _____________
SEX : Male / Female PREFERRED HAND: Right / Left
PREFERRED LEG: Right / Left
HEIGHT : _____ WEIGHT : ______DIAGNOSIS: _____________________________
BEADS IN BOX (number placed in
30 seconds)
Right ____ Left ____ Total
_____ Scaled score
_____
BEADS ON ROD (use cylinders only
– number placed in
30 seconds)
Eyes Open ____
Eyes Closed ____
Total
_____ Scaled score
_____
FINGER TAPPING (use
score sheet)
Right ____ Left ____ Total
_____ Scaled score
_____
NUT BOLT (number of
seconds to
complete tasks)
Large 100 - ___ = ____
Small 100 - ___ = ____
Total
_____ Scaled score
_____
ROD SLIDE (use
score sheet) Scaled score
_____ Sub Total _____
FINE MOTOR AVERAGE _____
GRIP STRENGTH
(best of two trials
with each hands)
Right ____ Left ____ Total ____ Scaled score
_____
58
FINGER-NOSE-FINGER (use score sheet)
Eyes open ____
Eyes Closed ____
Total ____ Scaled score
_____
JUMPING (use
score sheet) Total ____ Scaled score
_____ HEEL-TOE WALK (use score
sheet)
Forward ___ Backward___ Total ____ Scaled score
_____
ONE FOOT STAND (number of seconds up to 30)
Eyes open Right ____ Left ____ Total _____
Eyes Closed Right ____ Left ____ Total _____
Total _____ Scaled score
_____ Sub Total _____
GROSS MOTOR AVERAGE _____
OVERALL MOTOR AVERAGE _____
59
MAND SCORE SHEET
NAME : ________________________________
PROTOCOL SHEET
FINGER TAPPING
RIGHT LEFT
A. Rhythm ________ ________
B. Extraneous hand movements ________ ________
C. Overflow of movement in arm ________ ________
D. Distance complete ________ ________
E. Number of taps ________ ________
TOTAL ________ ________
ROD SLIDE
RIGHT LEFT
A. Change in speed _______ ________
B. Distractibility _______ ________
C. Head-body shifting _______ ________
D. Extraneous body movement _______ ________
E. Time taken _______ ________
TOTAL _______ ________
FINGER-NOSE-FINGER
EYES OPEN EYES CLOSED
RIGHT LEFT RIGHT LEFT
A. Arm movement smooth _______ _______ _______ ________
B. Index finger steady _______ _______ _______ ________
C. Contact points _______ _______ _______ ________
D. Elbow bending _______ _______ _______ ________
E. Indenting _______ _______ _______ ________
TOTAL _______ TOTAL ________
JUMPING
60
JUMP 1 JUMP 2 JUMP 3
A. Spring _____ _____ _____
B. Use of arms _____ _____ _____
C. Trunk balance _____ _____ _____
D. Landing knees flexed _____ _____ _____
E. Distance landing _____ _____ _____
TOTAL _____ _____ _____
HEEL-TOE WALK
FORWARD BACKWARD
A. Arm position _______ ________
B. Feet on tape _______ ________
C. Heel to toe distance _______ ________
D. Progression smooth _______ ________
E. Parallel placement of feet _______ ________
TOTAL _______ ________
PROTOCOL FOR SCORING FINGER-NOSE FINGER
(Allow a 10 second interval to observe each trial)
EYES OPEN
EYES CLOSED
Right Left Right Left
A Arm movement 4. Smooth, direct arm movement 2. Somewhat irregular or wavery arm
movement
1. Confused and jerky arm movement
B
Index finger on the extended hand 4. Held steady
2. Slight tremor or swaying
1. Marked termor or swaying
61
C
Contact point 4. Contact point at tip of nose and tip of
extended index finger
2. Missed contact point at either tip of nose or
tip of index finger
1. Missed contact points at both tip of nose
and tip of index finger
D
Bending of elbow (gradual movement inward) 4. Holds arm fully extended 2. Slight bend at elbow (less than 30º) 1. Noted bend at elbow (more than 30º)
E
Indenting 4. Lightly touches tip of extended index finger
and end of nose
2. Noted pushing of tip of extended index
finger or presses in end of nose once or
twice
1. Noted pushing in of tip of extended index
finger or presses in the end of nose three or
more times
Totals
62
PROTOCOL FOR SCORING JUMPING: (Body movements are rated according to an
overall impression of typical performance as observed during all three jumps)
A Spring 4. An even spring into the air from both feet 2. An awkward spring into the air, predominant use of one leg to spring
1. Clumsy spring; limited ability to spring off the floor
B Use of arms 4. Arms assist with slight spring forward and return to sides 2. Arms move limpy with limited assistance 1. Arms held rigidly; are not used to assist
C Trunk balance 4. Landing stable; centre of gravity midline (remains in place) 2. Landing unstable but able to regain balance 1. Landing unstable; steps backward or forward or uses hands to prevent
falling
D Landing with knees flexed 4. Smooth landing on both feet simultaneously with slight bending of
knees to absorb the fall
2. Somewhat stiff landing; limited use of knee bend 1. Stiff landing with stiff knees; jars the body when landing
E Distance of jump The distance scored recorded in the farthest jump of the three attempts
Total
63
PROTOCOL FOR SCORING HEEL-TOE WALK
(Individual walks a distance of 10 feet)
FORWARD BACKWARD
A Arms/body sway 4. Both hands remain on hips 2. Removed one hand from hip 1. Removed both hands from hips
B
Feet 4. Retained both feet on tape line 2. Foot altered from line once or twice (when
less than half the tape is covered, the foot is
considered off)
1. Foot altered from line three or more times
C Heel to toe distance 4. Heel positioned within one inch of the toe 2. Heel positioned greater than one inch from toe
once or twice
1. Heel positioned greater than one inch from toe
three or more times
D Progression 4. Smooth forward walk 2. Slight pauses in forward movement 1. Shifting of weight backward and forward
while walking
E Parallel placement 4. Both feet kept parallel to the tape line 2. Steps correctly, but then rotates foot to an
angle (20º or more) with the line
1. Steps at an angle (20º or more) with the line
64
Totals
Parallel Placement Rotation
20º
65
FINGER TAPPING SCORING PROTOCOL: (Observe for 10s interval with each hand)
RIGHT LEFT
A Rhythm of tapping 4. Even, consistent rhythm of tapping 2. 1-2 disruptions of rhythm, but regains consistent tapping
1. Erratic, non-rhythmic tapping
B Extraneous hand movements 4. Moves only the index finger, fist remain closed 2. Extraneous movement of thumb 1. Extraneous movement of thumb and other fingers
C Overflow of movement in arm 4. Wrist or forearm remains stationary while tapping 2. Occasional (1-2) movement of wrist or forearm to “assist”
tapping
1. Frequent (≥3) movement of wrist or forearm to “assist”
tapping
D Complete distance 4. Index finger move the complete distance between base and
suspended rubber band
2. Occasional (once or twice) incomplete movement between
base and rubber band
1. Frequent (three or more) incomplete movements of the index
finger between base and rubber band
E Number of complete finger taps in ten seconds Do not count incomplete movements or contacts made by
movements of wrist or forearm
Total
66
PROTOCOL FOR SCORING ROD SLIDE
(Observations during movement of the right and left hands)
The individual stands approximately one foot away from the rod slide and the height of the rod is at
waist level.
RIGHT LEFT
A Impulsive-jerky movements (change in rate of speed) 4. Continuous even slide 2. Changes in slide motion; obvious deviation in speed 1. Changes in slide motion; obvious deviation in speed with
erratic and impulsive movement
B Distractibility 4. Attended to ask without distraction (eyes remains focused
on bead during slide)
2. Distracted by extraneous stimuli (eyes shifted from focus
once during slide)
1. Distracted by extraneous stimuli (eyes shifted from focus
two or more times during slide)
C Head-body shifting 4. Head and body remain stationary while the eye track the
bead; the movement of the eyes parallels the movement
of the bead
2. Limited tracking movement of eyes with turning of head
or partial shifting of body to follow the bead
1. Simultaneous shifting of body while tracking the bead;
the body or head, rather than the eyes, shifts past the
midline
D Extraneous body movements 4. Body posture relaxed and stationary; moves only the arm
performing the task
67
2. Extraneous movements of other arm and legs once during
the task
1. Extraneous movements of other arm and legs two or more
times during the task
E Speed of movement (up to 30 seconds) Record the time taken to move the bead the full distance across
the rod. The maximum possible score for each hand is 30
seconds. When the speed of movement is 5 seconds or less,
record a score of “1” for each of the behavioural observations
above (A, B, C and D).
Totals
68
Balance and Movement Coordination (BMC) Test
BMC - MOVEMENT CONTROL AND COORDINATION TEST
Test 1: One foot balance with elevated hand or arms above head.
Objective: To measure static balance of the performer supported on one foot while hands holding a
rod above the head. The balance will be done on each foot with eyes open and eyes closed.
Equipment and Materials: Stopwatch, 30cm ruler and firm level surface.
Procedures: Hands straight above head and holding ruler about shoulder width. Stand on one foot
and lift up the other foot to make an angle at the knee approximately 60 to 90 degrees. With eyes
open, maintain the balance as long as possible until asked to stop. Then, repeat on the other foot.
Following this, the test is repeated on each leg with eyes closed. Start timing the trial when the
performer lifts up the free foot. Stop timing when the performer hops or slides or repositions the
supporting foot, or the free foot touches the floor, or one of the hands slip off the ruler, or after the
performer maintains the balance for 60 seconds. If a trial lasts less than 10 seconds, it is repeated and
the better score is taken.
Instruction: When you are ready to start, lift your leg and balance for as long as you can keeping
your hands stretched above your head.
Scoring: Record the time in seconds. The total score is derived from sum of seconds of each foot able
to balance with eyes opened and eyes closed.
Eyes opened Eyes closed Total (seconds)
Left foot
Right foot
Total
69
Test 2: Dynamic balance – sideways
Objective: To measure the dynamic balance of the performer while jumping sideways.
Equipment and Materials: Stopwatch, marking tape to mark a straight line on the floor.
Procedures: Performer stands next to a straight line marking tape on the floor. With their legs
together, the performer stands sideways next to the line. On the signal “go”, the performer jumps
sideways back and forth in 10 seconds over the line with legs together as fast as she/he can. The
performer should jump with both feet at the same time. Jumping with one leg is not allowed (See
Figure 1).
Scoring: Numbers of jumps in 10 seconds with both feet together. Do not count jumps with one leg or
jumps that touch the line.
Dynamic balance task
Numbers of jumps
Side jump
Figure 1: Diagram of Dynamic Balance Test
Test 3: Shuttle run - with object and without object.
Objective: To measure the ability of the performer to coordinate and control body movements on
changing directions.
Equipment and Materials: Stopwatch, marking tape to mark two parallel lines 5m apart, and two
blocks of wood 5 x 5 x 10 cm placed behind the line 5m from the starting line.
70
Procedures:
Task 1: The performer stands behind the starting line. On the signal ‘go’, performer runs to the
blocks at the other line and touches the ground on the far side of the line with one foot,
picks up one of the blocks, and runs back to the starting line placing the block behind the
line. The performer repeats the processes with the second block then run as fast as possible
across the finishing line. (See Figure 2)
Task 2: Repeats the run as describe above without picking up any object with rest allowed between
tasks.
Scoring: The length of time to complete the tasks is recorded in seconds.
Task 1 Task 2
Seconds
Finishing line
Starting line
Figure 2: Diagram of Shuttle Run Test
Test 4: Hop – stationary and speed.
Objective: To measure the ability of the performer to coordinate and control body movement on
changing directions.
Equipment and Materials: Stopwatch, two cones, marking tape.
1 2 3
71
Procedures:
Task 1: Stationary hop -performer hops continuously as many times as possible on one foot within a
50 cm marked squared in 10 seconds. Count each hop. Stop counting if any part of the foot
either steps onto or protrudes over the line or if the performer stops hopping. Repeat the
task with the other foot.
Task 2: Hopping for speed – the performer stands behind the starting line. On the signal ‘go’, the
performer hops to the other line (10-meter distance) as fast as she/he can. Stop the time
when the hopping foot touches the finishing line. Repeat the task with the other foot with
rest between tasks.
Scoring:
Task 1: The number of hops in time given.
Task 2: The time in seconds to complete the tasks.
Left foot Right foot Total
Task 1 (number of hop)
Task 2 (seconds)
Test 5: Zig-zag run – right and left direction.
Objective: To measure the ability of the performer to coordinate and control body movement on
changing directions.
Equipment and Materials: Stopwatch, Skittle/cones – 5
Procedures: Placed five skittle/cones in one straight line with 1.5m in between of each skittle/cones
(see Figure 3). The performer stands behind the starting line. On the signal ‘go’, the performer runs to
the left of first skittle, then to the right of second skittle, to the left of the third skittle, to the right of
the fourth skittle, to the left of fifth skittle, then turns back and runs again to the left and right of the
skittles alternately and runs to the finishing line as fast as she/he can. Time the run to complete the
task in seconds. Repeat the task from different starting line (to change the run direction) - see Figure
3a and 3b.
72
Scoring: Time in seconds
Right direction Left direction
Zig-zag run (seconds)
Figure 3a
Right direction
10 meter
start/finishing line start/finishing line
Figure 3: Diagram of Zigzag Run Test
Test 6: Quadrant jump – different directions
Objective: To measure the ability of the performer to coordinate and control body movement by
jumping in different directions.
Equipment and Materials: Stopwatch, masking tape
Procedures: Place two cross lines on hard surface (see Figure 4a and 4b). The performer stands on
the start position. On the signal ‘go’, the performer jumps with both feet into quadrant 1, then into
quadrant 2, into 3, into 4, and back to 1. Performer continues jumping until asked to stop. Count the
jumping. Repeat the jumping in a different sequence (see Figure 4b). Two trials for each direction are
given with rest between trials.
Scoring: The score is the number of times the feet land in correct quadrants in 10 seconds. Deduct
half (0.5) point each time performer lands on wrong quadrant or any part of the feet land on the line.
The better score of the two trials is the test score.
Figure 3b Left direction
73
Trial 1 Trial 2 Best trials
Numbers of
correct
landings
Numbers of
errors Numbers of
correct
landings
Numbers
of errors
Direction A
Direction B
Figure 4a Figure 4b
3 2 2 3
1 4 4 1
Start Start
Figures 4a and 4b. Diagrams of the Quadrant Jump Test
74
TEST BATTERY IN TALENT IDENTIFICATION PROGRAM - MOTOR PERFORMANCE
(Australia Sports Commission, 1998)
Test 1: Basketball Throw
Purpose: The basketball throw task is designed to measure upper body strength.
Equipment: Size 7 basketball, 15 meter tape measure
Procedures:
5. The student sits with their buttocks, back and head resting against a wall. Their legs rest on the
floor horizontally in front of the body.
6. The student uses a two-handed chest pass to push the ball in the horizontal direction as far
forward as possible. A one arm or shoulder pass is not allowed.
7. Ensure that the student keeps the head, shoulders and buttocks in contact with the wall as they
follow through, and the ball is thrown only using the arm and shoulder muscles.
8. Allow two trials for each student.
Scoring: Record the longest distance thrown to the nearest 5 cm (measure from the base of the ball
where it makes contact with the ground on the first bounce).
Trials Distance 1
2
Best score: ___________
Test 2: Vertical Jump
Purpose: The vertical jump task measures the ability to spring in a vertical direction.
Equipment: Powder chalk (talcum powder or flour is appropriate), Wall mounted board covering
heights from 150 to 350 cm (accurate to 1 cm).
75
Procedures:
1. The student dips the fingertips of the preferred hand into the powder chalk.
2. The non-preferred hand is placed on the hip.
3. The student stands with the preferred side nearest the board and reaches upward with their arm
closest to the wall and touches the board with their middle finger to leave a mark at the highest
possible point. The feet should be flat on the floor and the arm/hand extended as high as possible.
Record the position of this mark to the nearest 1 cm (Point A).
4. The student’s arms are to remain in the same position as above (the preferred arm is raised
vertically and the non-preferred arm placed on the hip) as they go into a crouch. The student can
choose the depth of crouch and is allowed to ‘bounce’ if desired. The student is not allowed to
swing the arms to assist momentum.
5. The student then springs upward from this position to touch the wall at the highest possible point
with the outstretched arm closest to the board (Point B).
6. Allow two trials for each student.
Scoring: Record the reaching height to the nearest cm. Record the final height (to the nearest cm) the
student jumped on the best trial. Subtract the reaching height from the vertical jump height to obtain
the vertical jump distance in centimeters.
Trials Point A Point B Score (Point B – Point A) 1
2
Best score: ___________
Test 3: 40 Metre Sprints
Purpose: Speed is also important in sports requiring short bursts of activity at high intensity such as
sprint running.
Equipment: Stopwatch, Skittle/cones (10), 40 metre running track that is straight, level and placed
cross-wind. If a grass surface is used ensure that it is dry.
Procedures:
5. Mark a 40-meter running track with witches’ hats placed at 10-meter intervals. The student starts
in a standing position with their front foot exactly on the line.
76
6. The timer should stand at the finish line with one arm held high, call ‘ready’ and then sweep
down their arm quickly to start the student (do not call go). As the arm sweeps down the tester
should simultaneously start the stopwatch which is held in the descending hand.
7. Stop the stopwatch when the student’s chest crosses the line. Emphasize to the student to run as
quickly as possible.
8. Allow two trials for each student.
Scoring: Record the time taken for the fastest trial to the nearest 0.1 of a second.
Trials Time 1
2
Best score: ___________
Test 4: Shuttle Run – Multistage fitness test
Purpose: The shuttle run (multistage fitness test) is used to assess aerobic fitness.
Equipment: Cadence audio CD/tape for shuttle run ,Masking tape ,Cassette player, 20 m marked
distance on a surface that is flat, even and slip resistant, Stopwatch, skittle/cone (4) and Shuttle run
record form.
Procedures:
1. Check the speed of the cassette player using the one minutes calibration period and adjust the
running distance if necessary (this is described on the tape and in the tape manual).
2. Measure the 20 m distance and mark with tape and witches hats.
3. Start the cadence audio tape. Instruct the student to run to the opposite end and place one foot
behind the line by the time the next beep sounds. If they arrive before the beep they should turn
(pivot) and wait for the signal, then run to the opposite line to reach this in time for the next
signal.
4. At the end of each minute the time interval between beeps is decreased, thereby running speed
becomes progressively faster.
77
5. Ensure the student reaches the end line each time and does not turn short. Emphasize to the
student to pivot and turn rather than run an arc which some tend to do (this takes more time).
6. Each student continues running for as long as possible until he/she can no longer keep up with the
tape. The criterion for eliminating a student is two lengths in a row where he/she is more than two
steps from the end.
Scoring: Record the last level and shuttle the student successfully completed.
Last Level and Shuttle: ___________
78
APPENDIX G
Table 1 Conversion of Summed Scaled Scores to NDI for Children - Ages ≥11 years
NDI Summed
Scale NDI Summed
Scale NDI Summed
Scale NDI Summed
Scale 155 134 126 119 97 94 68 51-52
154 125 118 96 93 67 49-50
153 133 124 117 95 92 66 47-48
152 123 116 94 91 65 45-46
151 132 122 115 93 89-90 64 43-44
150 121 114 92 88 63 41-42
149 131 120 113 91 87 62 39-40
148 119 112 90 86 61 37-38
147 130 118 111 89 85 60 35-36
146 117 110 88 84 59 33-34
145 129 116 87 83 58 31-32
144 115 109 86 82 57 29-30
143 128 114 108 85 81 56 27-28
142 113 107 84 80 55 25-26
141 127 112 106 83 79 54 23-24
140 111 105 82 78 53 21-22
139 126 110 104 81 77 52 19-20
138 109 103 80 75-76 51 18
137 125 108 102 79 73-74 50 17
136 107 78 71-72 49 15-16
135 124 106 101 77 69-70 48 13-14
134 105 100 76 67-68 47 12
133 123 104 75 65-66 46 11
132 103 99 74 63-64 45 10
131 122 102 73 61-62 44 8-9
130 101 98 72 59-60 43 6-7
129 121 100 97 71 57-58 42 4-5
128 99 96 70 55-56 41 2-3
127 120 98 95 69 53-54 40 1
79
TABLE 2
MAND Table of Norms for Normal Children
AGE 12-0
Scale
Score Beads
Box Beads
Rod Finger
Tapping Nut and
Bolt Rod Slide Hand
Strength Finger
Nose Jumping Heel Toe Stand
One Foot 20 65-66+ 31+ 120+ 194+ 63-64+ 95+ 19 64 30 117-119 191-193 62 92-94
18 62-63 29 113-116 189-190 60-61 90-91 17 60-61 28 110-112 186-188 58-59 87-89
16 59 27 107-109 183-185 57 84-86 15 57-58 26 103-106 181-182 55-56 82-83
14 55-56 25 100-102 178-180 53-54 79-81 40 13 54 24 97-99 175-177 92 52 79-80 76-78 39 120 12 52-53 23 93-96 173-174 90-91 50-51 76-78 74-75 38 114-119 11 50-51 22 90-92 170-172 88-89 48-49 73-75 71-73 37 107-113 10 49 21 87-89 167-169 86-87 47 70-72 68-70 36 100-106 9 47-48 20 83-86 165-166 84-85 45-46 67-69 66-67 35 94-99 8 45-46 19 80-82 162-164 82-83 43-44 64-66 63-65 34 87-93 7 44 18 77-79 159-161 80-81 42 61-63 60-62 33 80-86 6 42-43 17 73-76 157-158 78-79 40-41 58-60 58-59 32 74-79 5 40-41 16 70-72 154-156 76-77 38-39 55-57 55-57 31 67-73 4 39 15 67-69 151-153 74-75 37 52-54 52-54 30 60-66 3 37-38 14 63-66 149-150 72-73 35-36 49-51 50-51 29 54-69 2 35-36 13 60-62 146-148 70-71 33-34 46-48 47-49 28 47-53 1 34 12 57-59 143-145 68-69 32 43-45 44-46 27 40-46
80
TABLE 3
MAND Table of Norms for Normal Children
AGE 13-0
Scale
Score Beads
Box Beads
Rod Finger
Tapping Nut and
Bolt Rod Slide Hand
Strength Finger
Nose Jumping Heel Toe Stand
One Foot 20 67+ 31 130+ 196+ 87+ 94+ 19 65-66 30 126-129 193-195 83-86 92-93
18 64 29 122-125 191-192 80-82 80 89-91 17 62-63 28 118-121 188-190 76-79 79 87-88
16 60-61 27 114-117 185-187 72-75 78 85-86 15 59 26 110-113 183-184 92 69-71 77 82-84
14 57-58 26 106-109 180-182 91 65-68 77 80-81 13 55-56 25 102-105 177-179 90 61-64 76 78-79 40 120 12 54 24 98-101 175-176 89 58-60 75 75-77 38-39 115-119 11 52-53 23 94-97 172-174 88 54-57 74 73-74 37 108-114 10 50-51 22 90-93 169-171 87 50-53 73 71-72 36 101-107 9 49 21 86-89 167-168 86 47-49 72 68-70 34-35 95-100 8 47-48 20 82-85 164-166 85 43-46 71 66-67 33 88-94 7 45-46 19 78-81 161-163 84 39-42 70 64-65 32 81-87 6 44 18 74-77 159-160 83 36-38 69 61-63 30-31 75-80 5 42-43 18 70-73 156-158 82 32-35 68 59-60 29 68-74 4 40-41 17 66-69 153-155 81 28-31 67 57-58 28 61-67 3 39 16 62-65 151-152 80 25-27 66 54-56 26-27 55-60 2 37-38 15 58-61 148-151 79 21-24 65 52-53 25 48-54 1 35-36 14 54-57 145-147 78 17-20 64 50-51 24 41-47
81
TABLE 4
MAND Table of Norms for Normal Children
AGE 14-0
Scale
Score Beads Box Beads
Rod Finger
Tapping Nut and
Bolt Rod
Slide Hand Strength Finger
Nose Jumping Heel
Toe Stand
One Foot
Male Female Male Female 20 68+ 31 133+ 197+ 95+ 76+ 104+ 92+
19 66-67 30 129-132 194-196 91-94 74-75 101-103 90-91 18 65 29 125-128 192-193 88-90 71-73 98-100 87-89
17 63-64 28 121-124 189-191 84-87 69-70 95-97 85-86 16 61-62 27 117-120 186-188 80-83 67-68 80 92-94 83-84
15 60 26 113-116 184-185 77-79 64-66 79 89-91 80-82 14 58-59 26 109-112 181-183 92 73-76 62-63 79 86-88 78-79
13 56-57 25 105-108 178-180 91 69-72 60-61 78 83-85 76-77 12 55 24 101-104 176-177 90 66-68 57-59 77 80-82 73-75 39-40 116-120 11 53-54 23 97-100 173-175 89 62-65 55-56 76 77-79 71-72 38 109-115 10 51-52 22 93-95 170-172 88 58-61 53-54 75 74-76 69-70 37 102-108 9 50 21 89-92 168-169 87 55-57 50-52 74 71-73 67-68 35-36 96-101 8 48-49 20 85-88 165-167 86 51-54 48-49 73 68-70 65-66 34 89-95 7 46-47 19 81-84 162-164 85 47-50 46-47 72 65-67 62-64 33 82-88 6 45 18 77-80 160-161 84 44-46 43-45 71 62-64 59-61 31-32 76-81 5 43-44 18 73-76 157-159 83 40-43 41-42 70 59-61 57-58 30 69-75 4 41-42 17 69-72 154-156 82 36-39 39-40 69 56-58 55-56 29 62-68 3 40 16 65-68 152-153 81 33-35 36-38 68 53-55 52-54 27-28 56-61 2 38-39 15 61-64 149-151 80 29-32 34-35 67 50-52 50-51 26 49-55 1 36-37 14 57-60 146-148 79 25-28 32-33 66 47-49 48-49 25 42-48
82
TABLE 5
MAND Table of Norms for Normal Children
AGE 15-0
Scale
Score Beads
Box Beads
Rod Finger
Tapping Nut and
Bolt Rod
Slide Hand Strength Finger
Nose Jumping Heel
Toe Stand
One
Foot
Male Female Male Female 20 72+ 31 136+ 107+ 79+ 99+ 92+
19 70-71 30 132-135 103-106 77-78 97-98 90-91 18 68-69 30 128-131 99-102 74-76 95-96 87-89
17 66-67 29 124-127 197-199 95-98 72-73 92-94 85-86 16 64-65 28 120-123 193-196 91-94 70-71 90-91 83-84
15 62-63 27 116-119 190-192 87-90 67-69 87-89 80-82 14 60-61 26 112-115 186-189 83-86 65-66 85-86 78-79
13 58-59 25 108-111 182-185 92 79-82 63-64 80 83-84 76-77 12 56-57 25 104-107 179-181 91 75-78 60-62 79 80-82 73-75 40 118-
120 11 54-55 24 100-103 175-178 90 71-74 58-59 78 78-79 71-72 39 111-
117 10 52-53 23 96-99 171-174 89 67-70 56-57 77 76-77 69-70 38 104-
110 9 50-51 22 92-95 168-170 88 64-66 53-55 76 73-75 66-68 36-37 98-103 8 48-49 21 88-91 164-167 87 59-63 51-52 75 71-72 64-65 35 91-97 7 46-47 20 84-87 160-163 87 55-58 49-50 74 69-70 62-63 34 84-90 6 44-45 20 80-83 157-159 86 51-54 46-48 73 66-67 59-61 32-33 78-83 5 42-43 19 76-79 153-156 85 47-50 44-45 73 64-65 57-58 31 71-77 4 40-41 18 72-75 149-152 84 43-46 42-43 72 62-63 55-56 30 64-70 3 38-39 17 68-71 146-148 83 39-42 39-41 71 59-61 52-54 28-29 58-63 2 36-37 16 64-67 142-145 82 35-38 37-38 70 57-58 50-51 27 51-57 1 34-35 15 60-63 138-141 81 31-34 35-36 69 55-56 48-49 26 44-50
83
TABLE 6
MAND Table of Norms for Normal Children
AGE 16-0
Scale
Score Beads
Box Beads
Rod Finger
Tapping Nut and
Bolt Rod
Slide Hand Strength Finger
Nose Jumping Heel
Toe Stand
One Foot
Male Female Male Female 20 70+ 32 139+ 199 112+ 83+ 108+ 92+
19 68-69 31 135-138 196-198 108-111 81-82 105-107 90-91 18 67 30 131-134 194-195 105-107 78-80 102-103 87-89
17 65-66 29 127-130 191-193 101-104 76-77 99-101 85-86 16 63-64 28 123-126 188-190 97-100 74-75 96-98 83-84
15 62 27 119-122 186-187 94-96 71-73 93-95 80-82 14 60-61 27 115-118 183-185 90-93 69-70 90-92 78-79
13 58-59 26 111-114 180-182 92 86-89 67-68 80 87-89 76-77 12 57 25 107-110 178-179 91 83-85 64-66 79 84-86 73-75 40 118-120 11 55-56 24 103-106 175-177 90 79-82 62-63 78 81-83 71-72 39 111-117 10 53-54 23 99-102 172-174 89 75-78 60-61 77 78-80 69-70 38 104-110 9 52 22 95-98 170-171 88 72-74 57-59 76 75-77 66-68 36-37 98-103 8 50-51 21 91-94 167-169 87 68-71 55-56 75 72-74 64-65 35 91-97 7 48-49 20 87-90 164-166 86 64-67 53-54 74 69-71 62-63 34 84-90 6 47 19 83-86 162-163 85 61-63 50-52 73 66-68 59-61 32-33 78-83 5 45-46 19 79-82 159-161 84 57-60 48-49 72 63-65 57-58 31 71-77 4 43-44 18 75-78 156-158 83 53-56 46-47 71 60-62 55-56 30 64-70 3 42 17 71-74 154-155 82 50-52 43-45 70 57-59 52-54 28-29 58-63 2 40-41 16 67-70 151-153 81 46-49 41-42 69 54-56 50-51 27 51-57 1 38-39 15 63-66 148-150 80 42-45 39-40 68 51-53 48-49 26 44-50
84
APPENDIX H
Confirmatory Factor Analyses of The MAND
In all confirmatory factor analyses, the motor skills were loaded on the appropriate factors,
the measurement errors were not allowed to correlate, and the model was identified by fixing
the factor variances to 1.00. Three overall fit indices suggested by Hoyle and Panter (1995)
were then examined for the confirmatory models.
4.1.1. The MAND
McCarron (1982) developed the MAND to assess fine and gross motor ability at a basic level. The
instrument is primarily used to identify motor problems that underscore neurological dysfunction
within individuals. Therefore, the two factor and four factor psychometric models were examined
which were based upon the instrument development work of McCarron (1982). The first was a 2-
factor model that consisted of the fine and gross motor components originally derived by McCarron
(1982). The second was a 4-factor model that McCarron (1982) reported for a sample of normal
children aged 7 years. In all confirmatory factor analyses, the motor skills were loaded on the
appropriate factors, the measurement errors were not allowed to correlate, and the model was
identified by fixing the factor variances to 1.00. Three overall fit indices suggested by Hoyle and
Panter (1995) were then examined for the confirmatory models. These indices included the chi-
square, which is an absolute fit index that highlights lack of fit resulting from over-identifying
restrictions placed upon the model. The Nonnormed Fit Index (NNFI) was also examined to obtain an
estimate of the relative improvement per degree of freedom of the target model over a baseline model.
Finally, the Comparative Fit Index (CFI) was examined to determine the relative reduction in lack of
fit as estimated by the non-central chi-square of a target model to a baseline model.
The confirmatory factor analysis of the 2-factor model of the MAND proposed by McCarron (1982)
did not fit very well with the Malaysian Adolescent sample. Specifically, the 2-factor model differed
significantly from the independence model (Chi-square = 53.94, Robust Chi-square = 51.75, df = 34,
p < .027) and the other indices of model adequacy were poor. The observed goodness-of-fit (CFI =
.87; Robust CFI = .88; NNFI = .83; Robust NNFI = .77) were substantially lower than would be
desired. A standardised Root Mean Square Residual of .001 was also observed for the 2-factor model.
Most of the 55 residuals were of an acceptable magnitude (90.90% z < |.1|). However, 9.10% of the
residuals were greater than .2. The factor loadings averaged .382 and ranged from .139 to .691. From
a total of 10 motor skills, 4 of these had a variable loading less than .40. The correlation between the
two latent factors revealed a moderately low relationship (r = .405).
85
Confirmatory factor analysis of the four-factor model also suggested that this model was not a good
approximation of the MAND data. Specifically, the four-factor model differed significantly from the
independence model (Chi-square = 71.22, Robust Chi-square = 68.16, df = 29, p < .0001), and the
other indices of model adequacy were also poor. The observed goodness-of-fit (CFI = .73; Robust
CFI = .73; NNFI = .58; Robust NNFI = .41) was lower than would be desired. A standardised Root
Mean Square Residual of .001 was also observed for the four-factor model. Of the 55 residuals,
85.46% were of an acceptable magnitude (z < |.1|). However, 14.54% were larger than .2. The factor
loadings averaged .390 and ranged from .082 to .716. From a total of 10 motor skills, 8 had a variable
loading less than .40. The correlations among the latent factors revealed moderate relationships for
Factor 1 with Factors 2 (r = .413) and 4 (r = .599). Moderate relationships also were found for Factor
3 with Factors 2 (r = .672) and 4 (r = .679). There was a small correlation found between Factor 2 and
Factor 4 (r = .235), and a substantial correlation between Factor 1 and Factor 3 (r = .887).
In summary, the results for both factor models were unsatisfactory, thereby indicating that exploratory
factor analysis was required.
86
APPENDIX I
4.3. RESULTS: TESTING FOR MOTORIC ‘g’ – ALL PARTICIPANTS 4.3.1. First-Order Factor Analysis
A factor analysis was conducted on the normalised T-scores of the combined AIS+BMC. The oblique
rotation (PROMAX) with kappa = 4 was selected, as this number gives better results for further
higher-order factor analyses (Rummel, 1970). Three rotated factors were extracted that accounted for
53.2% of the variance. The eigenvalues, percent of variance, rotated factor loadings, and the
intercorrelation matrix of the AIS+BMC items are presented in Table 10.
Table 6. Correlations, Factors & Factor Loadings Underlying the AIS+BMC for All Participants.
Factor 1 2 3
Eigenvalue 4.64 1.18 1.09
% of variance 35.70 9.08 8.41
Cumulative % 35.70 44.78 53.19
Test 1 2 3 4 5 6 7 8 9 10 11 12 13
1. Shuttle Run - .81 .65 .57 .38 .44 .32 .26 .34 .30 .27 .18 .32 .906
2. Shuttle run/obj. - .59 .55 .34 .38 .27 .23 .31 .28 .22 .15 .27 .905 -.103
3. Hopping speed - .58 .36 .49 .31 .24 .29 .28 .21 .22 .27 .812
4. Zigzag run - .38 .44 .35 .31 .29 .27 .26 .17 .30 .705 .102
5. Multistage fitness test - .40 .23 .20 .22 .12 .22 .13 .20 .530
6. 40m sprint - .32 .24 .27 .18 .27 .22 .25 .528 .127 .156
7. Dynamic balance - .43 .40 .20 .22 .18 .22 .810 .100
8. Hopping-in-square - .23 .20 .17 .15 .17 -.114 .792
9. Quadrant jump - .21 .12 .15 .21 .544
10. Basketball throw - .21 .07 .10 .299 .388 -.334
11. Vertical jump - .09 .13 .264 .314 -.157
12. One foot balance with eyes open - .33 .800
13. One foot balance with eyes closed - .198 .679
Note. Loadings .50 and above are in bold.
87
Factor One
: The variables loaded on this factor were the shuttle run (.906), shuttle run with object,
(.905), hopping speed, (.812), zigzag run, (.705), multistage fitness test (.530) and 40m sprint (.528).
Factor Two
: High loadings in this factor were the dynamic balance (.810), hopping-in-square (.792)
and quadrant jump (.544).
Factor Three
: This factor had loadings on one foot balance with eyes open (.800), and one foot
balance with eyes closed (.679).
4.3.2. Higher-Order Factor Analysis
The higher-order factor analysis extracted one element and accounted for 55.9% of variance in the
solution. The eigenvalues, percentage of variance and factor loadings from the higher-order analysis
are presented in Table 11.
Table 7. Higher-order Factor Analysis of the AIS+BMC for All Participants.
Higher-Order Factor 1
Eigenvalue 1.68
% of variance 55.92
Factors 1 2 3
Factor 1 - .49 .37 .821
Factor 2 - .30 .804
Factor 3 - .597
4.3.3. Discussion – All Participants
First-Order Factor Analysis. There was a small variation in the underlying constructs of the AIS and
BMC tests when analysed separately, and then compared with the underlying constructs found for the
combined AIS+BMC. Specifically, the Anaerobic Power factor found in the factor analysis of the AIS
disappeared as an entity in the combined AIS+BMC analyses. Two of the motor skills that made up
anaerobic power (i.e., the 40m Sprint and the Multistage Fitness Test, [MSFT]) merged with the BMC
construct named movement coordination. The remaining two motor skills that assessed anaerobic
power from the AIS (i.e., the basketball throw and the vertical jump) did not exceed the cut-off point
of .50 for inclusion on any factor found for the combined AIS+BMC. Subsequently, the remaining
eleven motor skill tests loaded on to one of three factors and explained 53.2% of the variance.
88
The First Factor explained 35.7% of the variance. The variables that loaded on to this factor were the
shuttle run, shuttle run with object, hopping speed, zigzag run, multistage fitness test and 40m sprint.
The task analysis on the shuttle run, shuttle run with object, zigzag run and multistage fitness test
items, indicated that these motor skills reflected an ability to change movement direction and the body
position without loss of balance (Hilsendager, Strow & Ackerman, 1969). Hopping speed and 40m
sprint also loaded on this factor, indicating that speed and strength were important components. Both
motor skills are of a high intensity and require maximal rates of energy (Anshel, 1991; Bencke et al.,
2002; Manning et al., 1988). However, hopping at speed requires both speed and strength, and an
ability to maintain balance in a small base of support (Chelly & Denis, 2001; Haywood, 1993a). As a
group then, the motor skills that were loaded not only require speed, balance, agility and
characteristics related to coordination, they also require well timed and well balanced functioning of
several muscles together for successful performance (Barrow, 1977). Given these considerations,
factor one was labelled ‘movement coordination’.
The Second Factor explained 9.08% of variance and consisted of dynamic balance, hopping-in-square
and the quadrant jump. All of these tasks require balance and strength, and involve the capacity to
change body position quickly and accurately (Brown, 2001). According to Kollmitzer et al. (2000),
strength and balance are important components for postural control. Together then, the motor skills
clustering together here require strength, upright position and balance to maintain posture during the
dynamic movements needed to accomplish the tasks (Burton & Davis, 1992; Kollmitzer et al., 2000;
Westcott et al., 1997). Therefore, controlling body posture is an important component in this factor.
Two main functions of posture are ‘to remain inside the supporting surface’ and ‘as a reference frame
for perception and action with respect to the external world’ (Massion, 1994, p. 877). Since these
tasks require control and adaptable force to regulate the posture, this factor was labelled ‘postural
control’ (Burton & Davis, 1992; Kent, 1994).
Factor Three explained 8.41% of variance and had high loadings on one-foot balance with eyes open
and one-foot balance with eyes closed. Both motor skills require individuals to maintain or control
their centres of mass relative to their base of support to prevent falling and complete desired
movements’ (Westcott et al., 1997). Additionally, being able to balance on one leg requires an ability
to focus attention while inhibiting extraneous motor movements. Success in these two motor skills
requires control of extraneous motor movements and one’s centre of mass, especially when the eyes
are closed. Fleishman (1964) suggested that skills measured in situations with eyes open or closed
assesses gross body equilibrium. Since the more important items loaded on this factor reflected an
ability to maintain vertical balance in a static position (Bass, 1939; Burton & Davis, 1992), this factor
was named ‘static balance’.
89
Despite the basketball throw and vertical jump motor skills failing to meet the cut-off value of .50
required for inclusion in a factor, they did load minimally in a direction consistent with those factors.
Both loaded above .31 on postural control. The basketball throw and vertical jump require strength,
upright position and balance to maintain posture whilst performing (Burton & Davis, 1992;
Kollmitzer et al., 2000; Westcott et al., 1997). These characteristics are fundamental to the other
motor skills making up postural control. Also, the basketball throw exhibited a low loading of .33 for
static balance. As with the postural control required for the one-foot balance, the basketball throw
also requires balance ability. The fact that the loading is negative, is probably due to the nature of the
basketball throw which is performed explosively in complete contrast to the very static nature of the
one-foot balance skills.
Higher-Order Factor Analysis. The higher-order factor analysis conducted on the three identified
factors from the combined AIS+BMC (i.e., movement coordination, postural control and static
balance) revealed one element. This higher-order element accounted for 55.92% of the variance, and
the loadings for the first-order factors ranged from .60 to .82. Given that one element encompassed all
of the first-order factors it is suggested that this demonstrates the presence of ‘g’ in motor skill ability,
or general motor ability.
This hierarchical pattern of factors is similar to that found in factor analysis research examining the
existence of general intelligence. For instance, Johnson et al. (2004) demonstrated the existence of a
‘g’ in general intelligence via a second-order confirmatory factor analytic approach of three cognitive
test batteries. The three test batteries were the Comprehensive Ability Battery (CAB: Hakstian &
Cattell, 1975), the Hawaii Battery, including Raven’s Progressive Matrices (HBRAVEN: Defries et
al., 1974; Kuse, 1977), and the Weschler Adult Intelligence Scale (WAIS: Weschler, 1955). What
emerged from this analysis were 13 first-order factors. The first-order factor loadings on the second-
order ‘g’ factor for the CAB test ranged from .50 to .98. For the HBRAVEN test, these ranged from
.46 to .88 and, for the WAIS test, the first-order factor loadings on the second-order ‘g’ factor ranged
from .78 to .88. The correlations between these three ‘g’ factors ranged from .99 to 1.00, thereby
indicating that the concept of ‘g’ as a unitary construct was evident (Johnson et al., 2004). Johnson et
al. (2008) set out to replicate this finding in another sample by examining five cognitive test batteries.
Again, a second-order confirmatory factor analytic approach of the test batteries was undertaken.
Initially, exploratory factor analyses were performed on each of the batteries in order to develop
second-order factor models independently. The batteries were the Test Battery of the Royal Dutch
Navy (see Buros, 1959), the Factored Aptitude Test (see Buros, 1953), the Cattel Culture Fair Test
(see Buros, 1959), the General Aptitude Test Battery (van der Giessen, 1960) and the Groninger
Intelligence Test (Snijders & Verhage, 1962). The first-order factor loadings on the second-order ‘g’
90
factor for the Test Battery of the Royal Dutch Navy test, ranged from .61 to .92; and for the Factored
Aptitude Test these ranged from .46 to 1.00. The Cattel Culture Fair Test reported first-order factor
loadings on the second-order ‘g’ factor ranging from .50 to .73; for the General Aptitude Test Battery,
first-order factor loadings on the second-order ‘g’ factor ranged from .25 to .81; and the first-order
factor loadings on the second-order ‘g’ factor for the Groniger Intelligence Test ranged from .34 to
.90. Finally, the correlations between the five ‘g’ factors ranged from .77 to 1.00, thereby indicating
that the concept of ‘g’ as a unitary construct was evident. The AIS+BMC motor skills, first-order
factors and higher-order element for all participants is illustrated in Figure 4.
Figure 4. The AIS+BMC motor skills, first-order factors and higher-order element.
91
APPENDIX J
6.4. RESULTS: GROUPS DERIVED FROM MAND SCORES – FEMALE ADOLESCENTS Originally, the study aimed to perform discriminant analysis separately on the boys and the girls.
However, owing to the Poor coordination group of the boys failing to meet the recommended
minimum 20 cases per group (Hair et al., 1998), only the adolescent girls were analysed.
The results of the stepwise discriminant analysis revealed that three motor skills entered into the
discriminant function. Discrimination increased with the addition of each motor skill and by the third
step could discriminate between the motor coordination groups. Specifically, the Balance Eyes Closed
entered on the first step Wilks Lambda = .82, F(2, 158) = 17.10, p < .001, the Balance Eyes Open test
on the second step Wilks Lambda = .75, F(4,314) = 12.32, p < .001, the Shuttle Run With Object test
on the third step Wilks Lambda = .70, F(6,312) = 10.06, p < .001.
Two canonical discriminant functions were computed for the AIS+BMC (see Table 36). Both
functions were significant and indicated a strong association between the groups and predictors for
each function. Specifically, the first function produced a Wilks Lambda = .70, with a Chi-square (6) =
55.52, p < .001, and the second function produced a Wilks Lambda = .96, with a Chi-square (2) =
6.85, p < .034. The canonical R2s for the two functions were .27 for the first function and .04 for the
second function. The two discriminant functions accounted for about 89% and about 11%,
respectively, of the between-group variability. An examination of the unstandardised canonical
discriminant functions evaluated at group means reveals that the first function maximally separates
the High coordination group from the Poor group, with the Normal group in between. The second
function discriminates the Normal group from the Poor and High groups.
An examination of the structure correlations for the discriminant analysis did not reveal additional
motor skills that had a substantial effect on discriminating between the three coordination groups
beyond that indicated by the stepwise estimation. Thus, the first discriminant function that the
structure correlations indicated was that the motor skills Balance Eyes Closed, Balance Eyes Open,
and Shuttle Run with Object were able to discriminate between the High coordination group from
both the Normal and Poor coordination groups.
The coordination groups’ performances on these motor skills revealed that the High coordination
group recorded better performances than either the Normal or the Poor coordination groups - Balance
Eyes Closed (Mean = 86.54, SD = 27.08 vs Mean = 56.08, SD = 33.93 and Mean = 37.45, SD = 29.47,
respectively), the Balance Eyes Open (Mean = 119.75, SD = 1.22 vs Mean = 112.54, SD = 15.40 and
Mean = 100.73, SD = 20.68, respectively), and the Shuttle Run With Object test (Mean = 10.58, SD =
92
.93 vs Mean = 11.64, SD = 1.38 and Mean = 11.74, SD = 1.26, respectively). The discriminatory
power of these tests was relatively good, given their respective potency indices.
For the second discriminant function, the motor skills found to discriminate the Normal coordinated
group from the Poor and High coordinated counterparts appears to be the Balance Eyes Open (Mean =
112.54, SD = 15.40 vs Mean = 100.73, SD = 20.68 and Mean = 119.75, SD = 1.22, respectively), and
the Shuttle Run With Object (Mean = 11.64, SD = 1.38 vs Mean = 11.74, SD = 1.26 and Mean =
10.58, SD = .93, respectively).
93
Table 8. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canonical Correlations, Eigenvalues and Group Centroids for the
Three Female Motor Coordination Groups.
Discriminant Function
First Second
SW Value PI SW Value PI
Balance Eyes Closed .58 .77 .53 .28 .17 .00
Shuttle Run NI .46 .19 NI .45 .02
Hopping Speed NI .40 .14 NI .21 .00
Zigzag Run NI .33 .10 NI .28 .01
Multistage Fitness Test NI .33 .09 NI .21 .00
40m Sprint NI .32 .09 NI .27 .01
Quadrant Jump NI .24 .05 NI .09 .00
Hopping-in-Square NI .17 .02 NI .04 .00
Dynamic Balance NI .16 .02 NI .14 .00
Basketball Throw NI .12 .01 NI .03 .00
Balance Eyes Open .51 .67 .40 -.79 -.70 .05
Shuttle Run With Object .41 .51 .23 .63 .64 .04
Vertical Jump NI .08 .01 NI .24 .01
Canonical Correlation .52 .21
Eigenvalue .36 .05
Group Centroids
Poor -1.08 .37
Normal -.03 -.13
High 1.15 .30
Note. SW: Standardised weights. NI: Not included in the stepwise solution. Value: Structure
correlations with correlations greater than .50 in bold. PI: Potency Index.
For both motor skills, the performance of the Normal motor coordination group was better than that of
the Poor coordination and less than that for the High motor coordination group. However, the
discriminatory power of these tests is relatively low according to their relative potency indices of .05
and .04, respectively.
94
The jackknife classification analysis revealed that 115 (71.4%) of the participants were classified
correctly, compared with 89 (55.09%) who would be correctly classified by chance alone. However,
using sample proportions as prior probabilities, it appears that only the Normal coordination group
was more likely to be correctly classified (91.3%). Specifically, 105 Normal individuals were
classified correctly, five Normal individuals were classified as Poor (4.3%) and five Normals were
classified as High (4.3%). The High coordination group reported 16.7% correct classifications (n = 4),
with the rest misclassified as Normal (83.3%). The Poor coordination group had six individuals
correctly classified (27.3%). However, the other sixteen Poor individuals were classified as Normal
(72.7%). Thus, the classification rate of around 71% was achieved despite a disproportionate number
of cases being classified as Normal.
Finally, an examination of the misclassified adolescent girls revealed the following (see Table 37). All
16 misclassified Poor motor coordination individuals were misclassified as Normal. With the
exception of the Shuttle Run with Object, the 40m Sprint test and the Vertical Jump, the misclassified
girls performed the motor skills to a higher standard over their correctly classified Poor cohorts, with
a significant performance improvement for the Balance Eyes Open (p < .001). The five Normal motor
coordination individuals who were misclassified as High were able to perform all of the motor skills
to a higher standard over their correctly classified Normal cohorts. Significant performance
improvements were found for the Balance Eyes Closed, Balance Eyes Open and the Shuttle Run With
Object motor skills (p < .001). The five Normal motor coordination individuals who were
misclassified as Poor, with the exception of Shuttle Run with Object and Vertical Jump, performed all
of the motor skills to a lower standard over their correctly classified Normal cohorts, with a
significant performance decrement for the Balance Eyes Open test (p < .003). All of the misclassified
High motor coordination female adolescents were misclassified as Normal. For these individuals, with
the exception of Dynamic Balance, Basketball Throw and Hopping-in-Square, these individuals
performed the motor skills to a lower standard than their correctly classified High cohorts.
95
Table 9. Profiling Correctly Classified & Misclassified Observations in the Three-Group Discriminant
Analysis for Female Adolescents.
Mean Scores t test
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
Poor
(n = 6)
N
(n = 16)
H
(n = 0)
N
H
N
H
N
H
Balance Eyes Closed a 22.67 43.00 - -20.33 - -2.83 - .029 -
Shuttle Run 11.51 11.21 - .30 - .54 - .598 -
Hopping Speed 13.79 10.74 - 3.06 - 2.03 - .056 -
Zigzag Run 13.84 13.45 - .39 - .53 - .602 -
Multistage Fitness Test 9.50 12.19 - -.269 - -1.41 - .174 -
40m Sprint 8.94 8.97 - -.03 - -.06 - .950 -
Quadrant Jump 24.25 28.44 - -4.19 - -1.11 - .281 -
Hopping-in-Square 40.12 49.75 - -9.63 - -2.99 - .007 -
Dynamic Balance 14.67 18.31 - -3.64 - -1.62 - .121 -
Basketball Throw 3.80 4.34 - -.54 - -1.83 - .082 -
Balance Eyes Open a 72.33 111.38 - -39.05 - -7.56 - .000 -
Shuttle Run With Objecta 11.60 11.80 - -.20 - -.33 - .748 -
Vertical Jump 22.67 22.44 - .23 - .08 - .935 -
Normal
(n = 105)
P
(n = 5)
H
(n = 5)
-
P
H
t-value
P
H P H
Balance Eyes Closed a 54.35 30.00 118.40 24.35 -64.05 3.35 -19.42 .015 .001
Shuttle Run 10.99 11.01 9.92 -.02 1.07 -.05 2.10 .962 .038
Hopping Speed 10.45 11.96 8.18 -1.51 2.27 -1.19 1.88 .237 .063
Zigzag Run 13.24 13.85 11.57 -.61 1.67 -.94 6.60 .350 .001
Multistage Fitness Test 11.84 11.80 20.00 .04 -8.16 .43 2.11 .667 .101
40m Sprint 8.86 9.23 7.44 -.37 1.42 ` -.62 2.41 .538 .018
Quadrant Jump 29.37 25.10 33.80 4.27 -4.43 1.30 -1.35 .198 .179
96
Table 37 continued.
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
P H P H P H P H
Hopping-in-Square 45.86 41.00 56.80 4.86 -10.94 1.20 -2.63 .232 .010
Dynamic Balance 19.83 18.40 26.20 1.43 -6.37 .63 -2.82 .529 .006
Basketball Throw 4.20 4.02 4.86 .18 -.660 .51 -1.87 .609 .604
Balance Eyes Open a 114.73 59.00 120.00 55.73 -5.27 6.82 -5.34 .002 .001
Shuttle Run With Objecta 11.70 11.65 10.30 .05 1.40 .07 8.50 .943 .001
Vertical Jump 23.20 26.40 27.20 -3.20 -4.00 -1.14 -1.49 .258 .139
High
(n = 5)
P
(n = 0)
N
(n = 19)
P
N
P
N
P
N
Balance Eyes Closed a 113.00 - 79.58 - 33.42 - 2.48 - .011
Shuttle Run 9.63 - 10.33 - -.70 - -1.74 - .095
Hopping Speed 7.74 - 8.61 - -.87 - -1.17 - .255
Zigzag Run 11.85 - 12.36 - -.51 - -1.19 - .248
Multistage Fitness Test 15.60 - 13.89 - 1.71 - .66 - .515
40m Sprint 7.83 - 8.01 - -.18 - -.40 - .690
Quadrant Jump 37.00 - 33.82 - -3.18 - .83 - .417
Hopping-in-Square 48.40 - 48.79 - -.39 - -.13 - .900
Dynamic Balance 18.40 - 20.74 - -2.34 - -1.01 - .323
Basketball Throw 4.38 - 4.68 - -.30 - -.88 - .390
Balance Eyes Open a 120.00 - 119.68 - .32 - .50 - .619
Shuttle Run With Objecta 9.94 - 10.75 - -.81 - -1.83 - .081
Vertical Jump 26.20 - 23.89 - 2.31 - 1.42 - .175
Note. a =Variables included in the stepwise estimation. P = Poor, N = Normal, H = High. NA = Not
available. A Bonferroni correction was used to adjust the .05 significance value to .0038. Significant
differences are in bold.
97
6.5. DISCUSSION – FEMALE ADOLESCENTS
The results of the stepwise estimation revealed three motor skills that could maximally separate the
three motor coordination groups: Balance Eyes Closed, Balance Eyes Open and Shuttle Run with
Object. Although this is the best set of motor skills reported, the discriminant functions were
examined to see where discrimination would occur if all 13 motor skills were included in the model.
An examination of the discriminant functions did not reveal additional motor skills that had a
substantial effect on discriminating between the three motor coordination groups beyond that
indicated by the stepwise estimation.
Two discriminant functions were derived from the analysis and, given the magnitude of the canonical
structure coefficients, the potency indices and the canonical correlations for these functions, the first
function was deemed important. The first function indicated the motor skills that can maximally
separate the High coordination group from the Poor group with the Normal group in between. Thus,
when one considers all of the motor skills as a package, the Balance Eyes Closed, Balance Eyes Open
and Shuttle Run with Object were found to be the best set to separate the High motor coordination
group from the other two coordination groups. An examination of the mean performances of the three
coordination groups on these four motor skills indicated that the High motor coordination group
consistently outperformed the other coordination groups. The second discriminant function indicated
that the best motor skills for discriminating the Normal coordination group from the other two
coordination groups were Balance Eyes Open and Shuttle Run with Object. An examination of the
performance means for these motor skills revealed that the Normal group recorded better
performances than the Poor coordination group, and lower performances than the High coordination
group. However, according to their respective potency indices, the discriminatory power of these two
motor skills was relatively low.
When assessing the fit of the discriminant model, the predictive accuracy level of the discriminant
functions was examined. Using jackknife classification, the functions could reasonably classify the
adolescents. Specifically, the hit ratio was 89%, which is considerably higher than the 56% who
would be correctly classified by chance alone. The Normal group had the best correct classification
hit ratio with 91% of Normals being classified correctly, 4.5% classified as Poor and 4.5% classified
as High. The High group only had a correct classification hit ratio of 17%, with 83% being classified
as Normal. Finally, the Poor coordination group only had a correct hit ratio of 27%. The remaining
Poor motor coordination individuals were misclassified as Normal (73%). Once again, what is note
worthy in these findings is the disproportionate number of cases being classified as Normal. For
instance, most of the High coordination group, and over two-thirds of the Poor coordination group
were misclassified as Normal. Therefore, several female adolescents who performed the MAND
98
motor skills quite poorly, and were categorised as Poor in basic motor coordination, were able to
perform the AIS+BMC motor skills at a level higher than their correctly classified cohorts.
Conversely, there were female adolescents who performed the MAND motor skills very well and
were categorised as High in basic motor coordination, but 83% of these individuals performed the
AIS+BMC skills at a level lower than their correctly classified cohorts.
A: An examination of the misclassifications supports such a view. For example, those Poor motor
coordination individuals who were misclassified as Normal, except for Shuttle Run with Object, the
40m Sprint and the Vertical Jump, as a group, performed the motor skills to a higher standard over
their correctly classified Poor cohorts. In addition, they performed the Balance Eyes Open motor skill
for significantly longer than the correctly classified Poor cohorts. The Normal motor coordination
individuals who were misclassified as High performed all of the motor skills to a higher standard over
their correctly classified Normal cohorts, with significant performance improvements for the Balance
Eyes Closed, Balance Eyes Open and the Shuttle Run with Object motor skills. The Normal motor
coordination individuals who were misclassified as Poor, with the exception of Shuttle Run with
Object and Vertical Jump, performed all of the motor skills at a lower standard than their correctly
classified Normal cohorts, with a significant performance decrement for the Balance Eyes Open.
Finally, the High motor coordination individuals who were misclassified as Normal, with the
exception of the Dynamic Balance, Basketball Throw and Hopping-in-Square, performed the motor
skills to a lower standard over their correctly classified High cohorts. Thus, it appears that the
misclassifications found here once again make sense in terms of performance. Those individuals
misclassified to a level higher, generally performed the AIS+BMC motor skills to a higher level than
their correctly classified cohorts; and those individuals misclassified to a level lower, generally
performed the AIS+BMC motor skills to a lower level than their correctly classified cohorts.
Again, these findings are an important reminder about individual differences in motor skill
performance and that performance in one set of motor skills does not necessarily translate to similar
levels of performance in a different set of motor skills. It is clear that a large number of female
adolescents performed the MAND motor skills to either a very low standard or a very high standard,
and this level of performance was not translated across to their AIS+BMC performances. The
particular reasons as to why there were such large misclassifications for the Poor and High
coordination groups remain unclear and awaits future investigation.
6.9. RESULTS: GROUPS DERIVED FROM ‘g’ SCORES - FEMALE ADOLESCENTS Originally, discriminant analyses were to be performed separately on the boys and the girls. However,
owing to both the Poor and High ability groups of boys failing to meet the recommended minimum 20
cases per group (Hair et al., 1998), only the adolescent girls were analysed. A total of 161 female
99
participants were used in the following analyses (21 Poor motor ability, 120 Normal motor ability and
20 High motor ability).
The results of the stepwise discriminant analysis revealed seven motor skills that could maximally
separate the three motor ability groups. Discrimination increased with the addition of each test and by
the seventh step achieved the ability to discriminate between the motor ability groups. Specifically,
Hopping Speed entered on the first step Wilks Lambda = .58, F(2, 158) = 56.72, p < .001, Quadrant
Jump on the second step Wilks Lambda = .46, F(4,314) = 36.91, p < .001, Balance Eyes Open on the
third step Wilks Lambda = .40, F(6,312) = 30.54, p < .001, Dynamic Balance on the fourth step Wilks
Lambda = .35, F(8,310) = 27.26, p < .001, Balance Eyes Closed on the fifth step Wilks Lambda = .31,
F(10,308) = 24.53, p < .001, Hopping-in-Square on the sixth step Wilks Lambda = .29, F(12,306) =
22.03, p < .001, and the Multistage Fitness test on the seventh step Wilks Lambda = .27, F(14,304) =
20.04, p < .001.
Two canonical discriminant functions were found for the AIS+BMC (see Table 41). Both functions
were significant, and indicated strong associations between the groups and predictors for each
function. Specifically, the first function produced a Wilks Lambda = .27, with a Chi-square (14) =
202.66, p < .001, and the second function produced a Wilks Lambda = .87, with a Chi-square (6) =
21.78, p < .002. The canonical R2s for the two functions were .83 for the first function and .36 for the
second function. The two discriminant functions accounted for about 94% and about 6%, respectively,
of the between group variability. An examination of the unstandardised canonical discriminant
functions evaluated at group means reveals that the first function maximally separates the Poor motor
ability group from the High group, with the Normal group in between. The second function
discriminates the Normal motor ability group from the High and Poor groups.
100
Table 10. Standardised Weights, Structure Canonical Coefficient Values, Potency
Index, Canonical Correlations, Eigenvalues and Group Centroids for the
Three Female Motor Ability Groups.
Discriminant Function
First Second
SW Value PI SW Value PI
Hopping Speed .39 .56 .29 -.69 -.38 .01
Quadrant Jump .37 .49 .22 .34 .19 .00
Dynamic Balance .41 .46 .19 -.06 .06 .00
Multistage Fitness Test .19 .38 .13 .60 .38 .01
Balance Eyes Closed .35 .37 .13 .34 .17 .00
Zigzag Run NI .36 .12 NI -.02 .00
Shuttle Run NI .34 .11 NI -.03 .00
40m Sprint NI .27 .07 NI .01 .00
Shuttle Run With Object NI .23 .05 NI -.04 .00
Basketball Throw NI .12 .01 NI .06 .00
Balance Eyes Open .37 .33 .10 -.53 -.49 .02
Hopping-in-Square .26 .33 .10 .39 .39 .01
Vertical Jump NI .01 .00 NI .09 .00
Canonical Correlation .83 .36
Eigenvalue 2.21 .15
Group Centroids
Low -3.03 .60
Normal .06 -.22
High 2.80 .72
Note. SW: Standardised weights. NI: Not included in the stepwise solution. Value: Structure
correlations with correlations greater than .50 in bold. PI: Potency Index.
101
An examination of the structure correlations for the discriminant analysis did not reveal additional
motor skills that had a substantial effect on discriminating between the three motor ability groups
beyond that indicated by the stepwise estimation. Thus, the structure correlations for the first
discriminant function suggests that the best test for discriminating between the Poor motor ability
group, from both the High and Normal motor ability groups was the Hopping Speed test; with the
Quadrant Jump test approaching the cut-off value of .50. The Poor motor ability group recorded lower
performances than the Normal or High motor ability groups: Hopping Speed (Mean = 14.58, SD =
3.03 vs Mean = 10.02, SD = 2.06 and Mean = 7.48, SD = .83, respectively) and Quadrant Jump (Mean
= 21.52, SD = 4.46 vs Mean = 29.74, SD = 6.44 and Mean = 39.25, SD = 5.96, respectively). The
discriminatory power of the Hopping Speed motor skill appears to be relatively good, given its
respective potency index. For the second discriminant function, none of the tests exceeded the cut-off
value of .50. However, the Balance Eyes Open test approached the cut-off value for discriminating the
Normal motor ability group from the High and Poor groups (Mean = 113.78, SD = 12.97 vs Mean =
120.00, SD = .00 and Mean = 94.19, SD = 24.49, respectively).
The jackknife classification analysis revealed that 143 (88.8%) of the participants were classified
correctly, compared with 94 (58.76%) who would be correctly classified by chance alone. However,
using sample proportions as prior probabilities, it appears that only the Normal motor ability group
was more likely to be correctly classified (93.3%). Specifically, 112 Normal individuals were
classified correctly, five Normal individuals were classified as Low (4.2%) and three Normals were
classified as High (2.5%). The High motor ability group reported 75% correct classifications (n = 15),
with the other five misclassified as Normal (25%). The Poor motor ability group had 16 individuals
correctly classified (76.2%). However, the other five Poor individuals were classified as Normal
(23.8%). Thus, the classification rate of around 89% was achieved despite a disproportionate number
of cases being classified as Normal.
Finally, an examination of female adolescents revealed that the five misclassified Poor motor ability
adolescents were misclassified as Normal (see Table 42). With the exception of the Dynamic Balance,
Multistage Fitness Test, Zigzag Run, 40m Sprint, Basketball Throw and Hopping-in Square, the
misclassified adolescents performed the motor skills to a higher standard than their correctly classified
Poor cohorts, with a significant performance improvement for the Balance Eyes Open (p < .001). The
two Normal motor ability individuals who were misclassified as High were able, with the exception of
Shuttle Run with Object and Vertical Jump, to perform all of the motor skills to a higher standard than
their correctly classified Normal cohorts, with significant performance improvements for the
Hopping-in-Square motor skill (p < .001).
102
Table 11. Profiling Correctly Classified and Misclassified Observations in the Three-Group
Discriminant Analysis for the Adolescent Females.
Mean Scores t test
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
Poor
(n = 16)
N
(n = 5)
H
(n = 0)
N
H
N
H
N
H
Hopping Speed a 15.38 12.02 - 3.37 - 2.42 - .026 -
Quadrant Jump a 20.41 25.10 - -4.69 - -2.26 - .036 -
Dynamic Balance a 14.13 12.20 - 1.93 - .95 - .354 -
Multistage Fitness Test a 8.88 7.60 - 1.28 - 1.10 - .284 -
Balance Eyes Closed a 25.06 34.60 - -9.54 - -.89 - .416 -
Zigzag Run 14.76 14.89 - -.13 - -.21 - .833 -
Shuttle Run 12.25 11.97 - .28 - .51 - .614 -
40m Sprint 9.53 9.74 - -.21 - -.35 - .730 -
Shuttle Run With Object 12.65 12.40 - .26 - .34 - .741 -
Basketball Throw 3.78 3.73 - .04 - .11 - .91 -
Balance Eyes Open a 86.94 117.40 - -30.46 - -4.73 - .000 -
Hopping-in-Square a 41.13 38.20 - 2.93 - .76 - .454 -
Vertical Jump 18.50 20.60 - -2.10 - -.98 - .341 -
Normal
(n = 115)
P
(n = 3)
H
(n = 2)
-
P
H
t-value
P
H P H
Hopping Speed a 9.95 14.27 7.85 -4.32 2.11 -3.80 1.52 .000 .132
Quadrant Jump a 29.73 26.00 36.25 3.73 -6.52 5.56 -1.42 .000 .160
Dynamic Balance a 19.83 17.33 28.00 2.49 -8.17 1.06 -2.87 .293 .005
Multistage Fitness Test a 12.20 8.67 13.00 3.53 -.80 1.53 -.28 .128 .778
Table 42 continued.
Motor Group/
Motor Skills
Correctly
Classified
Misclassified
Difference
t-value
Sig.
103
P H P H P H P H
Balance Eyes Closed a 57.41 40.00 93.50 17.41 -36.09 .90 -4.45 .372 .089
Zigzag Run 13.01 14.78 12.57 -1.77 .45 -2.46 .51 .015 .611
Shuttle Run 10.81 11.95 10.43 -1.14 .38 -2.07 .57 .040 .569
40m Sprint 8.79 8.97 7.99 -.18 .81 -.25 .91 .801 .365
Shuttle Run With Object 11.48 11.97 12.12 -.49 -.64 -.68 -.72 .498 .470
Basketball Throw 4.24 4.83 5.00 -.60 -.76 -1.46 -1.55 .148 .125
Balance Eyes Open a 113.62 116.00 120.00 -2.38 -6.38 -.31 -.68 .756 .497
Hopping-in-Square a 46.17 39.67 57.00 6.51 -10.83 1.50 -2.05 .136 .042
Vertical Jump 23.74 22.67 22.00 1.07 1.74 .32 .42 .753 .677
High
(n = 15)
P
(n = 0)
N
(n = 5)
P
N
P
N
P
N
Hopping Speed a 7.73 - 6.76 - .97 - 2.59 - .019
Quadrant Jump a 40.80 - 34.60 - 6.20 - 2.21 - .040
Dynamic Balance a 25.20 - 23.80 - 1.40 - .57 - .577
Multistage Fitness Test a 18.67 - 15.40 - 3.27 - 1.01 - .327
Balance Eyes Closed a 100.80 - 71.00 - 29.80 - 2.29 - .034
Zigzag Run 11.70 - 11.42 - .28 - .81 - .430
Shuttle Run 9.95 - 9.12 - .83 - 3.14 - .006
40m Sprint 7.43 - 7.05 - .38 - 1.31 - .207
Shuttle Run With Object 10.57 - 9.45 - 1.12 - 2.54 - 0.02
Basketball Throw 4.89 - 4.84 - .05 - .13 - .901
Balance Eyes Open a 120.00 - 120.00 - .00 - NA - NA
Hopping-in-Square a 56.80 - 53.20 - 3.60 - .71 - .488
Vertical Jump 27.60 - 25.80 - 1.80 - .68 - .506
Note. a = Variables included in the stepwise estimation. P = Poor, N = Normal, H = High. NA = Not
available. A Bonferroni correction was used to adjust the .05 significance value to .0038. Significant
differences are in bold.
104
The three Normal motor ability individuals misclassified as Low, with the exception of the Basketball
Throw and Balance Eyes Open, performed all of the motor skills to a lower standard than their
correctly classified Normal cohorts. Significant decrements were found for Hopping Speed and
Quadrant Jump (p < .001). Five misclassified High motor ability adolescents were misclassified as
Normal. With the exception of Hopping Speed, Zigzag Run, 40m Sprint and Shuttle Run with Object,
they performed the motor skills to a lower standard than their correctly classified High cohorts.
6.10. DISCUSSION – FEMALE ADOLESCENTS
The results of the stepwise estimation revealed seven motor skills that could maximally separate the
three motor ability groups - Hopping Speed, Quadrant Jump, Balance Eyes Open, Dynamic Balance,
Balance Eyes Closed, Hopping-in-Square and the Multistage Fitness test. Although this is the best set
of motor skills reported, the discriminant functions were also examined to see where discrimination
would occur if all 13 motor skills were included in the model. An examination of the discriminant
functions did not reveal additional motor skills that had a substantial effect on discriminating between
the three motor ability groups beyond that indicated by the stepwise estimation.
Two discriminant functions were derived from the analysis and, given the magnitude of the canonical
structure coefficients, the potency indices and the canonical correlations for both functions, the first
function was deemed slightly more important than the second function. The first function indicated
the motor skills that can maximally separate the Poor motor ability group from the High group, with
the Normal group in between. When one considers all of the motor skills as a package, only Hopping
Speed was found to discriminate the Poor motor ability group from both the Normal and High ability
groups. An examination of the mean performances of the three motor ability groups on this motor
skill indicated that the Poor ability group performed this skill at a considerably lower level than either
of the other two ability groups. None of the motor skills in the second function were sufficiently
strong enough to clearly discriminate the Normal ability group from both the High and Poor ability
groups. However, Balance Eyes Open did approach the .50 cut-off. The performance mean for this
motor skill revealed that the Normal group recorded better performances than the Poor ability group
and lesser performances than the High motor ability group. However, according to its potency index,
the discriminatory power of Balance Eyes Open was low.
When assessing the fit of the discriminant model, the predictive accuracy level of the discriminant
functions was examined. Using jackknife classification, the functions classifed the female adolescents
quite well. Specifically, the hit ratio was 89%, which is considerably higher than the 59% who would
be correctly classified by chance alone. The Normal ability group had the best correct classification
hit ratio, with 93% of Normals being classified correctly, 3% misclassified as Poor and 4%
105
misclassified as High. The High motor ability group had a correct classification hit ratio of 75%, with
25% being misclassified as Normal. Finally, the Poor motor ability group had a correct hit ratio of
76% and the remaining Poor motor ability individuals were misclassified as Normal (24%). Once
again, a disproportionate number of cases were being classified as Normal, but the percentages were
not as high as those found for the MAND framework. Using GMA scores to create the ability groups,
only 25% of the High motor ability group and 24% of the Low motor ability group were misclassified
as Normal. Thus, some female adolescents who were classified as having Poor motor ability when
based on their ‘g’ scores, performed some AIS+BMC motor skills at a level higher than those
correctly classified. Conversely, female adolescents classified with High motor ability from their ‘g’
scores, performed the AIS+BMC motor skills to a lower level than their correctly classified cohorts.
The misclassification results support such a view with Poor motor ability individuals who were
misclassified as Normal, with the exception of Dynamic Balance, Multistage Fitness Test, Zigzag
Run, 40m Sprint, Basketball Throw and Hopping-in Square, performed the motor skills better than
their correctly classified Poor cohorts. In addition, they performed the Balance Eyes Open motor skill
for a significantly longer time than their correctly classified Poor ability cohorts. The Normal motor
ability individuals who were misclassified as High were able, with the exception of the Shuttle Run
with Object and Vertical Jump, to perform the motor skills at a better standard than the correctly
classified Normal cohorts; with significant performance improvements for Hopping-in-Square. The
Normal motor ability individuals who were misclassified as Poor, with the exception of the Basketball
Throw and Balance Eyes Open, performed all motor skills at a lower standard than their correctly
classified Normal cohorts; with significant decrements found for Hopping Speed and Quadrant Jump.
Finally, the High motor ability female adolescents who were misclassified as Normal, with the
exception of Hopping Speed, Zigzag Run, 40m Sprint and Shuttle Run with Object, performed the
motor skills at a lower standard than their correctly classified High cohorts. Thus, it appears that the
misclassifications found here again make sense in terms of performance. Those individuals
misclassified to a higher level generally performed the AIS+BMC motor skills to a higher level than
their correctly classified cohorts, and those individuals misclassified to a level lower generally
performed the AIS+BMC motor skills to a lower level than their correctly classified cohorts.
However, once again these findings are especially intriguing given that the ‘g’ score was derived from
the performance of the motor skills making up the AIS+BMC. The particular reasons as to why there
are misclassifications for the Low and High motor ability groups are unclear. It is possible that the
best set of motor skills as derived by the discriminant analysis on the face of it are quite good at
discriminating between the three ability groups, but fall short with particular individuals. Thus, it is
important for practitioners to examine the nature of the misclassifications and indeed examine each
misclassified case separately to determine the nature of these individuals.