MINORITY ADOLESCENT EMOTIONAL WELL BEING AS IMPACTED BY THE

EMPOWERED YOUTH PROGRAMS

by

MAHLET ENDALE

(Under the Direction of Arthur Horne)

ABSTRACT

Academic achievement is the primary concern of K-12 institutions. Unfortunately, many

factors affecting academic achievement are out of teachers’ control. Self-confidence,

satisfaction with life, family environment, and hope have all been associated to levels of

academic achievement. It might be hard for the existing educational system to alone help

students with the above factors. Most students, and especially disadvantaged students, can

benefit from an external program in addition to traditional education. Therefore there is a need

for external programs that can supplement the educational experience while at the same time

acting as a buffer against barriers that impede academic achievement. One such program is the

Empowered Youth Programs (EYP). EYP is an enrichment program that targets

underrepresented K-12 students and aims to help participants achieve academic and social

excellence. EYP is structured to impact students’ self-concept, self esteem, satisfaction with life,

family environment, and hope. Using an existing EYP database, this study examined EYP’s

impact on these factors. This study assessed self-reports on measures of self-esteem, self-

concept, satisfaction with life, family environment, and hope. It looked at the relationship

between students’ emotional wellbeing and academic achievement. It looked for changes in

student self-reports on the psychological measures with EYP participation. Finally, it examined

whether any changes on self-reported psychological measures could be attributed to EYP. It was

found that on average students scored within healthy ranges on measures of hope, life

satisfaction, self-concept, and self-esteem. However, on average scores were within unhealthy

ranges on the family environment measure. The database was incomplete so the relationship

between academic achievement and emotional wellbeing was inconclusive. It was found that 1

semesters or more of EYP participation reduced family conflict at a statistically significant level.

Although, all other measures showed improvement in scores with 1 or more semesters of EYP

participation, these differences were not statistically significant. As to whether changes in self-

reports could be attributed to EYP, findings to the other questions of this study suggest that EYP

has a positive influence on participants’ emotional wellbeing, however, without a control group

it is difficult to assess how much to attribute to EYP.

INDEX WORDS: Enrichment programs, emotional wellbeing, life satisfaction, hope, family

environment, self-concept, self esteem

MINORITY ADOLESCENT EMOTIONAL WELL BEING AS IMPACTED BY THE

EMPOWERED YOUTH PROGRAMS

by

MAHLET ENDALE

M.Ed., University of Georgia, 2003

B.S., University of Georgia, 2001

A Dissertation Submitted to the Graduate Faculty of The University of Georgia in Partial

Fulfillment of the Requirements for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2007

© 2007

Mahlet Endale

All Rights Reserved

MINORITY ADOLESCENT EMOTIONAL WELL BEING AS IMPACTED BY THE

EMPOWERED YOUTH PROGRAMS

by

MAHLET ENDALE

Major Professor: Arthur Horne, Ph.D.

Committee: Deryl Bailey, Ph.D. Allan Stewart, Ph.D. Linda Campbell, Ph.D.

Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2007

iv

DEDICATION

This document is dedicated to all the students who have passed through the Empowered

Youth Programs. It is also dedicated to Dr. and Mrs. Bailey, who founded the program and have

dedicated their lives to the students. Finally, it is dedicated to the many staff and volunteers who

have helped make the program a success. May all the students, staff, and volunteers see the

fruits of their labor.

v

ACKNOWLEDGEMENTS

Pursuing this doctoral degree has been the largest and most difficult task I have taken on thus far in my life. I was stretched academically, professionally, and personally during this time. I accomplished things that I would never have thought possible. However, none of it would have been possible without the people around me. I would like to take this time to thank each of you who played a role in my being where I am today. I especially want to thank the following people: God: I would not be here today without my God. My teacher of patience, joy, and peace. My comforter and strongest advocate. The one who believes in me more than anyone. I thank you publicly for holding my hand and guiding my feet through this process. My extended family: My family is strewn across 3 continents yet our bond is solid. The thoughts, guidance, physical support, financial support, and prayers have allowed my family to be where we are today. We are not all here on earth anymore but even from heaven I know I am being watched over. My immediate family: My father: You have been where I am so you truly understand the ups and downs of this process I am in. That means so much. I am a carbon copy of you, so you understand me. Your faith and pursuit of God is an inspiration and has impacted the entire family more than you know. You are such a phenomenal example of what it means to live right. To live without cutting corners, or looking for the easy road. You make me proud! My mother: You are the epitome of love, support, self-sacrifice and strength. You are wise and aware and know when to push me and when to let me be. You are my distraction from school or life when I need it, and the one who forces me to sit down and work when its time to do so. Your faith in God has been present longer and more faithfully than the rest of us in this family and you have kept us anchored to Him when the rest of us floated around. I am proud of who you are and I hope I can come close to being as good a mother as you. My sister Liya: You are wise beyond your years. You have seen and experienced more than many your age. In my time here I have gone to you for love, support, understanding, distraction, advice, and for guidance. You give these unconditionally. You and Mom are also the ones who will quickly tell me I am acting a fool when I need to be set right. It is so refreshing to have someone tell me I need to get it together when I am not making any sense. I thank you. Ms. Shirley: Ms. Shirley, I am including you in my family section because you are my second mother on this campus. You are the true hero. Everyday I am blown away with you ability to take care of so much and so many on a continuous basis. The most brilliant part is that you do it with a smile. I have never met a more positive person. Even if there is total chaos you manage to laugh through it and you manage not to let it get you down. I hope to be more like that. As for this time, really, you have

vi

been here for me since I was a 19 year old girl arriving to campus for the first time my sophomore year as an undergrad. You have taken me in, supported me, and managed to keep me laughing. You are my encourager, and an example of strength. Faculty and Staff: Collectively, I want to thank everyone who works on 4th floor Aderhold because every single one of you have impacted me in one way or another. I appreciate you all being here and doing the work that you do. I want to specifically address some of you with whom I have had the most contact and who have impacted me most. Dr. Stewart, you have been part of my Aderhold experience since the master’s program. Thank you for sitting on my committee and being open to consultations with me as I was working with Dr. Bailey in the Empowered Youth Programs. Your calm and attentive nature is inspiring. Dr. Bailey & Mrs. Bailey, your passion for what you do is an inspiration. Through hard work you two have built an organization with love, passion, and perseverance. Through EYP you guide all the children that come through the program, but you also guide your staff and volunteers. We see your hard work even when others do not. You have taught me to follow what I am passionate about, not to be afraid to work hard, and not to wait for others’ acknowledgement when I know that I am doing something positive and impactful. Keep it up, because we need someone like you and the community needs someone like you. Dr. Horne, you have been a friend, a listener, a guide, and someone who understands my experience. You listened to my frustrations and helped me learn how best to navigate this and other systems without forgetting who I am. You are someone who has a kind word for me right as I need it to remind me that I am constantly progressing and that 4th floor Aderhold does not have to define my life or my successes. I appreciate your open door policy for us to come in anytime and talk, I appreciate you taking us into your home and breaking bread with us, and I respect your wisdom and your example of approaching all that life throws at you with laughter. You show that it is possible to not let the little (or big) things get to you. I have learned academically from you, but I’ve learned even more from your way of being. Dr. Campbell, out of all of our doctoral instructors I think I have known you the longest! I truly did not understand all you are until my doctoral experience. I am in awe of your ability to successfully manage so many things at once. The thing that I am even more in awe of is the fact that even with all your successes and where you are professionally, you relate to us as a regular approachable person. Like Dr. Horne, you lead life with laughter and even during difficult times you manage to keep moving forward. You are also one of the few who regularly lets me know that you believe in me and what I am capable of. Like Dr. Horne your words always seem to come at a point when, though I don’t verbalize it, I am internally doubting myself the most. Those words help me to keep pushing forward. My Cohort: Hotties, I can not believe we are walking away from here never to all be in the same place like this again. You all have been encouragers, supporters, guiders, and motivators in my time here. My growth in this time comes from course work as well as from being around all of you. I am so very proud of you and can not wait to see what all you will accomplish in your professional lives. I love each and every one of you.

vii

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS............................................................................................................V

LIST OF TABLES……………………………………………………………………………….IX

LIST OF FIGURES ...................................................................................................................... XI

CHAPTER

1 Introduction....................................................................................................................1

Empowered Youth Programs ....................................................................................2

The Problem ..............................................................................................................6

Purpose of the Study..................................................................................................7

Hypotheses ................................................................................................................9

2 Review of Literature ....................................................................................................10

Enrichment Programs ..............................................................................................10

Family Environment................................................................................................12

Self-Concept ............................................................................................................16

Self Esteem..............................................................................................................17

Satisfaction with Life ..............................................................................................18

Hope ........................................................................................................................19

The Current Study ...................................................................................................21

viii

3 Methodology................................................................................................................24

Participants ..............................................................................................................24

Procedures ...............................................................................................................25

Instruments ..............................................................................................................26

4 Results..........................................................................................................................31

Anticipated Data Set................................................................................................31

Actual Data Set........................................................................................................32

Findings by Instruments ..........................................................................................33

Hypothesis I.............................................................................................................39

Hypotheses II and III ...............................................................................................43

Hypothesis IV..........................................................................................................44

Hypothesis V ...........................................................................................................49

5 CHAPTER 5 - Discussion ...........................................................................................56

Descriptive Findings by Instruments.......................................................................56

Hypothesis I.............................................................................................................58

Hypotheses II & III..................................................................................................60

Hypothesis IV..........................................................................................................61

Hypothesis V ...........................................................................................................63

Limitations of Study and Future Recommendations ...............................................65

REFERENCES ..............................................................................................................................70

APPENDICES .............................................................................................................................140

A Analysis of Variance Hypothesis IV, Fig. 1.1 to 1.5 ................................................140

B Comparison of MSCS and FES Scores against Normative Data, Fig. 2.1 to 2.6 …146

ix

LIST OF TABLES

Page

Table 1: Student FES Scores and Normative Data Provided in FES Manual ...............................74

Table 2: Summary of Hope Scale Scores ......................................................................................75

Table 3: MSCS Scores Color Coded to Represent Manual’s Scoring Guide ................................76

Table 4: Summary of Rosenberg Self Esteem Scale Scores..........................................................77

Table 5: EYP Scores of the Satisfaction with Life Scale as Interpreted by the Instrument Scoring

Guide...............................................................................................................................78

Table 6: Matrix of Available Scores Across Measures and Semesters .........................................79

Table 7: Available Assessment Scores for Fall 2002 ....................................................................80

Table 8: Available Assessment Scores for Spring 2003................................................................81

Table 9: Available Assessment Scores for Fall 2003 ....................................................................83

Table 10: Available Assessment Scores for Spring 2004..............................................................86

Table 11a: Available Assess. Scores for Fall 2004 for SES, SWLS, Hope, and MSCS Scales…88

Table 11b: Available Assessment Scores for Fall 2004 for FES Scales........................................90

Table 12: Available Assessment Scores for Spring 2005..............................................................93

Table 13a: Available Assessment Scores for Fall 2005 for SWLS and MSCS Scales..................96

Table 13b: Available Assessment Scores for Fall 2005 for FES Scales......................................100

Table 14: Available Data for Grades ...........................................................................................104

Table 15: Descriptive Statistics and Shapiro-Wilk Test Results for Semester Assessment Data106

x

Table 16a: Numbers of Available Sets of Instrument Scores by Semester .................................110

Table 16b: Breakdown of Normality of Available Sets of Instrument Scores by Semester........110

Table 17a: Input Data for Hope-Total for Analysis of Variance for Hypothesis IV ...................111

Table 17b: Input Data for MSCS-Total for Analysis of Variance for Hypothesis IV.................113

Table 17c: Input Data SES for Analysis of Variance for Hypothesis IV ....................................115

Table 17d: Input Data for SWLS for Analysis of Variance for Hypothesis IV...........................118

Table 17e: Input Data for FES for Analysis of Variance for Hypothesis IV...............................120

Table 18: Summary Shapiro-Wilk Normality Test for Data Used for Testing Hypothesis IV ...132

Table 19: ANOVA Output Summary, Hypothesis IV, With Respect to Type III Sum of Squares

for Semester Number as Source of Variation ...............................................................133

Table 20: Means and Standard Deviations for MSCS Normative Data ......................................134

Table 21: Results of One-Sample T Test Comparing Normative Data for Measure with Scores

for Spring 2003, Fall 2003, Fall 2004, Spring 2005, Fall 2005 and Scores Pooled

Across These Semesters................................................................................................135

Table 22: Means and Standard Deviations for FES Normative Data for Normal and Distressed

Individuals.....................................................................................................................137

Table 23: Results of One-Sample T Test Comparing Normative Data for FES Measure for

Normal and Distressed Individuals with Scores from Fall 2004, Fall 2005, and Scores

Pooled Across Semesters ..............................................................................................138

xi

LIST OF FIGURES

Page

Figure 1.1: Data Structure for Each Measure of Analysis for Variance of Hypothesis IV .........140

Figure 1.2: Distribution of Hope-Total and MSCS-Total Scores by Semester Number for

Analysis for Hypothesis IV .........................................................................................141

Figure 1.3: Distribution of SES and SWLS Scores by Semester Number for Analysis of

Hypothesis IV..............................................................................................................142

Figure 1.4: Distribution of COH, EXP, CONF, and IND Scores by Semester Number for

Analysis of Hypothesis IV ..........................................................................................143

Figure 1.5: Distribution of AO, ICO, ARO, and MRE Scores by Semester Number for Analysis

of Hypothesis IV .........................................................................................................144

Figure 1.6: Distribution of ORG and CONT Scores by Semester Number for Analysis of

Hypothesis IV..............................................................................................................145

Figure 2.1: Means with 95% Confidence Interval for MSCS Assessment Measure Scores for

Normative (n=2501) and Semester Data, and for Data Pooled Across Semesters….146

Figure 2.2: Means with 95% Confidence Interval for Normative FES Assessment Measure

Scores for Normal (n=5331) and Distressed (n=1515) Individuals ............................147

xii

Figure 2.3: Means with 95% Confidence Interval Comparing FES Assessment Measure Scores

for Fall 2004 and Fall 2005 with Normative Data for Normal Individuals ................148

Figure 2.4: Means with 95% Confidence Interval Comparing FES Assessment Measure Scores

for Fall 2004 and Fall 2005 With Normative Data for Distressed Individuals ...........149

1

CHAPTER ONE - INTRODUCTION

Academic achievement for all students is the primary concern of K-12 institutions.

However, there are many factors that contribute to the academic achievement of students.

School factors such as classroom size and school resources can affect how students perform. On

the other hand, the student’s intellectual ability plays a key role. However, there are other

characteristics of the student that impact school performance (Caspi, Elder, & Bem, 1987;

Dweck, 1999; Ensminger & Slusarcick, 1992; Hinshaw, 1992; Luster & McAdoo, 1996).

Factors such as self-confidence, satisfaction with life, the family environment, and hope have all

been associated to levels of academic achievement. Unfortunately, many of these factors are out

of teachers’ classroom control as they attempt to help students learn in the school settings. With

some of the aforementioned factors, it might be hard to imagine that the existing educational

system alone can help all students achieve in schools. Disadvantaged students especially can

benefit from a supplemental system to offset deficiencies in their background. Therefore there is

a great need for external programs that can supplement the educational experience while at the

same time acting as a buffer against some of the barriers that impede academic achievement.

2

Empowered Youth Programs

One such program is the Empowered Youth Programs (EYP)

(http://www.empoweredyouthprograms.org). Originally EYP was developed as Gentlemen on

the Move (GOTM) (Bailey & Bradbury-Bailey, 1997). Today EYP is comprised of GOTM,

Young Women Scholars (YWS), and Parents of Empowered Youth (PEY). The GOTM program

was started by a high school in 1989 in North Carolina as a program targeting high school age

African American boys in weekly sessions of academic tutoring and social skills (Bailey &

Bradbury-Bailey, 1997). As the program evolved the YWS component was added in 2003 to

include female participants in the same age range. The program expanded again to first include

middle school students (2003), and then elementary (2004) school students. As the program

progressed it was evident that parent involvement was key to helping the students experience

academic and social success as participants of EYP. Therefore the PEY component was created

to provide workshops for parents and guardians of program participants (Bailey & Paisley,

2004). Today all three components function together under the umbrella of Empowered Youth

Programs. Through referrals and self-selection, participants in the program include k-12 African

American and South Korean youth across all levels of academic achievement. It is important to

note that the majority of program participants are African American. EYP aims to help

participants function at their optimal level in academics and social skills (Bailey, 2001; Bailey,

2003; Bailey & Paisley, 2004). The student interventions include the Saturday Academy,

Summer Academy, Academic Monitoring, After-School Tutorial, Fall and Spring Exam Lock-In,

individual and group counseling referrals, and Community Service (Bailey, 2001; Bailey, 2003;

Bailey & Paisley, 2004). Below are in depth descriptions of these interventions.

3

Saturday Academy

During the academic school year, the Saturday Academy component of EYP meets 3-4

Saturdays a month in the College of Education on the University of Georgia campus in Athens

Georgia. Saturday Academy consists of five academic rotations including reading

comprehension, vocabulary, math, science, and self-discovery. Saturday Academy primarily

functions to help EYP participants master these core competencies that serve as prerequisites for

advanced level academic courses. The self-discovery rotation involves activities to aid in

character education and leadership development. Topics such as healthy relationships, healthy

decision-making, anger management, college exploration, and career decision-making are

covered in this rotation. The Saturday Academy also includes an hour and a half of homework

assistance to provide students with help on classroom assignments.

Summer Academy

A condensed form of the Saturday Academy, the Summer Academy involves a one-week

intensive period of academic and social activities. This portion of EYP originally met on the

University of Georgia campus Monday through Friday. During this time students engage in

activities including academic training, field trips, and a ropes course. There are also programs

that enhance problem solving, communication, and decision-making skills. The week ends with

a three-day camping trip where some of the activities are continued.

Academic Monitoring

Program directors, graduate assistants and certified teachers serve as weekly academic

monitors for program participants. The academic monitors maintain weekly to bi-weekly contact

with teachers and parents. Through this contact academic monitors collect students’ academic

4

and social progress. This information is collected in a standardized manner through the

completion of Academic Monitoring Forms by teachers. Academic Monitors also attend

parent/teacher conferences when requested and sit in on classes to get an understanding of what

the child’s classroom interactions are like.

Fall and Spring Exam Lock In

At the end of each semester, EYP high school students are required to participate in an

Exam Lock-in that begins on Friday afternoon and ends mid Sunday afternoon. This over night

experience involves intensive final exam preparation via hour-long study sessions followed by

quiz periods. All meals, breaks, recreation, showers, and secure sleeping areas are provided.

Exam Lock Ins occur in one of the local high schools where one of the program directors is

employed as a science educator.

Counseling Services

Individual and group/family counseling services are available when needed. Students

and families who request counseling are referred to school based school counselors or the Center

for Counseling and Personal Evaluation (CCPE)

(http://www.coe.uga.edu/chds/counseling/info.html). The CCPE is an in-department training

clinic with masters and doctoral level clinicians supervised by licensed faculty psychologists at

the University of Georgia. The CCPE also is available to do psychological, Learning Disability,

and Attention Deficit Disorder evaluations. Counseling services are provided at no charge to

EYP participants and assessments are available at a significantly reduced price.

5

Community Service

EYP values civic responsibility and moral leadership. Therefore, students are provided

with volunteer and community service opportunities. In the past students have participated in the

Early Head Start Literacy Initiative, landscaped the grounds of a local High School in Athens

Georgia, participated in a city-wide clean-up of a historical African American Grave Yard,

offered free services to the elderly, and sponsored recreational events for pre-k and elementary

school students, and collected donations for relocated Katrina survivors.

Parents of Empowered Youth

As Kanter and Ferguson (1998) explain, “each community has its own needs – there is no

‘one size fits all’ after-school program. Families and community members need to participate in

all aspects of its operation to create a successful program that is culturally relevant, age

appropriate in its activities, and individualized. This collaboration among the school, the family,

and the community strengthens [programs] because it creates an environment where everyone

feels some ownership” (Kanter & Ferguson, 1998, p. 29). To incorporate this idea into EYP,

parents and guardians are given responsibility in the program as well. Families are expected to

provide transportation to and from the Saturday Academy and to attend monthly Parents of

Empowered Youth (PEY) meetings/workshops. PEY and the EYP staff work closely together to

provide quality enrichment and remedial education for program participants. Parents are asked

to be actively involved in decision-making, in establishing and following EYP policies, and in

providing encouragement and support to one another. PEY meetings are also utilized to problem

solve problems parents may be facing in helping their child succeed. Parents are provided with

6

information on how best to navigate the educational system and what role they play in helping

their children take full advantage of all the resources available.

The Problem

Adolescents in America are faced with many challenges during a time adults expect them

to solely focus on their academics. Temptation for substance use, decisions about sex, lack of

direction and purpose, and peer pressure affect many teens. Many African American teens also

face high drop out rates, poor academic performance, lack of role models, discrimination, and

racism (Bailey & Paisley, 2004). In addition to addressing academic success, EYP is structured

so as to impact students’ self-concept, self esteem, satisfaction with life, family environment, and

hope to help them build the strength and courage to face their daily personal and interpersonal

struggles as well. These are all factors that have been positively correlated with academic

achievement, though this relationship is debated at times; especially in regards to self esteem and

self-concept. The majority of the research that has been completed previously, however, has

been primarily conducted with Caucasian or immigrant families. The current expectation is that

the data analyzed in this study will reveal some information about relationships between

academic achievement and the above factors for African American adolescents.

Additionally, many achievement programs like EYP exist but a very small amount

conduct program evaluations to assess efficacy. So, we do not know which ones are truly

effective or why they are effective/ineffective. Of the programs that do program evaluations,

they primarily focus on assessing their academic interventions. This study will serve as a

program evaluation of the psychologically oriented aspects of EYP. The expectation is that the

7

results show students functioning at healthier levels across the above factors after spending a

significant amount of time in the program.

Purpose of the Study

A key piece to increasing the efficacy of programs such as EYP is to regularly assess

their impact. This way, strong components of the program can be maintained and weaker

components can be improved. For the present study it is important to understand program

participants’ self reports across measures of psychological wellbeing and the impact of EYP on

participants across these measures. Specifically, the study will assess self-reports on measures of

self-esteem, self-concept, satisfaction with life, family environment, and hope. These are all

areas the program was designed to impact.

There were two goals in assessing participant self reports across measures of self esteem,

self-concept, satisfaction with life, family environment, and hope. First, there is a gap in the

literature regarding the relationship between these factors and academic achievement in African

American adolescents; this will be examined. Additionally, the students’ first assessment and

last assessment across each domain were examined to look for any changes in their self report.

This data was then compared to normative data for each measure to identify any similarities and

differences between program participants and the normative sample.

This information will be useful for both the psychology and the education fields. First,

the study may contribute to a gap in the literature regarding the relationship between self esteem,

self-concept, satisfaction with life, family environment, and hope with academic achievement for

African American adolescents. This information may lead to understanding how these factors

should be addressed in helping African American students improve their academic achievement.

8

EYP is a creative and innovative program that addresses the needs of minority youth,

including at risk students and those who are doing well. A benefit of an enrichment program

such as EYP is that it can be used to assist everyone since all students could benefit from extra

support and help. EYP is an evolving program that aims to match the ever changing needs of a

dynamic student population. EYP’s flexible components can serve as buffers that can help

reduce gaps in student needs no matter how small or large. A major component of such

programs is rigorous evaluation to determine what aspects are impactful. By identifying these

factors, they can be communicated to the educational and psychological fields to better impact

the lives of other minority youth.

The research questions guiding this study are:

- How do African American high school age participants evaluate their lives when it

comes to their self-esteem, self-concept, hope, family environment, and satisfaction

with life?

- How do students’ ratings across all measures correlate with their academic

achievement?

- The longer students participate in EYP, do their self-reports change on measures of

self concept, self-esteem, satisfaction with life, family environment, and hope?

- Can any changes in self-reported self-esteem, self-concept, satisfaction with life,

family environment, and hope be related to EYP participation?

9

Hypotheses

1. Program participants will follow the normal distribution of self-report across

the different measures used since students range in age, grade, gender, and

ability level.

2. Students with higher achievement will have higher self-reports across hope, family

environment, and satisfaction with life.

3. Self-concept and self-esteem will not be correlated with participants’ academic

achievement.

4. The longer students participate in EYP the healthier levels they will exhibit

across all measures.

5. EYP participation will relate positively to improvements on all measures.

10

CHAPTER TWO – REVIEW OF LITERATURE

Enrichment Programs

Currently, external enrichment programs are just beginning to gain popularity across the

U.S.. External refers to programs that are established by organizations outside the formal school

system. According to Kanter and Ferguson (1998) the programs are “facilitated by

collaborations among parents, educators, colleges, work-study students, libraries, youth groups,

volunteer organizations, cultural institutions, local businesses, and community organizations.

However, such programs are just beginning to gain a presence” (Kanter & Ferguson, 1998, p.27).

Research shows that the American public is realizing the value of after school programs. A

study by Lake Snell Perry & Associates (2002) found that individuals across different socio-

economic levels were in favor of using federal funding to pay for “quality after school programs”

(Lake Snell Perry & Associates, 2002, p. 1). This is a positive perspective because it means that

even children from low-income households would have access to the resulting programs. “With

the backdrop of the continuing economic downturn, the survey finds Americans are increasingly

supportive of efforts to help low-income families” especially when it comes to funding programs

that can help children that come from low-income families (Lake Snell Perry & Associates,

2002, p. 1). However, people are not just looking for baby sitters. A recent survey found the

following.

11

- 95% of parents felt technology training would be helpful

- 91% were interested in programs that involved the arts and cultural activities

- 90% valued programs that offered community service opportunities for participants

- 90% of minority parents were interested in tutoring of basic skills (Kanter &

Ferguson, 1998).

It should be noted that the EYP program incorporates technology training, arts, community

service opportunities, and tutoring of basic skills in its curriculum.

In addition to parent desires for enrichment programs, existing research has some

suggestions for making an enrichment program successful. First, when programs involve middle

school students, it is important that program creators remember this is a time of change for the

students. “Students in their early teens are growing rapidly, both physically and mentally, but

they still need to count on, talk to, and confide in adults. In addition to family members and

teachers, they also need mentors, such as counselors, coaches, or neighbors” (Kanter &

Ferguson, 1998). Although this study only examines high school students, EYP includes

participants from kindergarten to high school seniors. The program attempts to meet students

where they are along their developmental path and works with families, teachers, mentors, and

counselors to meet the students’ needs.

Enrichment programs can also be used to boost the education gifted children receive.

EYP attempts to do this with both disadvantaged and gifted students in the program.

Researchers show that enrichment programs, along with early identification and parental

involvement can help gifted students’ learning and growth (McIntosh, 1995). McIntosh (1995)

suggests four components in helping ethnic minority gifted students. First, she emphasizes the

need for early identification. The earlier students are identified the sooner programs and

12

education can be tailored to their specific needs. She also encourages incorporating the student’s

culture into the curriculum. This helps give the student a sense of value, helps them feel more

connected to the program, and gives those around the student a better understanding of the

student. The third piece is teacher education. Teachers often are not able to identify gifted

students, nor do they know how to foster the student’s ability. Through teacher education they

are better equipped to work with the student. The fourth component involves parent education.

As mentioned above parent involvement is key to a child’s success, but parents need to know

how they can help their gifted child. Of these suggested components, EYP is most involved in

parent education. The program works with parents of all the children in order to help them give

their child the best education available. There are frequent workshops, the directors regularly

meet with parents one on one, and the entire staff is ready and available to attend parent meetings

upon request.

Family Environment

It was decided that family environment would be examined because research has shown

that a family environment can influence many aspects of a child’s life. For example, a family

environment perceived by the child to be lacking cohesiveness was correlated to higher rates of

depression when the child entered ages 18-26 (Reinherz et. al., 2003). Parental expectations

have also been found to reduce adolescent alcohol use and accompanying problems, reduce

friendships with peers that drink, and increase self efficacy to avoid drinking (Nash, McQueen,

& Bray, 2005). A healthy family environment has also been found to be a protective factor

against adolescent delinquency (Sullivan, 2006). Family environments have even been found to

alter how a child’s brain functions. One study shows that at risk families resulted in children

13

who’se amygdales did not respond comparatively to that of healthy families (Wolpert, 2006).

The relationship between family environment and academic achievement has not been studied

much, however, the studies that exist have shown positive correlations between family

environment and academic achievement (Marjoribanks, 1981, Mohanraj, 2005). Mohanraj

(2005), in a study with South Asian Indians in India, specifically found perceived higher child

independence and low conflict family environments to be positively correlated with higher

academic performance. This relationship has been found with ethnic minority participants as

well as majority participants (Kurdek & Sinclair, 1988; Luster & McAdoo, 1996; Marjoribanks,

198). One study examined first, second and third generation Mexican American families and

found differing aspects of the family environment being linked to academic achievement

(Rodríguez, 2002).

There have been problems identified with the existing research on the link between

family environment and academic achievement. According to Marjoribanks (1981) some of the

problems in the research include studies failing to “(a)…use sensitive environmental measures,

(b) [they] have not examined associations between environment measures and academic

achievement at different levels of children’s intellectual ability, and (c) [they] have not analyzed

associations between family socialization processes and academic achievement for children from

different social-status and ethnic groups” (Marjoribanks, 1981, p. 155). Additionally, in the

current study I found that family environment is defined in different ways according to different

researchers. For example in the above three articles Family Environment was defined in three

different ways. Marjoribanks (1981) describes it as “…parents’ aspirations for their children and

parents’ press for English and press for reading within the family,… parents’ press for

independence for their children, and parents’ individualistic-collectivistic achievement-value

14

orientations” (Marjoribanks, 1981, 157). In their study, Kurdek and Sinclair (1988) used

sections of the family environment scale to conceptualize family environment. This included

“conflict among family members, achievement orientation, and intellectual/cultural orientation.

Because of the similarity in content and in an effort to improve scale reliability, items from the

achievement orientation and intellectual/cultural orientation scales were combined to form a

single achievement-intellectual orientation scale” (Kurdek & Sinclair, 1988, p. 91). In the

Rodríguez study the components of family environment studied included: “family involvement

in education, family monitoring of adolescent extracurricular activities, family control of

adolescent activities, and familism, which measures the extent of familial affiliation” (Rodríguez,

2002, p. 89). Although there is some overlap across studies, it becomes hard to compare them

when they do not operationalize family environment in the same way.

The current study takes the above-mentioned concerns into account. The EYP program

opted to use the Family Environment Scale for several reasons. First, it is easy to administer.

Second it uses a broader concept of family environment, which allows researchers to examine as

many factors of the family environment as possible. According to the Family Environment Scale

Manual (Moos & Moos, 2002) these factors are broken up into three dimensions and can be

described as follows.

Relationship Dimension

- Cohesion: how much commitment, help, and support family members give each

other

- Expressiveness: how much family members are welcome to say how they feel

- Conflict: how much anger and conflict is expressed by family members

15

Personal Growth Dimensions

- Independence: How asssrtive and self-sufficient family members are

- Achievement Orientation: How much academic and professional activities are seen

in a achievement or competition oriented way

- Intellectual-Cultural Orientation: How much interest exists in political, intellectual,

and cultural activities within a family

- Active-Recreational Orientation: How much a family participates in social or

recreational activities

- Moral-Religious Emphasis: How much a family focuses on ethical and religious

values

System Maintenance Dimensions

- Organization: Level of importance placed by a family on organization and structure

during family events or responsibilities

- Control: How much structured rules and procedures are applied in family functioning

As mentioned above some of these dimensions have been found to correlate differently with

different factors in a student’s life. In other words, some of the above dimensions are found to

positively correlate with aspects of students’ lives (psychological wellbeing, academic

performance, etc.) while others are negatively correlated. Thus the FES can provide specific

components of EYP participants’ lives that can be addressed by EYP programming. The FES

has also been used in a variety of studies with ethnic and cultural minorities indicating some trust

in the measure’s cultural relevance and sensitivity (Orford et.al., 2001; Peleg-Popko, Avigdor, &

Iman, 2003, Rey, Peng, & Morales-Blanquez, 2000). Finally, in the current study Marjoribanks’

concerns regarding socialization are taken into consideration as well since program participants

16

come from different socio-economic backgrounds. This study also further examines the link

between family environment and academic achievement in African American adolescent

students. It will assess for any relationship between student self-report of family environment

and academic achievement. Additionally, it will gauge the impact of EYP participation on

reports of family environment.

Self-Concept

“A self-concept is the association of the concept of self with one or more (nonvalence)

attribute concepts” (Greenwald et. al., 2002). Self concept impacts people’s self-esteem and how

they see themselves in a social context whether this is family or friendships. Problems with self-

concept can lead to emotional and mental difficulties (Gale Encyclopedia of Psychology, 2001).

Self-concept has been positively correlated with life satisfaction. It also has been linked to

motivation to effect change in one’s life (Strelnieks, 2005). The body of literature suggests

disagreement regarding the existence between self-concept and academic achievement. For

example, Garzalli & Everhart (1993) found this relationship had a strong positive correlation for

gifted seventh and eighth grade students, however, they did not find a correlation for

academically weak students of the same grade range. Another study (Braverman, 2003) found a

significant difference between adolescents with high self-esteem and low self-esteem across their

academic achievement, life satisfaction, tardiness, and participation in extra-curricular activities.

High self-concept students performed at healthier levels across all these domains. According to

Braverman, this has important implications for “practice [including] the design, implementation,

and monitoring of school-based programs to enhance self-concept” (Braverman, 2003, 66). One

study used the Multidimensional Self-Concept Scale and found Academic Self-Concept and

Physical Self-Concept portions of the assessment to accurately predict academic performance in

17

minority adolescents (Strelnieks, 2005). The current study sought to further examine the link

between self-concept and academic achievement in African American adolescent students in

order to alleviate some of the confusion in this area, at least in relation to African American high

school students. It assessed for relationships between student self-report of self-concept and

academic achievement. Additionally it gauged the relationship of EYP participation on reports

of family environment.

Self Esteem

Self-esteem is “an individual's sense of his or her value or worth, or the extent to which a

person values, approves of, appreciates, prizes, or likes him or herself” (Blascovich & Tomaka,

1991). Another commonly used definition is “a favorable or unfavorable attitude toward the

self” (Rosenberg, 1965, p.15). Self esteem has implications for a person’s sense of efficacy and

worthiness in life (Center for Conscious Living, 2003). A person’s self esteem “has profound

effects on [his or her] thinking processes, emotions, desires, values, choices, and goals” (Center

for Conscious Living, 2003). Essentially, self esteem is important because “how you feel about

yourself affects how you act” (National Network for Child Care, 1995). Both general and

academic specific self-esteem have been positively associated with academic performance

(Valentine, DuBois, & Cooper, 2004). Additionally, research has shown that healthy levels of

self-esteem are also associated with lower school drop out rates and motivation to do well

academically (Blum & Jones, 1993; Somers & Piliawsky, 2004). However, the Valentine,

DuBois, & Cooper (2004) meta-analytic review of self-beliefs and achievement shows both the

promising findings and some of the problems of research on self-esteem and academic

achievement. Overall, high school students showed the strongest positive correlation between

18

the two factors. Additionally, there was no statistical difference in reports based on gender.

However, when race and ethnicity of study participants is examined, it becomes clear that more

research is necessary when examining this relationship. Of the 32 studies examining self-beliefs

in the meta-analysis, six specifically looked at self-esteem and all six had significant effect sizes

showing a positive relationship. However of the six studies, totaling 3,656 participants, only

three studies provided information on the ethnicity of the participants. In the three studies,

totaling 1,533 participants, approximately 1290 (84%), were White. No other information was

provided regarding the race/ethnicity of the other 16% of the participants. Additionally, as

Valentine, DuBois, & Cooper (2004) point out, of the studies that address results in African

American participants, they do not do an adequate job of extricating the effects of socio-

economic status versus race/ethnicity in their results.

The current study is important because it can provide an examination of the relationship

between self-esteem and academic achievement for African American students. Additionally,

with information on the family SES status available, SES as a variable can be examined

separately and jointly with race/ethnicity. Finally, the relationship between EYP participation

and self-reports of self esteem were gauged as well.

Satisfaction with Life

Satisfaction with life is seen as the cognitive component of subjective well-being

(Andrews & Withey, 1976; Pavot & Diener, 1993). Pavot & Diener (1993) refer to satisfaction

with life as “a judgmental process in which individuals asses the quality of their lives on the

basis of their own unique set of criteria” (Pavot & Diener, 1993, p. 164). It is an important

factor to examine in because mental health and life satisfaction have also been positively

19

correlated (Piko, 2006). There are several factors that contribute to life satisfaction including

contentment with family and friend relationships, enjoying one’s work or school, and one’s sense

of worth (Diener, 2006). Research examining relationships between academic achievement and

life satisfaction have yielded mixed results. Some show a relation between life satisfaction and

academic achievement (Bennett & Okinaka, 1990) while others yield results showing an indirect

relationship (Peterson, 2000). For example, Peterson (2000) found satisfaction to be positively

correlated with students who felt they had an academic/life direction and with students who were

more involved with school activities. However, those students who had a stronger direction and

were more involved in their schools were also the ones who were high academic achievers.

Therefore, satisfaction with life can be used as an indirect indicator of what may be contributing

to a student’s level of achievement.

In the current study the relationship between satisfaction with life and academic

achievement in African American adolescent students was studied. Relationships between

student self-report of satisfaction with life and academic achievement was examined.

Additionally the relationship of EYP participation on reports of hope will be gauged.

Hope

Hope is important because it gives people a chance a reason to keep trying and pushing

forward even when things are difficult. As previously mentioned, hope is defined as “the

process of thinking about one’s goals, along with the motivation to move toward those goals

(agency), and the ways to achieve those goals (pathways)” (Snyder, 1995, p. 355). In other

words, pathways embodies the idea of having a sought outcome in mind and having an idea of

what needs to happen to reach this goal while agency embodies having the determination to carry

20

through with one’s part in making the goal happen. These two components are independent

concepts, but both are necessary to fully encompass the concept of hope (Snyder et. al., 1991).

There is, however, some debate as to the importance of one of these concepts over the other. For

example, Cramer & Dyrkacz (1998) report the motivational component of hope to be more

important than finding a way to pursue the sought goal. On the other hand, it has been said that

“in the absence of the strategies to be implemented, goal-directed motivation is useless” (Irving,

Snyder, & Crowson, 1998, p. 197). It is important to differentiate hope from optimism since

they have yielded differing results. After controlling for optimism, hope has been positively

correlated with problem-focused coping, positive mental health outcomes, and positive health

outcomes. However, this was not the case when assessing optimism after controlling hope

(Kashdan et al., 2002; Snyder et al., 1991; Scioli, 1997).

Very few studies were found that assess the relationship between hope and academic

achievement. Two that were found did show a positive correlation between these two variables.

Snyder et. al. (2002) found college freshman grades were positively correlated with levels of

hope after controlling for entrance exam scores. Additionally, the researchers found that high

hope also predicted a higher chance of college graduation. According to Contini (2000), hope

allows students to approach their academic career with an emphasis and expectation of success.

However, there is research assessing Goal Theory and its connections with academic settings.

For example, Dweck (1999) discusses how students tend to pursue learning goals or performance

goals and how this results in adaptive and maladaptive achievement behaviors. However, at this

time it is unclear as to why students gravitate towards one type of goal over the other

(Covington, 2000). Hope theorists propose “that goals themselves do not produce behaviors, but

rather, people’s views of themselves as being agents capable of “initiating (agency) and

21

implementing (pathways) actions to pursue valued personal goals…produce the” mastery or

performance oriented responses (Snyder, et. al., 2002).

This study examined the relationship between hope and academic achievement in African

American adolescent students. It assessed for relationships between student self-report of hope

and academic achievement. Additionally, it gauged the relationship of EYP participation and

reports of hope.

The Current Study

There is much support for enrichment programs. They are a good way to supplement

traditional K-12 educations while being able to introduce other skills and opportunities.

Enrichment programs are also beneficial for low achieving to gifted students. However, to

increase program efficacy it is important to constantly assess and evaluate program interventions.

This study intended to do this with the Empowered Youth Programs by assessing program

participants’ self-evaluations on self-concept, self-esteem, satisfaction with life, family

environment, and hope.

In the process this study addressed two issues. First, gathering information on how

African American students self evaluate across the different variables. Second, how effective is

EYP in helping program participants maintain healthy levels of self concept, self-esteem,

satisfaction with life, family environment, and hope. This is important because of the problems

regarding existing literature linking self-concept, self esteem, satisfaction with life, family

environment, and hope with academic achievement. Prior studies have had difficulty with the

appropriateness of instruments used to assess the constructs and population. This study used

carefully selected instruments that were appropriate for the age and race/ethnicity of participants.

22

Additionally, the relationship of the different factors with academic achievement sometimes has

not been studied thoroughly (i.e. satisfaction with life) while some of the research (i.e. with

satisfaction with life and self-concept) has yielded contradictory results. By studying a specific

age group and race/ethnicity across these factors, this study may provide some clarity at least

with the specific population involved. Finally, this study provides information on a group of

participants who are not commonly participants in this type of research. The participants in this

study are African American high school boys and girls who vary in achievement level, who

represent different socio-economic levels , who are not all considered “at risk”, and who are not

mandated to participate in the program. Considering the rarity of this type of participant, this

study will hopefully shed some light on self-concept, self-esteem, satisfaction with life, family

environment, and hope with this population.

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CHAPTER 3 - METHODOLOGY

The components of EYP target both psychological and academic success. This study

took a quantitative approach to assess the impact of EYP on the psychological well-being of

program participants. Specifically, it assessed the impact of a psychoeducational program

designed to assist minority youth on self-concept, satisfaction in life, hope, and the family

environment. Currently, data on enrichment programs mainly focuses on the impact they have

on the participants’ academic achievement. This is the case despite the data linking

psychological well-being and academic achievement. Additionally, much of the literature in

existence focuses on ethnic majority students. There are only a small number of enrichment

programs whose participants are primarily African American, and of these, very few have

published on the mental impact of their program.

Participants

Participants of this study were active members of the EYP who were present for

assessment administrations at the beginning and end of each academic school year from August

2001 to May 2006. At the time of assessment administration they were in the 9th through the 12th

grade. They attended high schools in Clarke and surrounding Georgia counties. Due to self

selection, all participants were male and female ethnic minority students of African American

and Korean ethnicities. Their academic abilities ranged from low achievement to high

achievement.

25

Procedures

Program participants find out about the program through word of mouth and

handouts/fliers from friends, parents, and teachers. Before the beginning of each year’s Saturday

Academies, there is an orientation meeting that all students and parent/guardians are expected to

attend. At this meeting a program overview was provided, the staff was introduced, and the

parents completed the application materials to have their child(ren) enrolled in the program.

This application packet included a formal application with contact information as well as the

child’s current class schedule,a parent consent form giving EYP permission to work with his/her

child permission as well as to include his/her child’s information in any research conducted

through the program that year, a student assent form that the student signs agreeing to participate

in the program and any research that may come out of the program, a permission to release

information form that is given to teachers and schools allowing the program to collect formal

academic feedback from teachers and schools, and a liability release form in case any illnesses or

accidents occur during an EYP event.

As part of a program evaluation process, all high school level EYP participants were

asked to complete a battery of assessments at the beginning and the end of each academic school

year. Anyone who chose not to do so was free to decline this process. This assessment was

administered at the first Saturday Academy of the year. When students entered the program mid-

year, they were given the assessments within two weeks of entering the program. This equaled

up to two Saturday Academy Attendances before the completion of the assessment packet. As

each student entered the program he/she was given an identification number under which all

assessments were stored. Names were not attached to assessments. Returning students kept their

26

participant number. At the end of the year, either at the last Saturday Academy or during the

Exam Lock in May, participants completed the battery of assessments again.

As the assessments were completed, the information was entered into the database under

the students’ identification number. Currently, there exists four years of assessment data. This

database was accessed for this study. All data are locked and secure with no possibility of

defining individual identities, except by the program director, Bailey and necessary EYP staff.

As mentioned above, during the orientation sessions parents signed a release that

allowed participants’ schools to release information on academic and classroom behavior to

EYP. Academic monitors maintained bi-monthly contact with teachers and school counselors to

collect this information using the Academic Monitoring Forms. Additionally, progress reports

were collected at the end of grading periods, semesters, and academic school years. This

information was collected for all participants and was entered into a database.

Instruments

This study aimed to assess participants’ self-esteem, self-concept, satisfaction with life,

family environment, and hope.

Rosenberg Self Esteem Scale

Self-esteem was measured through the Rosenberg SelfEsteem Scale (SES). “The

Rosenberg Self-Esteem Scale is perhaps the most widely-used self-esteem measure in social

science research” (University of Maryland Department of Sociology, 2005). The SES was

developed in the 1960s by the sociologist Dr. M. Rosenberg. The 10 items were answered on a

four point Likert scale ranging from strongly agree to strongly disagree. At its development, the

27

instrument was normed with “5,024 high school juniors and seniors from 10 randomly selected

schools in New York State and was scored as a Guttman scale. The scale generally has high

reliability: test-retest correlations are typically in the range of .82 to .88, and Cronbach's alpha

for various samples are in the range of .77 to .88. Studies have demonstrated both a

unidimensional and a two-factor (self-confidence and self-deprecation) structure to the scale”

(University of Maryland Department of Sociology, 2005). Scores had a possible range of 0-30.

Multidimensional Self-Concept Scale

Self-Concept was measured through the Multidimensional Self-Concept Scale (MSCS).

The MSCS “is a thoroughly researched and standardized clinical instrument” that “assesses

global self-concept and six context-dependent self-concept domains that are functionally and

theoretically important in the social-emotional adjustment of youth and adolescents” (ProEd,

2005). They include social self-concept, competence self-concept, affect self-concept, academic

self-concept, family self-concept, and physical self-concept. “Each MSCS subscale evidences

very high reliability (coefficient alpha > .90), and the Total Scale Score reliability exceeds .97

for the total sample” (ProEd, 2005). The MSCS has been found to highly correlate with other

measures of self-concept and self-esteem. The MSCS can be administered either to individuals or

to groups in approximately 20 minutes. For this study, it was administered in small groups

ranging from one to 10 students at a sitting. “The scale is simple to score and interpretation

allows for both norm-referenced and interchild comparisons across each of the six scales. Scores

are reported as standard scores (M = 100, SD = 15) or as T scores (M = 50, SD = 10) and can be

graphically displayed for ease of interpretation” (ProEd, 2005).

28

The Family Environment Scale

The Family Environment Scale (FES) was used to measure program impact on the

family. This instrument was first chosen because of the ease of administration. Considering

some of the administrators are clinicians in training it was important to make administration as

simple as possible. Second, the FES also provides a variety of information on participants’

families. In other words the FES not only provides information on whether or not a family is

doing well, but it also gives insight into what aspects of a family are especially strong or

problematic. This measure evaluates the “social environment of the family unit” using 90 items

that are grouped into ten subscales “measuring three underlying dimensions of the family

environment: Relationship, Personal Growth, System Maintenance and Change.” It takes

approximately 15-20 minutes to administer and has been normed for individuals 11 years and up

(Mindgarden, Inc., 2005). The FES was normed on 1,432 normal and 788 distressed families.

This included families from across the U.S., single parent families, multigenerational families,

racial and ethnic minority families, and families with children across age groups. The FES has a

strong internal consistency (Cronbach’s Alpha >.73) and a strong test-retest reliability

(Cronbach’s Alpha > .73). The validity of this measure was ascertained by comparing it to the

Family Assessment Device (FAD) and the Family Adaptapability and Cohesion Evaluation Scale

(FACES II). This yielded high correlations across subsections of the FES indicating a strong

convergent validity (Moos & Moos, 2002).

The Hope Scale

The Hope Scale was used to measure levels of hope experienced by program participants.

This measure, which has been translated into 20 languages, contains eight hope items and four

29

filler items. The four items assessing determination reflect past, present and future experiences.

“The four pathways items pertain to people’s cognitive appraisals of their ability to generate

means for surmounting goal-related obstacles and reaching goals” (Snyder et al., 1991, p. 572).

Participants complete the assessment using an 8-point Likert scale describing “how they

generally perceive themselves in goal pursuits across situational contexts” (Snyder et al., 2002,

p. 822).

The Hope Scale is designed for older children and adults and was normed using three

populations including University of Kansas introductory psychology students, one outpatient

group of individuals seeking psychological treatment, and one inpatient group of individuals.

Construct validity was established by demonstrating that higher hope individuals, as measured

by the Hope Scale, generate more ideas to help themselves reach their goals and that they have

more mental agency to follow these ideas (Kahle & Snyder, 2001; Snyder et al., 1991). In

assessing concurrent validity, the Hope scale has been positively correlated with scales

measuring similar concepts including optimism, Generalized Expectancy for Success Scale,

problem solving, self-efficacy, and self-esteem. (Scheier & Carver, 1985; Fibel & Hale, 1978;

Heppner & Petersen, 1982; Magaletta & Oliver, 1999; Rosenberg, 1965). According to Snyder

et. al. (1991), the internal reliability meets the .70-.80 acceptability rate needed for research

purposes since the total Cronbach’s alpha ranged from .74-.84. The temporal stability of the

measure has also been ascertained by test-retest correlations falling at 0.85, p<0.001 over a 3

week interval (Anderson, 1988); 0.73, p<0.001 over an 8 week interval (Harney, 1989); and 0.76

and 0.82 respectively, p<0.001 over a 10 week interval (Gibb, 1990). Finally, strong construct

and discriminant validity have been established for this measure.

30

The Satisfaction with Life Scale

The Satisfaction with Life Scale was used to assess participants’ satisfaction with life.

This is a five item assessment that students answer using a seven point Likert scale. The scores

range from five to thirty-five with a higher score indicating higher satisfaction. This scale has a

2-month test-retest correlation coefficient of 0.82 and an alpha coefficient of 0.87 for a sample of

176 undergraduates from the University of Illinois (Diener et al., 1985).

Rather than assessing domain specific satisfaction this measure allows students to gauge

their overall life. This is a measure with strong convergent validity when compared to other

scales of subjective well-being. This is also a measure that has been shown to be sensitive

enough to show subtle changes when given as pre and post assessments for clinical interventions.

Finally, the Satisfaction With Life Scale “shows discriminant validity from emotional well-being

measures” (Pavot & Diener, 1993).

31

CHAPTER FOUR - RESULTS

Anticipated Data Set

The data set anticipated before examination spanned fall 2001 through fall 2005. With

two semesters per academic school year, this included nine semesters of data. There were five

assessments being considered with each instrument encompassing multiple subscales (please

note above instrument description for detailed information on subscales):

Family Environment Scale: 10 Subscales

Hope Scale: 3 Subscales

Multidimensional Self Concept Scale: 7 subscales

SES: No subscales

Satisfaction With Life Scale: No subscales

This means there were 22 potential scale scores per student per semester. For the nine

semesters a data matrix of scale scores would potentially consist of X rows representing number

of students with some scores by Y columns where Y would equals 198 (i.e. 22x9).

In the manipulation of grade data, only academic class grades (English/Literature,

Mathematics, and Science) were considered since all students are required to take these courses.

Elective courses are less consistent across students and thus harder to compare. Therefore, the

matrix for grades is expected to consist of X rows of students by 27 columns (Y=9 semesters by

3 grades per semester).

32

Actual Data Set When data compilation began, it quickly became clear that there were many gaps in the

data. Once a data matrix was constructed, the extent of the problem became clearer. First, it was

found data were actually not available until Fall of 2002 reducing the number of anticipated

semesters from 9 to 7. Table 6 shows a matrix of measures and semesters for which scale score

data are available. There were no semesters for which complete data existed across all 5

measures. Scores by instruments varied from only two semesters of data (for FES) to a

maximum of 6 semesters of data (SES). Additionally, the Hope and SES measures were the only

instruments for which scale scores were available across consecutive semesters with no semester

data gap. Returning to the matrix table, it was found that there were 166 participants with one or

more scores (scale score for SES and SWLS, and subscale score for the rest). This meant a data

matrix of 166 rows by 154 columns (Y=154 from 7 semesters X 22 subscales of scores). Upon

populating this matrix table with data, it was found that many of the cells showed missing data.

Please note Table 6 for the graphical matrix. This table provides the summary of available data

as a matrix of instruments and school semester with the available number (n) of scale scores out

of a maximum possibility of 166 per instrument. Please refer to Tables 7 through 13b for

information on existing data for each semester across instruments and subscales. The data for

grades proved to be the sparsest. The details on grades are available in Table 14.

The following section of this chapter addresses actual statistical analyses. First I will

provide descriptive information on how students in EYP generally tend to score across the

different measures. This will provide time specific snapshots of how students are doing at any

given point. This information is not intended for evaluation of how EYP is affecting students.

Instead, it is a description of how EYP participants generally are doing at any given point. After

33

the descriptive information, the rest of the results have been organized according to research

hypotheses. The original hypotheses are presented followed by the relevant statistical analysis

and findings.

Findings by Instruments

FES

Creators of the FES conducted research to assess how normal and distressed individuals

scored on their measure and created descriptive FES scores for these two populations (Moos,

2002, p. 111). Table 1 summarizes these statistics and those of the EYP participants from this

research alongside the normal and distressed normative data of the manual. Detailed

comparisons of the EYP and the normative scores are presented later in the hypothesis V section

of the results chapter. As table 1 shows, EYP participants scored comparatively to the normal

normative sample on the Fall 2005 Moral-Religious Emphasis subscale. For all other subscales

EYP participants fared worse than the normal sample scores and at times were statistically

comparable to the distressed normative sample. The negative difference between a “normal”

population and EYP participants (with EYP students scoring at an unhealthier level) was found

to be statistically significant for all subscales across all FES data except the Moral-Religious

Emphasis subscale (for 2004 and 2005 data) and the 2005 Achievement Orientation subscale.

Essentially, the findings of the FES portion of the study suggest that apart from the Fall

2005 scores EYP participants’ scores are not similar to those of healthy individuals. After

discussing this with program Director Dr. Bailey, I was informed how this is what the program

expects considering how a large number of program participants are considered at risk.

34

However, these findings need to be considered carefully considering there were several problems

with the data. First, the small N size could be impacting the findings. This finding could be

retested by collecting more EYP data over time and re-examining the results. Second, the

normative data for the FES is debated by researchers. Several researchers have cross checked

the validity, reliability, and the normative samples provided in the FES manual and question its

strength (Roosa & Beals, 1990; Boyd et. al., 1997). Third, the possibility exists that some

program participants did not take the assessments seriously and did not answer in a way that

truly reflects their families of origin. If enough participants did this there could have been a

resulting lowered mean score that is more reflective of these inaccurate assessments rather than

the overall well being of program participants. Fourth the Fall 2005 data could have been

negatively impacted by a surge in program participants. Some of these individuals were new to

the program altogether and without proper guidance might have had difficulty understanding the

assessments. It would be ideal if these findings could be more rigorously re-examined and

retested with a larger EYP sample size to see if these findings are truly reflective of the

population.

Hope

The overall summary of Hope Scale results are provided in Table 2. Instrument creator

Snyder (1995) has stated a hope total score of >24 suggests a hopeful person while <24 suggests

a person is not hopeful. However, the author did not make distinctions between healthy and

unhealthy levels of hope. In general, however, researchers have often created their own

interpretation of what constitutes high, medium, or low levels of hope. When using Snyder’s

interpretation of scores, 75% of participants would fall in the hopeful category. Semester means

would indicate all are hopeful individuals. I devised my own three-level class interpretation as

35

another method for discerning interpretation of scores by dividing scores into low/medium/high

classes. Total possible scores for pathways, agency, and total scores were simply divided into

equal thirds. Therefore, for pathways and agency (which have a maximum possible score of 16)

low score equals 5.33 or lower, a medium score is denoted by responses between 5.34 and 10.67.

A high score would then be 10.68 to 16. To calculate the Hope Total ranges (possible range of

scores between 8 and 32) 8 was subtracted from 32 and the remaining number (24) was divided

by three (8). So a low score ranges from 8 (the lowest possible score) to 16. A medium score

ranges from 17-24. A high score ranges from 25-32. It is important to point out that a low Hope

score does not necessarily denote unhealthy levels of hope since there is no established scoring

criterion denoting healthy vs. unhealthy levels of hope.

Based on these criteria, Table 2 shows all scores fall with in high ranges of scores except

the Spring 2004 Hope Total score. That score fell at the highest point of the medium range.

Although it cannot be stated for certain based on the above explanation, such high scores

potentially indicate healthy levels of hope for participants of this study. To further complicate

interpretation of this assessment, I was not able to find widely accepted normative data for the

Hope Scale so a comparison group was not available for this measure. This makes it hard to

assess how these students are doing compared to other similar students in the general public.

The factors affecting previously discussed assessments also impact the hope scale. The

results for this study would be significantly enhanced if the sample sizes were larger.

Additionally, students’ seriousness in completing the measures had not been gauged. A third

factor to consider here is how low/median/high scores could be defined. The above table shows

one approach, but there are a wide variety of ways researchers have used this measure. Another

simple way to break down the scores would be to use the median score as an indicator of score

36

variation separating out low from high scores in respect to the middle ground. This approach

would not be impacted by outliers.

These results are difficult to compare to other research since Hope Scale results are

interpreted in differing manners. If the validity of the result in this study could be strengthened

this could be a valuable contribution to the body of literature because the role of hope in the

wellbeing and especially academic performance of adolescents is rarely studied (Gilman,

Dooley, & Florell, 2006). Further, research in this area with minority adolescents is even rarer.

MSCS

For the MSCS, instrument creators established a scoring scale to assess how individuals

are doing based on their response to the measure. Table 3 shows the results of the semester

MSCS scores color coded to represent the manual’s scoring interpretations. As the table shows,

all mean scores from this study fell in the average range except the Spring 2005 Family Self

Concept subscale score, which fell in the moderately positive range. Therefore on average

student self-reports indicate they are in the healthy range for all aspects of self concept measured

by this instrument. When compared to normative data presented by the MSCS Manual

(Hypothesis V results) EYP students show significantly higher scores across all subscales except

MSCS Total. In other words, EYP participants exhibit higher self concept across their social

life, competence level, affect, academic performance, views on their families, and views about

their physical appearance. However, when it comes to their overall self concept they are similar

to the normative sample in the MSCS manual.

There could be several reasons for this divergence from a large normative sample when it

comes to the subscales. First, enough students may have artificially inflated their scores that

significantly skewed the overall data pool. Second, there might be some common factor specific

37

to this population of students from the same region of the country that is bolstering their self

concept. One of these factors could potentially be EYP participation. One way to test this

theory is to find a control sample of students from the same schools as EYP participants who

complete the assessments at the same times as EYP participants without taking part in EYP.

Whatever the reason for this finding, it will be interesting to continue to collect this data with

larger sample sizes since it provides information on a population for which this type of data is

not readily available.

SES

Similar to the Hope Scale, creators of the Rosenberg Self-Esteem Scale did not provide

interpretation of scores for the measure. If a similar approach to the MSCS (the instrument

measuring the closest construct to self-esteem) is taken then the range of SES scores could be

interpreted as shown in Table 4 (the numbers are created by assessing what percentage of the

total possible score each of the MSCS categories signify and finding those percentages for the

total possible of SES Scores). As shown by Table 4, all mean SES scores fall in an average

range indicating healthy Self Concepts across time. The deduction of healthy made here needs to

be verified, however, with further research indicating what SES scores truly indicate a “healthy”

self esteem. I was not able to find a normative sample to compare these scores with so it is

unclear whether these results converge or diverge from the average score on this measure. It is

good news for students to score in such a high range across all semesters of SES data. As

mentioned above it is possible that enough students artificially inflated their scores to the extent

that this significantly and positively impacted the means. About 7% of the observations were in

the upper outlier category. There might also be some common factor specific to this population

38

of students from the same region of the country that is bolstering their self concept. One of these

factors could potentially be EYP participation. One way to test this theory is to find a control

sample of students from the same schools as EYP participants and comparing their SES scores to

those of EYP participants. Whatever the reason for this finding, it will be interesting to continue

to collect this data with larger sample sizes since it provides information on a population for

which this type of data is not readily available.

SWLS

Table 5 shows EYP scores on the Satisfaction with Life scale as interpreted by the

scoring guide for the instrument. As shown Table 5, SWLS scores exist for Spring 2003, Fall

2004, and Fall 2005. The Spring 2003 and Fall 2004 mean scores fall in the satisfied range. The

Fall 2005 mean scores fall in the Slightly Satisfied range. When looking at these overall, it

appears these students are doing fairly well when it comes to life satisfaction. Twenty-five

percent of the scores fall in the extremely satisfied class. Normative data was not available for

the SWLS so a comparison group was not available for this measure. This makes it hard to assess

how these students are doing compared to the general public. It does, however, provide numbers

for a sample of minority adolescents. It would be interesting to see how these numbers might

change with a larger sample of EYP participants. As mentioned above this data might also have

been affected by how seriously the students considered their answers before putting them down.

Now that student’s responses to the assessments have been examined, the discussion

continues to address results pertaining to this study’s hypotheses.

39

Hypothesis I: Program Participants Will Follow the Normal Distribution of Self-Report Across

the Different Measures Used Since Students Range In Age, Grade, Gender, and Ability Level

Data by Semesters

Skewness, kurtosis, boxplots, normal probability plots (rankit plots), histograms, and

Shapiro Wilk test were used to assess normality. The Shapiro-Wilk test is one of the most

discerning measures of normality. Therefore it was used as the primary test to assess for

normality across instruments despite its sensitivity to outliers (outliers can influence a data set

being diagnosed as non-normal when in fact it is). The test does not indicate the cause of non-

normality. Summaries of descriptive statistics along with the Shapiro-Wilk test for normality are

provided in Table 15, for each of the 22 possible subscales under consideration.

Table 16b shows all measures across all semesters and how results came out generally

when it comes to normal distribution. All but the Fall 2005 semester dataset showed normality

was above 72 percent. Fall 2005 data was only 16.7 percent normal. The reason why these Fall

2005 data appear un-proportionally non-normal is not clear. While students’ self-reports would

be expected to be diverse considering they vary in grade (9-12), age, and socioeconomic status,

the reason for such high degree of non-normality is not clear.

The statistics for the individual semester scores are summarized in Table 15 . Boxplots,

histograms, and kurtosis/skewness charts are also available upon request. There are 79 rows of

data in the Table 15, which represent the number of records that exist for the instrument scores

from individual semesters. Table 15 is arranged in 3 groups based on the Pr<W value of the

Shapiro Wilk Test. The Pr<W value of the Shapiro-Wilk test indicates whether the results are

normally distributed or not. If the Pr<W is less than 0.05 then the results are not normally

distributed. If Pr<W are over 0.05 then the results are normally distributed. The first 27 rows

40

(#1 to #27, column 1) display data where Pr<W values indicate strong non-normality. Data for

scales in rows 28 through 31 (#28 - #31, column 1) might be considered approximately normally

distributed because the Pr<W value is close enough to 0.05. Rows 32 through 79 have Pr<W

values greater than 0.05 indicating normal distribution of the appropriate data. Boxplots indicate

that while 50% of the data (bound by the boxes) show tight grouping around the median or mean,

the upper and lower 25% values are spread out suggesting outliers as being problems with

normality in some of the scores as shown in Table 15. In other words individual semester scores

fall into three categories, normally distributed, approximately normally distributed and non-

normal distribution. Below is a description of what semester scores were/were not found to be

normally distributed. Implications of findings are addressed in detail in the discussion chapter.

For individual semesters, the number of scores (n) varied from 11-83 (Table 6). While

the Shapiro-Wilk test can handle both small and large n sizes (3- 2000), a smaller n can have

higher instances of false non-normal distribution results because of relatively high variability.

FES

Available semester data for FES were limited to only Fall 2004 and Fall 2005. As shown

in Table 11, all but Active-Recreational Orientation, Control, and Moral-Religious Emphasis

subscales of the Fall 2004 FES scores (#1, #2, #3 column 6, Table 15) were normally distributed.

The Fall 2005 data were non-normal across all subscales except Expressiveness (#39 column 6,

Table 15). Based on individual semester scores then, hypothesis I is NOT REJECTED for all

Fall 2004 data except the 3 subscales Active-Recreational Orientation, Control, Moral-Religious

Emphasis, and REJECTED for all but the Expressiveness subscale of the Fall 2005 data. This

means that the fall 2004 data was fairly normally distributed but that it changed completely in

41

Fall of 2005. In fall 2005 there was a significant jump in sample size which may have affected

the results. These results indicate that generally FES scores are low but that they are varied.

Therefore it would be helpful for EYP facilitators to understand that family environment is an

area EYP participants need help with but that interventions need to be created keeping in mind

that there are a wide range of families that are coming though the program.

Hope

Five sets of sequential semester data exist for Hope. Of these Hope-P scores for Spring

2003, and Fall 2004, and Hope-A scores for Fall 2003, Fall 2004, and Spring 2004 were non-

normal (#13 to #17 column 1, Table 15). The rest were either approximately normal or normal.

It was hypothesized that Hope Total scores would be normally distributed. This hypothesis is

NOT REJECTED for Hope-Total based on individual semester scores. Hope –Total is the

overall indicator of hope for this measure therefore the fact that it is normally distributed is the

most important finding for this measure. This implies that there is a wide range of hope among

program participants. This means EYP programming directed at helping students maintain

healthy levels of hope needs to consider the variety of hope students are coming into the program

with.

MSCS

Looking at this measure by semesters, five subscales in 2005 and one in 2003 were found

to be non-normal (#18 to #23 column 6, Table 15). The rest of the MSCS semester data were all

normally distributed (Table 15). It was hypothesized that MSCS Total scores, the measure of

overall self-concept, would be normally distributed. It was found that MSCS Total scores were

42

normally distributed for all semester scores except Fall 2005. It is possible that a series of

extreme outliers contributed to Fall 2005 MSCS Total being non-normal, which if taken out

would lead to normality. On balance, therefore, it seems reasonable to ACCEPT hypothesis I for

MSCS-Total that scores of this instrument are normally distributed. Again, this finding has

implications on how EYP programming targets participants’ self-concept. Hypothesis I results

indicate that students exhibit a wide range in self-concept. Therefore interventions would have

to be planned out with this in mind.

SES

Scores for Fall 2003 and Spring 2005 (#24 and #25 column 6, Table 15) were non-normal

while the remaining 4 semesters of SES data (Fall 2002, Fall 2004, Spring 2003, and Spring

2004; #75 to #78 column 6, Table 15) were normally distributed. Therefore, hypothesis I is

REJECTED for Fall 2003, Fall 2005. It is NOT REJECTED for SES data in Fall 2002, Spring

2003, and Spring 2004. These findings show that except for 1 semester of data all the SES

scores were normally distributed. This indicates students’ self esteem varies across participants.

Program interventions impacting self esteem need to be able to reach students where students are

in order to be most effective.

SWLS

Of the three semesters of data, Fall 2004 and Fall 2005 were non-normal while the Spring

2003 data were normally distributed. Therefore hypothesis I is REJECTED for Fall 2004 and

Fall 2005 data. The hypothesis is NOT REJECTED for Spring 2003 data. The SWLS data was

43

more non-normal than the other instruments. It is important for program facilitators to

understand how this data is skewed in order to better address the needs of participants.

Hypothesis II: Students with Higher Achievement Will Have Higher Self-Reports Across Hope,

Family Environment, and Satisfaction with Life

Hypothesis III: Self Concept and Self Esteem Will Not Be Correlated With Participants’

Academic Achievement

As mentioned above, data on grades for the three classes (English literature,

Mathematics, and Science) were significantly smaller than those for the five assessment

measures (Please see Table 14). In responding to Hypotheses II and III the plan had been to

determine the correlation between self-reports on assessments and grades by pairing the two

relevant sets of data based on the student ID number. In other words, each student’s available

self-reports on psychological measures was going to be compiled alongside his or her available

grades. The scores of participants who had the necessary data were compiled as shown in Tables

18-12. For clarification, the second column titled “No” represents anyone of the 166 rows of

data that actually exist as previously explained. Any break in the sequence of numbers from 1 to

166 in this column indicates that necessary assessment and grades data were missing. In the

gender column, the number 1 represents males and 2 represents females. As the tables show,

there are significant gaps in these dataset. In order to accept or reject hypothesis II and III

Pearson correlation analyses were going to be carried out on paired observations of grades and

assessment or assessment subscale for each semester. A minimum of 3 observations is required

to compute the Pearson correlation coefficient and t-test of significance. In many cases only 3

observations or less were available making it difficult to generalize the findings. After careful

44

consideration it was decided that there data is insufficient to run the analyses. Due to significant

gaps in the data the findings would not be strong enough to be accepted as accurate

representation of the students.

Hypothesis IV: The Longer Students Participate In EYP The Healthier Levels They Will Exhibit

Across All Measures

Length of student participation was defined by the number of semesters a student

participated in the program. This information was collected by seeing how many assessments

students completed. Initially, there was hope that program attendance would give more detailed

information on how many EYP “treatments” students participated in but this information was not

consistently available for the same time span as the assessment data. As a result number of

assessments completed was used as an indicator of how many semesters students participated in

the program. Upon making this decision, the data for each measure was first sorted by the

number of semesters each student completed the assessment. This produced several

combinations of data sets for potential use in an analysis of variance (ANOVA). Number of

semesters of participation was used as the independent variable and scores on instruments was

seen as the dependent variable. Data sets for one semester spanned across several of the total

semesters thus forming replications. Similarly two and three semester data sets formed

replications. Scores for the maximum number of semesters students participated in for each

instrument formed only one data set with no replication for that particular instrument.

To explain this in another way, different data sets were created to organize student

responses by how many times a particular person completed a particular assessment. One data

set comprised of students who only completed an instrument once. They were pulled out as a

45

group that could be used as a control group in the ANOVA for that instrument considering they

do not have pre-post comparison scores indicating minimal EYP exposure (this is especially true

considering first time assessments are frequently completed as students first enter the program

before they have exposure to any of it’s programming.) A second data set was made up of

students who completed a particular instrument twice. A third data set comprised of students

who completed an assessment three times, and so on. The number of observations for each

replication was variable. Based on this configuration data sets were created as input files for an

ANOVA for each instrument.

The schematic structure of the dataset is shown in Fig. 2.1 of Appendix A. Data were

configured as Y- by X-group. The Y-group indicates groups with single or multiple semester

scores. The X-Grouping is a mechanism to pool the scores of each semester separately. These X-

groups were used directly in ANOVA. The actual datasets are presented in Tables 17a through

17e for each of the five instruments, respectively.

Group A of Group 1 (Fig. 2.1) represents the control group indicated above. In Tables

17a to 17e this group was given a special code of 10 in column 5. The rest of the groups are

coded with the relevant semester number in this column (hence 1 to 6 from Group B to Group F,

Fig. 2.1). The SAS code was formulated to test differences of score means among X-groups.

Shapiro-Wilk normality test was carried out on the data set prior to ANOVA. The results

are summarized in Table 18. According to the test these data- sets are normal or approximately

normal with exception of SES and FES subscales MRE and CONT of X-group 2 which had p-

values of 0.008 or less. In examining the scores it was decided that outliers were the primary

reason for non-normality. As an example, the test of SES in X-group 5 showed non-normality

with 12 observations. When one outlier was removed the test showed the remaining 11 scores to

46

be normal. Considering the hypersensitivity of the Shapiro-Wilk normality test the significance

of the test is in question when the number of observations is small as in some of the X-groups.

Therefore ANOVA analysis was used with the assumption that any non-normality was a result of

the test’s sensitivity due to problems with the database rather than any indication of true non-

normality.

A summary of the ANOVA output with respect to Type III Sum of Squares for semester

number as source of variation is presented in Table 19. The Pr>F of the model for each measure

except FES-Conflict and FES-Moral-Religious Emphasis subscales was > 0.29 essentially

indicating no statistical difference among X-group means across all instruments except for the

two subscales. Since the control group is taken as a separate observation (semester), total

number of semester observations is the sum of actual number of semesters plus the control

group, hence the degree of freedom results in Table 19.

Hope-Total

The ANOVA showed no statistical difference among X-group means (Table 19) for

Hope-Total. Fig. 2.2 seems to generally bear this out with the exception of the mean score for the

4th semester of the 8 students that participated in 4 semesters for this measure. While not

supported by the ANOVA, the greater mean score of these 8 students in semester 4 suggests

improvement with each ensuing semester of participation. The 25 percentile score is about 26,

which is greater than for the other semesters. Unfortunately the number of students who

participated in larger numbers of semesters was much smaller than those who participated in

smaller number of semesters, which might have had undue influence on the statistical output.

There were only 3 students that lasted through 5 semesters of participation for the Hope measure

47

(Fig. 2.2). It is difficult to give weight to scores based on only three students. In other words, the

expectation was that the longer students participated in the program the higher their assessment

scores would be but this was found not to be statistically significantly true for Hope-Total scores

therefore hypothesis IV is rejected in respect to hope. Despite having to reject hypothesis IV due

to mean score changes not being statistically significant, it appears from Fig. 2.2 Hope-Total the

longer participants were in the program the higher their hope scores appears to have become.

MSCS-Total

The ANOVA outcome for MSCS-Total is similar to that of the Hope-Total measure

(Table 19). Nevertheless, Fig. 2.2 indicates a slight inching upward of the mean semester scores

for students with multiple semester participation. Twenty students had a third semester mean

score as well as a 25 percentile score numerically greater than two or one semester scores. The

number of students who made it through 4 semesters of EYP, however, dropped to six with a

drop in the mean score. Again, hypothesis IV is rejected due to the ANOVA not yielding

statistically significant results, however, the longer participants were in the program the higher

their mean MSCS-Total scores appear to became.

SES

The SES scores appear to have had proportionally more outliers than scores for other

measures. The ANOVA indicates no effect for multiple semester participation for SES (Table

19). However, as shown in Fig. 2.3 the mean scores for students with multiple semester

participation are numerically greater than for the control group, although only slightly.

48

Hypothesis IV is rejected here as well due to statistically insignificant results regarding self

esteem.

SWLS

The ANOVA indicated no difference among X-group means (Table 19). The 2nd semester

score of students with more than one semester participation was slightly greater than the control

group (Fig. 2.3). Hypothesis IV is rejected here as well due to statistically insignificant ANOVA

results. However, there were slightly higher SWLS mean scores for students with two semesters

of EYP versus the control group.

FES

The ANOVA indicated multiple semester participation as impacting scores for FES-

Conflict and FES-Moral-Religious Emphasis subscales (Table 19). Boxplots are available upon

request. The mean scores for FES-Conflict were different only between the control group and

semester 1 scores of students with multiple semester participation, with the mean score of the

control group being greater indicating an environment of more conflict (i.e. less conflict under

semester 1 group). The mean score of the second semester was also slightly less than the control

group’s. The ANOVA result for Moral-Religious Emphasis was mixed. Compared to the control

group, the mean score of the first semester group was smaller while that of the second semester

was the same. The mean score for the second semester was greater than the mean score for the

first semester for students with multiple semester participation. While not statistically

significant, numerically slightly greater mean second semester scores were observed for some

FES subscales including Cohesion, Expressiveness, Independence, Achievement Orientation and

49

Intellectual-Cultural Orientation. Based on these results hypothesis IV is not rejected for the

FES-Conflict subscale but rejected for the other subscales. In other words students reported

statistically less conflict in their family when they had two semesters of EYP participation. All

other findings were not in line with the expectation that student scores would improve the longer

students participated in the program.

Hypothesis V: EYP Participation Will Have a Positive Impact Across All Measures.

SES, SWLS, and Hope

Hypothesis V is the most difficult hypothesis to respond to. First EYP is only one aspect

of students’ lives and second there was no external control group for comparison. The original

hope was to find a normative data set for all the measures and compare participants’ scores with

the normative data. However, a normative data set was only found for the FES and MSCS. In

considering the other measures, hypothesis IV gives some clues to hypothesis V. Hypothesis IV

results indicate that participants showed higher scores in hope, self-esteem, and satisfaction with

life when they participated in at least two semesters of EYP. Unfortunately these findings were

not statistically significant. As discussed under hypothesis IV, however, this may be due to

problems with the database rather than truly insignificant results. Although EYP participation

cannot be extracted as a cause for this small but positive change, there is a possibility that it

could be having a positive influence.

MSCS-Total

As explained above, hypothesis IV is rejected due to the ANOVA not yielding

statistically significant results, however, a trend of numerically greater means were observed

50

with the longer participants. Additionally, in considering hypothesis V, a comparison between

EYP participants and normative data was completed. Normative data for the MSCS is provided

in the MSCS manual. This information is valuable in assessing how program participants

compare to a general sample of similar students. In the MSCS manual this normative data is

shown in Table 2.4 of Chapter 2: Development and Standardization of MSCS (Bracken, 1992).

This table is organized by what grade a student is in and provides sample size, mean, and

standard deviations for each subscale. The normative data was constructed from 2501 students

from grades 5 through 12 (1427 of whom were in grades 5 to 8). At this time, EYP participant’

grades (what year in high school they are in) is not available therefore direct comparisons by

grades, as is listed in the manual, was not feasible. Instead, the EYP data were compared to the

pooled (grade 5 through 12) data provided in the manual which are summarized in Table 20.

MSCS scores for our students were available for Spring 2003, Fall 2003, Fall 2004, Spring 2005,

and Fall 2005 (Table 8 through Table 13a).

First, 95% confidence intervals were constructed for the normative data above using One-

Sample T procedure. (Moore & McCabe, 1989). As was discussed under hypothesis I, the test

for normality showed that the MSCS data were generally normally distributed. It was shown that

removing a few outliers produced normally distributed data. A One-Sample T Test was used to

test the null hypothesis that the means of the MSCS assessment scores are equal to the normative

means given above. According to Moore and McCabe (1989), only normality of means of

samples is required by the t procedures (sample means of observations tend toward normal

distribution even of populations are not normal).

Results are presented in Table 21 and Fig. 2.1 to Fig. 2.4 (Appendix B). As can be seen

in Table 21, for every MSCS subscale except MSCS-Total the P-values for the One-Sample T

51

test are all <0.0001 for all the semesters under consideration. Therefore the null hypothesis that

means of these MSCS subscales are equal to means of the equivalent normative data is rejected.

The graphs in Fig. 2.1 to Fig. 2.3, where no overlap of the 95% confidence intervals is observed

between the means of the normative data and those of program participants, support this

conclusion as well. In fact the means of program participants are 20 or more units greater than

the normative means with exception of Family subscale whose semester mean scores are

approximately 15 units greater than the normative means. In other words, program participants

reported significantly higher levels of self-concept across all MSCS subscales than did the

normative sample. It is important to revisit that over half the sample for the normative table

comprised of 5th-8th graders. This might impact the difference between EYP students and the

normative sample.

For the MSCS-Total scores, Table 21 shows that at the alpha=0.05 level one cannot reject

the null hypothesis suggesting that the MSCS-Total means of the normative data and those of

EYP Participants are statistically equivalent. However, at the alpha =0.1 level it can be rejected

for Fall 2003 and Fall 2004 (P=0.0711 and 0.0647 respectively). Note that the normative means

and standard deviation for MSCS-Total are given as “raw scores” while the scores for other

subscales are given in “scaled scores”. It is unclear why researchers chose to organize their

normative sample this way. It is also unclear how all subscale samples could be so different

between the normative sample and EYP participants and yet the Total subscale results remain

statistically the same. This will be examined further in the discussion chapter.

52

FES

As explained under hypothesis IV the ANOVA indicated multiple semester participation

was positively impacting scores for the FES-Conflict subscale. The ANOVA was statistically

significant for FES-Conflict. While not statistically significant, numerically slightly greater

mean second semester scores were also observed for subscales Cohesion, Expressiveness,

Independence, Achievement Orientation and Intellectual-Cultural Orientation. These results

suggest that having at least two semesters of EYP yields better reported family environments for

program participants. In considering hypothesis V, a comparison between EYP participants and

normative data was completed as well. The FES manual provides researched normative data

regarding people who are “normal” and those who are “distressed” (Moos, 2002, p. 111). These

data are summarized in Table 22.

In order to further examine hypothesis V means for the FES were compared with

normative data for normal and distressed individuals given in the Table 22. Assessment scores

were available for Fall 2004 and Fall 2005 (Table 11b and Table 13b).

First, 95% confidence intervals were constructed for the normative data above using One-

Sample T procedure (Moore & McCabe, 1989). Results are presented in Fig. 2.4 (Appendix B).

All FES Fall 2004 assessment subscales except Active-Recreational Orientation, Control, and

Moral-Religious Emphasis were normally distributed. All FES Fall 2005 assessment subscales

except Expressiveness were non-normal. The One-Sample T Test was used to test the null

hypothesis that the means of the FES assessment scores are equal to the normative means for

normal and distressed individuals given above. The alternative hypothesis was that the means

were not equal. According to Moore and McCabe (1989), only normality of means of samples is

53

required by the t procedures (sample means of observations tend toward normal distribution even

of populations are not normal). Results are presented in Table 23 and Fig. 2.5 to Fig. 2.6.

Normal individuals

fall 2004.

As shown in Table 23, the P-values with respect to normal individuals are <0.05 with the

exception of that for Moral-Religious Emphasis. The null hypothesis that means of Fall 2004

scores are equal to means of the normative data is thus rejected for all but the Moral-Religious

Emphasis subscale. This conclusion is supported by Fig. 2.5 where no overlap of the 95%

confidence intervals is observed between the normative data and those of the Fall 2004 FES

measure scores. In fact, even for Moral-Religious Emphasis the overlap is hardly noticeable.

Other than the Moral-Religious Emphasis subscale, students’ scores are actually worse than the

“Normal” normative data provided by the FES manual. The expectation was that EYP

participants would have higher FES scores than the normative data. Therefore hypothesis V is

rejected for Fall 2004 FES Scores. The implications of this finding are addressed in the

discussion chapter.

fall 2005.

Table 23 shows P-Values > 0.05 for two subscales, Achievement Orientation and Moral-

Religious Emphasis, leading to acceptance of the null hypothesis for these two measures and

rejecting it for the rest of the FES subscales. In other words, these two subscales are statistically

close to that of “Normal” individuals of the FES manual while all other Fall 2005 subscales are

statistically different. Fig. 2.5 again supports these conclusions by illustrating overlap of the

54

95% confidence intervals is observed for Achievement Orientation and Moral-Religious

Emphasis but not for the other subscales. Hypothesis V is rejected here as well though because

students’ scores are largely worse than the “Normal” normative data provided by the FES

manual. The implications of this finding are addressed in the discussion chapter.

Distressed Individuals

Considering hypothesis V aims to compare program participants with “normal”

individuals, the following FES information is not being evaluated in terms of hypothesis V. The

following information is provided to further illustrate findings of how students scored on the FES

scale.

fall 2004.

Means for three subscales, Expressiveness, Moral-Religious Emphasis, and Organization, can be

considered the same as the equivalent normative means for distressed individuals (P value >

0.05, Table 21; Fig. 2.6) of the FES manual.

fall 2005.

All measures had different means than the normative data for distressed individuals (P

value <0.05, Table 21; Fig.2.6).

Overall Hypothesis V Results

Except for those measures that have the same means as the normative data as explained

above, assessment measure means are less than those of the normative data with exception of

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Conflict and Control subscales. This suggests that overall EYP participants provided a more

negative self-assessment than that of the “normal” normative sample. Higher Conflict subscale

scores mean more conflict in the home of EYP participants. So participants are registering a

more negative self-assessment compared to the normative sample for that subscale as well.

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CHAPTER FIVE - DISCUSSION

This discussion chapter will provide several types of information. First, it will discuss

the implications of the results on each instrument. Second, each individual hypothesis is

discussed independently. Finally a discussion is presented on limitations of this study and ways

to improve on it.

Descriptive Findings by Instruments

Overall across all instruments except the FES mean scores were in the average to high

ranges. Except for the FES, these scores tended to stay within the same range over time with

minimal fluctuation. For all those instruments the results imply that on average students enter

the program in a healthy emotional state across the dimensions studied (self-concept, self esteem,

satisfaction with life, and hope) and that they continue to do well across the dimensions while in

the program. This implies there may be little room for improvement across these dimensions. If

this is the case then EYP programming may want to use its resources to target other more

problematic areas in students’ lives. One factor that may contribute to these results is that the

students who self-select into the program may already be fairly healthy. As previously

discussed, some other areas EYP already targets include academic achievement, career planning,

civic responsibility, and social excellence.

The way the data was organized, it provided snapshots of scores per semester without

differentiating new students from returning students. In other words, it provided information of

how students tend to score as a group at any given semester. It would be

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interesting to analyze the scores after separating out beginners from returnees to asses how they

may be similar or different.

FES scores suggest that family environment is a problem for the majority of EYP

participants. In speaking with the founder and director, he expressed no surprise at this finding.

Many EYP participants come from difficult backgrounds and according to the founder and

director this is being accurately portrayed by their FES responses. It should be taken into

consideration that the responses are from the students’ perspectives. It would be interesting to

cross check these findings by having parents fill out the parent version of the FES as well. The

stark difference between the high scores on all other instruments and the low scores on the FES

is interesting as well. This goes against the existing literature by suggesting that the other factors

examined (self-concept, self esteem, satisfaction with life, and hope) may not be so heavily

influenced by a person’s family environment. Throughout this study the FES has been difficult

to work with because it yielded inconsistent results, results that go against existing literature, and

because it lacks an overall comprehensive score. Therefore the fact that the FES results contrasts

so strongly both from the existing literature and the findings of other instruments in this study

need to be considered carefully for their meaning to determine whether the findings could be

influenced by problems with the interpretation of the instrument or the lack of applicability of

the instrument to this population. If the findings are accurate it could mean that students’

resilience is not as strongly impacted by unhealthy family environments as previously thought.

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Hypothesis I: Program Participants Will Follow the Normal Distribution of Self-Report Across

the Different Measures Used Since Students Range In Age, Grade, Gender, and Ability Level

Normal distribution is important for two reasons. First, I had hoped to show that EYP

participants are a diverse group of students with varying needs but who represent a normal

sample of the general population. EYP staff members conceptualize student scores as being

normally distributed and thus EYP programs aim to reach students across a broad area of needs.

This assumption is well supported by research where many phenomena in nature appear to

follow an approximate normal distribution. Normally distributed data can be succinctly

conceptualized with the mean and variance serving as helpful parameters of the distribution. If it

is found that students do not follow normal distribution across the measures, it provides

important information for EYP staff. This information can be used to better target interventions

to better help areas of deficiency in students’ emotional well-being.

The second reason is that many statistical analyses (parametric) are based on the

assumption that data are normally or approximately normally distributed. However, due to a

variety of factors, including sample size as evidenced in this study, it is often difficult to truly

assess for normal distribution. In such an instance with the right test of normality a strong

enough case for approximate normality can be made. The Shapiro-Wilk test, a common test for

normality and used in this study, does not guarantee normality but provides evidence of certain

type of non-normality when the data are not normally distributed.

Table 16b gives a breakdown of normal versus non-normal data by semesters. For six of

the semesters (all but Fall 2005) 72 through 100 percent of the observations were considered

normal or approximately so. On average 80% of the data were normal or approximately so. In

following the Central Limit Theorem this finding suggests that these assessment instruments

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scores are probably all normally distributed on a larger scale. – i.e. if the current clusters of data

have such high probability of normality, normative data with much higher observation numbers

would most likely be normal. Hence the underlying distribution of these measures might be

normal. Such a high level of normal distribution and the implications of the Central Limit

Theorum suggest that it is important that EYP assumes students enter the program from diverse

points of well being. This is part of the underlying assumption of EYP and thus programming is

designed to be able to meet students where they are in terms of academics and emotional well

being.

Fall 2005 yielded interesting findings, though, when it comes to distribution. Fall 2005,

of which only 16.7% was found to be normally distributed, brings the average of 80% normality

to 66% when all semesters are considered. Non-normality could be caused by measurement

errors, outliers, and a variety of external factors. It may be helpful to further examine outliers in

the findings because they may indicate students who are doing especially well or are dealing

with deficiencies. This was not done for this study due to the amount of analysis that was

already being done. Additionally, this study aimed to look at overall trends rather than

individual students’ results. This information, again, can better help EYP target its interventions

as they relate to emotional well-being.

It is not clear why the Fall 2005 scores showed so much less normality than other

semesters considering the N size jumped from 20s and 30s to 60s and higher. Fall 2005 was

affected by unusually high levels of divergence and outliers. That semester my duties in EYP

changed and I was available to catch every single new comer to ensure he/she completed the

assessments before beginning EYP. This was not always possible to do in the past. Some of the

30 or so newcomers to the Fall 2005 EYP participants were novices to the program and their

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scores represent one of a pre-EYP participation. There is not a post-EYP score for them. With a

larger N size the chance of students completing the instruments without truly understanding it is

higher. The above could be one reason for such relatively high degree of non-normality. Another

possibility is that enough students misrepresented true scores by artificially either over or under

inflating them. By misrepresenting themselves students can hide any problems or highlight the

ones that they want to draw attention to. In all cases it would be interesting to collect data on a

control population to assess differences between EYP participation and non-participation. A

fourth possible cause for non-normality is that students did not take the assessments seriously

and “Christmas treed” their answers. This seems unlikely to have happened as a group, though,

because if it had been the case then responses would have shown more diversity and range than

there were. To truly assess normality of this population on these measures it would be helpful to

have a more complete assessment database where larger samples are collected per semester.

Hypothesis II: Students with Higher Achievement Will Have Higher Self-Reports Across Hope,

Family Environment, and Satisfaction with Life

Hypothesis III: Self Concept and Self Esteem Will Not Be Correlated With Participants’

Academic Achievement

The discussion of hypotheses 2 and 3 will be presented here together considering they are

very similar. Additionally the same problems were encountered for both hypotheses during data

analysis. The initial intent was to determine the correlation between self-reports on assessments

and grades by pairing the two relevant sets of data based on the student ID number. However,

table 14 shows vey large gaps in the existing grads database for the time frame of this study.

After careful consideration it was decided that the data was too sparse to run any meaningful

analysis.

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Hypothesis IV: The Longer Students Participate In EYP The Healthier Levels They Will Exhibit

Across All Measures

In examining hypothesis IV the data was organized to create groups of students who had

completed the same number of assessments (one to six) on each measure. Those who completed

one particular assessment only once with no additional assessment were grouped and served as a

control group. Next an analysis of variance (ANOVA) was run on each group comparing their

assessment score means across semesters. Overall there was no statistical difference between

group means for Hope-Total, MSCS-Total, SES, and SWLS. The ANOVA indicated multiple

semester participation as impacting scores for FES-Conflict and FES-Moral-Religious Emphasis

but none of the other FES subscales. Despite these statistical results there appears to have been

increasing trends of slightly higher but statistically nonsignificant means with increasing

duration of EYP participation. With the Conflict subscale, it was found that the control group

had statistically higher reports of family conflict than students who were in the program longer.

This finding is interesting because the first comparison of scores were done at the first time they

completed the instruments. When the two groups took the measure at the initial time, they

scored with a statistically significant difference. It is unclear as to why this finding resulted.

One possible explanation is that the control group consisted of students who took the measure at

the start of the semester and the second group consisted of enough students who might have had

some EYP experience before taking the assessment for the first time (ex: starting the program

mid semester, not having completed the assessments when enrolling, and then took their first

assessment at the end of the year with students who were taking it for the second time that

academic year.) Another likely explanation is that the variance and small N size may have

contributed to this finding. Additionally, it may give insight into the type of student who may

62

stick with a program like EYP. The control group represents students who drop out of the

program for various reasons. It may mean that students with higher conflict in the home have

more difficulty being able to commit to a program like EYP.

Although statistically significant, the ANOVA findings of the Moral-Religious Emphasis

subscale means were mixed. Compared to the control group, the mean score of the first semester

group was smaller while that of the second semester was the same as the control group. In other

words scores at one point for the control group, were higher for the first semester scores of the

non-control group, and then was lower again for the second set of scores for the intervention

group. In considering this result the first thought that comes to mind is that EYP does not

heavily address Moral-Religious topics with students or families. This is not a faith or morality

based program and although appropriate behavior and personal responsibility are addressed EYP

does not take a Moral-Religious perspective on its programming. So it is not surprising that

results for this subscale showed a fluctuation of scores rather than a consistent increase in scores

that other FES subscales and other instruments had. These score fluctuations are likely due to

factors other than EYP participation considering EYP does not have components that strictly

address family Moral-Religious Emphasis.

Overall, in considering hypotheses IV results there were no “negative” findings. Scores

showed less distress the longer students participate in the program. The majority of the findings

showed slight “positive” though statistically nonsignificant changes in assessment means the

longer students participated in the program. One important problem that has been a running

theme in this study is the small numbers of completed assessments. In this case the ANOVA test

might not have been sensitive enough to pick out differences among X-group means because of

small number of scores with progressive semester participation. Another problem with assessing

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these hypotheses was the outliers which persisted throughout measures and across semesters.

Considering these problems and given the analysis it is difficult to draw conclusions regarding

causality. These students are influenced by a wide variety of factors in their daily lives including

family, friends, teachers, school, as well as other programs they participate in. Additionally, they

are growing and maturing in the four years of high school. Some of these small changes may

simply be due to the natural maturation process.

Hypothesis V: EYP Participation Will Have a Positive Impact Across All Measures

As mentioned before, causality is very difficult to infer based on how this study was

designed. In order to truly assess causality there are several things that would need to be in

place. First, it would be necessary to have a comparable control group that completed the

assessments at the same time as EYP participants without taking part in EYP. Second, it would

have been essential to have participants randomly assigned to a treatment and control group. For

the present study, however, a sample of convenience was used rather than an controlled

treatment and control sample. Third, thorough attendance records would help to assess how

many “doses” of EYP participants are truly receiving. However, none of these steps were

available for this particular study. When this data was collected, the primary purpose of EYP

was to provide a service to its participants rather than collect data on them. In focusing on

service provision, some of these research related guidelines were not focused upon.

However, there is some information that can be gleaned from the data that exists. First,

even though it was not statistically significant Hypothesis IV results suggests students who spend

more time in the program show healthier scores on the measures. In fact, family conflict went

down at a statistically significant level. Additionally, normative samples exist for FES and

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MSCS measures. EYP scores were compared to these normative samples to see how students

fared compared to non-EYP participants. With the FES, it was found that EYP students scored

consistently and significantly at unhealthier levels than the “normal” normative sample that is

reported in the FES manual. With the MSCS, EYP students scored at significantly higher (and

healthier) levels across all subscales. However, when it comes to overall self-concept, students

scored at a level comparable to the normative sample. This disparity between FES and MSCS

results is very puzzling. Additionally, it is unclear how MSCS subscales can vary so

dramatically from normative data and the overall self-concept difference still not be statistically

significant. As previously mentioned, some researchers question normative data provided by the

FES; therefore these results may seem worse than they actually are. In other words, as

previously discussed researchers found that the normative data provided by the FES are not

necessarily accurate. If this is true then the scores for this study may mean that participants’

family environments may not be as un-healthy as is implied by the FES normative sample.

When it comes to the MSCS comparative sample, the grade level ranged from fifth graders to

high school seniors. This larger age difference may contribute to score differences. If EYP is a

contributing factor to these results, then it might mean that self-concept is better addressed by

program components than family environment is. However, it is difficult to tease out how these

results came to be or what they mean. Without a randomized independent control group, a larger

sample size, and longer data collection it is hard to say why the results came out as they did. It is

quite possible that EYP contributed to these positive findings. Based on what I have seen I

believe some of the findings of the study may have been too impacted by the limitations of the

study to provide a true sense of EYP students’ state. Statistical backing for what EYP facilitators

65

see every day would be very beneficial. In order to do so, however, there would need to be some

adjustments in how data is collected.

Limitations of Study and Future Recommendations

The biggest problem found in this study was the varying and often very small sample

size. There were several factors that contributed to this. First, the program is limited in the

number of students it can accept at any given time considering the limited numbers of volunteers

that are available. When participant numbers were well into the 80s it was difficult to attend to

all the students in an age appropriate manner while trying to stretch out the limited resources

available. Larger participant numbers meant students received less personal attention.

Therefore, although program evaluation is important it is also important that the purpose of the

program is not lost. EYP exists first and foremost as a service to the participants and their

families. Therefore, although increasing participant size is helpful for research the service to

students should not be lost in the process.

With that said, there are ways to maximize the numbers that do come through EYP each

year. The first step is to try and minimize missing data. The attendance database is one database

that would have been more helpful had it been more complete. Saturday Academy and lock in

attendance can be beneficial for assessing how many “doses” of EYP each student is getting.

Beginning with Spring 2005 the attendance database appeared to be well maintained but this was

not the case prior to that. In addition to having the database well maintained it would be helpful

to have it stored in a way that would transfer well to research. This will make it easy to

incorporate into research quickly. Clear dates are important of course. Also, storing it in a way

66

that can transfer easily to SPSS or other statistical software would be helpful. It would also be

helpful to summarize the data by semesters along the way.

The grades database was another database that was necessary but difficult to work with in

this study. The first problem was that the database data had missing data. This could

presumably be fixed. Considering all release forms for students are kept, EYP volunteers and

staff could easily go to these students’ schools and collect old grades from students past and

present to fill in any existing holes. Once this information is collected, it might be helpful to find

a way to group scores. For example, in this study databases were created for all science, math,

and English/literature grades. Doing so might even impact what grades are collected or stored

since it may be deemed that some grades are not necessary to keep. One suggestion for

consideration is whether grades are the best measure of academic performance for research.

Grades tend to be somewhat subjective measures and it might be helpful to collect other markers

of academic performance that are less subjective and contain the same materials across all EYP

participants. This way researchers know the students are being tested on a standard format

making comparison more valid. This might also be an easier database to manage since there will

be fewer data points in that kind of a database.

The assessment database was the third database used in this study. The problems

encountered here included differing administration of assessments, miss-scored assessments, un-

entered assessments, incomplete assessments, and missing assessments. In training staff to

administer assessments it was found that different staff members administered assessments

differently. Additionally, there were changes from year to year on how assessments were

administered. Some semesters data collection occurred in small groups, other times in large

groups, and at times some measures were administered one on one. Although it is unclear how

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much of an impact these different methods had, it goes without saying as much uniformity as

possible can only help program evaluation and student change. When it comes to how

assessments are scored, a checking system would be beneficial. Although not frequently, I have

at times found scoring errors when going back and looking over unusual assessment scores. A

checking system for scoring assessments would reduce this problem. There were also semesters

where due to staff shortages assessments were not entered immediately. It took one full week of

working close to 40 hours to enter all missing assessments that were found in the EYP office into

an existing assessment database. In doing so I found some assessments without ID numbers or

ones that were incomplete and thus were not usable. Having a faster turnaround time from

assessment administration to scoring and entering could minimize these problems. The ID

problems were resolved in the second half of the assessment administration because assessment

packets with ID numbers on all assessments were being made in advance to minimize that

problem. The problem of incomplete and missing assessments might be resolved by this quicker

turn around time as well. In the Fall of 2005, when a faster turn around time was implemented,

the number of completed assessments jumped significantly. There was an average of 39

completed assessments in Fall of 2004 and an average of 71 completed assessments in Fall of

2005. (This number is somewhat affected by the number of EYP participants that were enrolled

at the beginning of each of those years.) Monitoring students closely as they complete

assessments is another way to minimize incomplete or missing assessments. This can also help

to assess fatigue when students answer questions so they can be given necessary encouragement

or breaks. This way their answers are more likely to reflect how they truly perceive themselves.

Another way to get honest and more complete responses is to provide some sort of follow up or

feedback to students about the instruments so it has meaning for them and their parents. In doing

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so, however, it will be important to maintain the confidentiality they are promised as much as is

possible.

In addition to problems with sample size, completing the data analysis of this study was

difficult. Considering this project was attempting to merge three very different databases

(databases included the assessment database, attendance database, and grades database) that had

been independently collected in three very different manners. Just the assessment database alone

was very large in itself because it included scores for the five psychological assessments. Had I

to do this project again I would not have tried to answer so many different questions in one

sitting. Had I taken any one of the assessments and examined them alone there would have been

enough data to manipulate and write up. However, the merging of these five very different

assessments of the assessment database with the attendance database and the grades database

proved to be very complicated.

Ways to possibly minimize this complication can begin with re-examining the

assessments used for this study. Are there shorter measures of self concept and family

environment? Considering they each have several subscales, unless studied independently these

two are hard to manage. At least with the self concept scale there is a MSCS-Total subscale that

can be used independently while factoring in the other MSCS subscales. The FES has 10

subscales that have to all be examined independently to get an overall sense of family

environment. Additionally, the FES has higher levels of controversy in the literature regarding

what the scores actually mean for those who complete them (Boyd et. al., 1997; Roosa & Beals,

1990). Furthermore, towards the end of writing up this study I became aware of a student

Satisfaction with Life Scale and a Student Hope scale. These may be more appropriate for

program participants since these instruments better target participants’ age ranges. A con with

69

changing these measures now, though, would be that it would be harder to take longitudinal data

that includes previous assessments before an instrument change.

Finally, in addition to sample size and data analysis difficulties a third limitation of this

study comes when interpreting results. Although the hope had been that results could speak to

program impact on emotional well-being this is difficult to do without a similar control sample.

In future EYP research it would be beneficial to find a one time or long term group of willing

participants apart from program participants. Another interpretation problem arises in the

definition of length of EYP participation for hypotheses 4 and 5. Currently, number of times

participants completed instruments wound up being the indicator of how many semesters

participants completed. This does not indicate whether participants completed full semesters in

the program, though. Initially the hope was to use program attendance to assess how many

Saturdays a person participated in the program, however, this was not possible to do as described

above. Continuing the more rigorous attendance record begun in Spring of 2005 will better serve

to provide true program participation in the future.

As mentioned before, EYP’s current focus is on service provision. More specifically, to

develop and nurture academic and social excellence in children and adolescents. There are many

components of EYP that are shown to be effective as discussed in the literature review. Also,

there are testimonies of parents, students, teachers, and EYP staff indicating what a positive

impact EYP has on its participants. Being able to show how and why the program impacts

students is beneficial. Research can help show the impact of the program and can help improve

the program. Hopefully the recommendations from this study will assist with future research of

EYP and other enrichment programs with similar goals.

70

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National Network for Child Care. (1995). Self Esteem and Children. Retrieved June 14, 2007 from http://www.nncc.org/Guidance/self.esteem.html.

Orford, J., et.al. (2001). Ways of coping and the health of relatives facing drug and alcohol problems in Mexico and England. Addiction 96 (5), 761-774.

Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5 (2), 164-172.

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Reinherz, H.Z., Giaconia, R.M., Stashwick, C.K., & Fitzmaurice, G. (2003). Childhood and adolescent predictors of major depression in the transition of adulthood. American Journal of Insanity, 160 (12), 2141-2147.

Rey, J.M., Peng, R., & Morales-Blanquez, C. (2000). Rating the quality of the family environment in different cultures. Journal of the American Academy of Child & Adolescent Psychiatry 39(9), 1168-1174.

Rodríguez, J.L. (2002). Family environment and achievement among three generations of Mexican American high school students. Applied Developmental Science, 2, 88-94.

Roosa, M.W., & Beals, J.B. (1990). Measurement issues in family assessment: The case of the family environment scale. Family Process 29 (2), 191–198.

73

Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

Scheier, M.F., & Carver, C.S. (1985). Optimism, coping, and health: Assessment and implications of generalized outcome expectancies. Health Psychology, 4, 219-247.

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Snyder, C.R. (1995). Conceptualizing, measuring, and nurturing hope. Journal of Counseling and Development, 73, 355-360.

Snyder, C.R. (1995). Managing for high hope. R&D Innovator, 4 (6). Obtained May 1, 2007 from http://www.winstonbrill.com/bril001/html/article_index/articles/151-200/article162_body.html

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Snyder, C.R., Shorey, H.S., Cheavens, J., Mann Pulvers, K., Adams III, V.H., & Wiklund, C. (2002). Hope and academic success in college. Journal of Educational Psychology, 94(4), 820-826.

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74

Table 1: Student FES scores and Normative Data provided in the FES Manual

FES Fall 2004 (N=22-34)

FES Fall 2005

(N=65-76)

Normal Individuals

N=5331

Distressed Individuals

N= 115 FES Subscale Mean SD Mean SD Mean SD Mean SD Cohesion 4.20 1.20 4.80 1.40 6.80 2.26 5.45 2.67Expressiveness 4.40 1.50 4.20 1.10 5.27 2.14 4.55 1.98Conflict 5.00 1.40 5.40 1.20 3.54 2.24 4.36 2.58Independence 5.00 1.20 4.80 1.40 6.35 1.61 5.84 1.81Achievement Orientation 5.30 1.10 5.70 0.90 5.90 1.65 6.07 1.78Intellectual-Cultural Orientation 5.00 1.00 5.10 1.10 5.60 2.32 4.49 2.35Active-Recreational Orientation 5.30 0.90 5.10 1.10 6.09 1.94 4.73 2.30Moral-Religious Emphasis 5.20 1.20 5.70 0.80 5.57 2.15 5.09 2.19Organization 5.10 1.20 5.00 1.10 5.67 2.17 5.41 2.29Control 6.20 1.00 5.80 1.00 4.72 2.10 4.96 2.14

75

Table 2: Summary of Hope Scale Scores

HOPE SCALE

Semester N Pathways M (SD)

Agency M (SD)

Total M (SD)

Fall 2002 23 12.9 (1.5) 12.6 (1.9) 25.4 (2.6) Spring 2003 31 13 (2.5) 13.9 (1.6) 27 (3.4) Fall 2003 41 12.3 (1.8) 13.3 (1.9) 25.6 (3.2) Spring 2004 34 12.3 (1.5) 12.6 (1.3) 24.9 (2.4) Fall 2004 36 12.6 (1.9) 13.1 (1.6) 25.9 (3.0) P or A: 10.68 – 16 or Total: 25-32 High Hope

P or A: 5.34 -10.67 or Total: 17 - 24 Medium Hope

P or A: < 5.33 or Total: < 8.00 Low Hope

76

Table 3: MSCS scores color coded to represent the manual’s scoring guide

77

Table 4: Summary of the Rosenberg Self Esteem Scale Scores

ROSENBERG SES SCORES Semester Mean SD N Above 37.2 Extremely Positive Self Esteem Fall 2002 25 2.2 24 35-37.2 Very Positive Self Esteem Spring 2003 23.7 2.7 31 32-34.5 Moderately Positive Self Esteem Fall 2003 25.9 6.8 50 23.5-31.7 Average Self Esteem Spring 2004 24.5 2.4 36 21.6-23 Moderately Negative Self Esteem Spring 2005 24.6 2.7 76 18-21.5 Very Negative Self Esteem

Below 18 Extremely Negative Self Esteem

78

Table 5: EYP scores on the Satisfaction with Life scale as interpreted by the Instrument scoring guide

SWLS SCORES 35-31 Extremely Satisfied Semester Mean SD N 26-30 Satisfied Spring 2003 25.6 5.7 32 21-25 Slightly Satisfied Fall 2004 25.7 6.7 38 20 Neutral Fall 2005 24.2 6.7 73 15-19 Slightly Dissatisfied

14-Oct Extremely Dissatisfied

.

79

Table 6. Matrix of Available Scores Across Measures and Semesters1

School SemesterInstrument Measure Fall

2002 Spring 2003

Fall 2003

Spring 2004

Fall 2004

Spring 2005

Fall 2005

FES 22-34 65-76

Hope 23 31 41 34 36

MSCS 29 51 49 11-36 67-71

SES 24 31 50 36 47 83

SWLS 32 38 73

1 Numbers in highlighted cells indicate N available scores (observations) out of a maximum of 166 each. Statistics for N were: mean 44.1, median 36, standard deviation 17.4, minimum 11, and maximum 76. These were derived from the 78 scale and sub-scale instruments with scores for the semesters shown above.

FES – Family Environment Scale, 10 subscales HOPE – Hope Scale, 3 subscales MSCS – Multidimensional Self Concept Scale, 7 subscales SES – Self Esteem Scale, no subscales SWLS – Satisfaction With Life Scale, no subscales

80

Table 7. Available Assessment Scores for Fall 20021

FL 2002 Hope Fall 2002No Student ID Gender SES Hope -P Hope-A Hope -Total

1 79 1 27 13 13 26 2 80 1 27 13 10 23 3 81 1 22 15 14 29 4 82 1 24 11 8 19 5 83 1 25 11 10 21 6 84 1 21 12 13 25 7 100 1 27 12 12 24

10 103 1 24 12 14 26 13 107 1 24 13 15 28 17 111 1 21 16 9 25 18 112 1 25 20 114 1 29 14 12 26 21 115 1 28 10 11 21 32 129 1 27 14 13 27 72 200 2 13 15 28 74 202 2 24 75 203 2 26 12 13 25 79 207 2 28 13 12 25 94 223 2 26 12 14 26 96 225 2 11 12 23

102 232 2 26 14 14 28 103 235 2 25 104 236 2 26 14 13 27 108 242 2 23 15 13 28 109 243 2 24 13 14 27 110 244 2 22 13 15 28

Total scores 24 23 23 23

1 No data available for shaded cells Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

Table 8. Available Assessment Scores for Spring 20031

FL 2003 Hope Fall 2003 MSCS Fall 2003

No Student

ID Gender SES Hope -P

Hope-A

Hope -Total Social Comp Affect Academic Family Physical Total

7 100 1 18 25 16 14 30 138 124 115 105 90 79 8 101 1 21 29 4 13 17 9 102 1 23 33 17 15 32

10 103 1 25 23 13 14 27 106 114 107 108 125 120 12 105 1 24 25 13 14 27 13 107 1 23 27 12 14 26 106 112 108 121 125 109 14 108 1 24 25 16 16 32 15 109 1 15 10 25 17 111 1 24 27 111 86 91 98 104 87 18 112 1 24 21 12 11 23 94 94 98 98 87 110 19 113 1 94 103 106 108 94 107 20 114 1 26 34 15 15 30 21 115 1 27 30 12 13 25 82 102 103 100 94 93 26 120 1 25 22 11 12 23 29 123 1 22 20 12 14 26 96 90 106 113 125 101 30 124 1 19 22 12 13 25 96 96 88 102 101 122 33 131 1 22 22 13 15 28 138 96 103 117 113 132 34 132 1 78 80 80 77 79 78 44 143 1 23 21 12 12 24 135 112 110 88 84 126 45 144 1 24 31 13 16 29 114 112 134 100 113 114 53 157 1 27 26 14 16 30 102 114 119 121 79 91 54 161 1 17 34 16 14 30 141 121 124 117 125 123 55 162 1 25 33 12 15 27 115 127 124 120 125 128 56 163 1 23 20 13 12 25 91 92 105 104 117 107 72 200 2 29 31 14 16 30 105 97 98 86 90 100 73 201 2 22 16 16 16 32 85 84 85 93 88 82 74 202 2 25 35 16 16 32 145 138 145 145 125 132

1 See note at the end of the Table.

81

Table 8. Available Assessment Scores for Spring 2003 (Cont.)1

FL 2003 Hope Fall 2003 MSCS Fall 2003

No Student

ID Gender SES Hope -P

Hope-A

Hope -Total Social Comp Affect Academic Family Physical Total

75 203 2 21 20 16 13 29 106 84 94 106 99 105 79 207 2 28 30 10 13 23 81 209 2 26 27 13 13 26 114 114 106 119 102 115 82 210 2 27 17 12 13 25 79 67 37 84 87 99 84 212 2 87 64 88 68 99 115 88 216 2 25 23 12 11 23 93 96 96 83 89 86 92 220 2 108 81 97 119 113 87 96 225 2 31 86 87 97 91 98 98 97 226 2 22 13 13 14 27 71 75 71 84 57 86

105 237 2 24 25 13 15 28 100 105 106 84 104 109 Total scores 31 32 31 31 31 29 29 29 29 29 29

1 No data available for shaded cells Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

82

Table 9. Available Assessment Scores for Fall 20031

FL 2003 Hope Fall 2003 MSCS Fall 2003

No Student

ID Gender SES Hope -P

Hope-A

Hope -Total Social Comp Affect Academic Family Physical Total

7 100 1 29 9 16 25 124 99 128 117 83 86 105 8 101 1 17 88 84 90 83 82 75 80 9 102 1 34 14 15 29 105 121 119 113 85 98 106

10 103 1 30 13 14 27 109 112 118 113 120 118 118 11 104 1 25 86 88 107 106 101 118 103 12 105 1 29 13 12 25 103 88 100 96 105 103 101 13 107 1 29 105 108 106 115 120 104 110 14 108 1 34 12 15 27 120 117 141 119 113 132 128 15 109 1 24 103 92 93 96 83 95 92 16 110 1 32 122 106 116 110 102 124 117 17 111 1 33 126 121 124 125 125 85 118 18 112 1 23 12 13 25 100 106 97 105 96 110 103 19 113 1 24 11 12 23 102 117 105 106 88 105 103 20 114 1 29 14 15 29 105 102 108 98 83 103 99 21 115 1 14 11 12 23 76 75 77 84 81 75 72 22 116 1 24 100 96 94 95 92 104 96 23 117 1 33 130 111 107 119 117 138 123 24 118 1 21 145 136 145 127 125 126 142 25 119 1 30 13 16 29 87 70 75 102 71 77 73 26 120 1 31 109 105 87 116 120 96 106 27 121 1 108 100 96 107 117 135 112 28 122 1 29 100 86 91 102 85 96 91 29 123 1 25 109 117 115 134 117 101 118 30 124 1 25 13 12 25 91 74 83 93 76 89 84 31 128 1 13 32 129 1 29 79 77 81 86 82 89 78 33 131 1 33 114 112 104 127 104 126 115

1 See note at the end of the Table 83

Table 9. Available Assessment Scores for Fall 200(Cont.)1

FL 2003 Hope Fall 2003 MSCS Fall 2003

No Student

ID Gender SES Hope -P

Hope-A

Hope -Total Social Comp Affect Academic Family Physical Total

34 132 1 83 100 90 86 77 95 85 35 133 1 29 126 129 116 131 125 113 130 36 134 1 28 100 111 118 109 120 114 115 37 135 1 34 103 115 91 84 87 87 93 38 136 1 84 97 107 105 110 100 102 40 138 1 16 16 32 41 139 1 14 16 30 42 140 1 13 13 26 43 141 1 29 44 143 1 11 11 22 45 144 1 14 14 28 72 200 2 11 12 11 23 97 88 98 83 87 96 91 73 201 2 24 12 16 28 88 81 85 95 90 82 85 74 202 2 33 16 15 31 145 136 137 125 125 132 142 75 203 2 24 10 11 21 94 92 80 88 90 95 94 76 204 2 29 14 15 29 128 126 118 108 105 132 123 77 205 2 18 11 9 19 78 206 2 14 12 12 24 91 96 86 82 82 81 83 79 207 2 29 13 15 28 108 117 137 113 100 115 117 80 208 2 16 14 30 115 118 106 116 110 89 111 81 209 2 34 11 14 25 135 123 122 120 102 128 127 82 210 2 21 10 10 20 100 100 94 102 89 103 97 83 211 2 28 13 13 26 120 112 124 116 120 117 121 84 212 2 75 77 79 73 82 87 73 85 213 2 103 109 122 121 110 115 118 86 214 2 33 11 11 22 87 81 113 88 78 86 94 87 215 2 85 87 77 76 85 91 80

1 See note at the end of the Table 84

Table 9. Available Assessment Scores for Fall 200(Cont.)1

FL 2003 Hope Fall 2003 MSCS Fall 2003

No Student

ID Gender SES Hope -P

Hope-A

Hope -Total Social Comp Affect Academic Family Physical Total

88 216 2 12 11 23 112 108 114 107 113 113 114 89 217 2 33 12 15 27 108 97 103 95 91 120 103 90 218 2 31 13 13 26 97 100 101 102 88 101 97 91 219 2 14 12 12 24 93 222 2 117 108 107 105 90 123 109 94 223 2 23 12 14 26 120 121 108 106 97 109 111 95 224 2 13 15 28 96 225 2 10 12 22 97 226 2 7 9 13 22 98 227 2 32 16 15 31

132 300 2 10 11 21 133 301 2 21 12 13 25 134 302 2 19 11 13 24

Total scores 50 41 41 41 51 51 51 51 51 51 51 1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

85

86

Table 10. Available Assessment Scores for Spring 20041

SP 2004 Hope Spring 2004 No Student ID Gender SES Hope-P Hope-A Hope-Total

7 100 1 25 13 13 26 8 101 1 19 9 102 1 26 15 14 29

10 103 1 14 14 28 12 105 1 12 10 22 14 108 1 28 15 15 30 17 111 1 24 18 112 1 26 12 13 25 19 113 1 27 11 12 23 20 114 1 24 14 13 27 21 115 1 23 11 12 23 23 117 1 29 25 119 1 10 13 23 26 120 1 23 30 124 1 25 12 12 24 33 131 1 29 39 137 1 26 40 138 1 15 13 28 41 139 1 12 15 27 42 140 1 13 12 25 43 141 1 27 44 143 1 12 12 24 45 144 1 13 15 28 46 147 1 12 13 25 47 150 1 24 9 13 22 48 151 1 12 12 24 49 152 1 12 14 26 50 153 1 11 11 22 51 154 1 10 13 23 52 156 1 11 12 23 57 167 1 14 13 27 58 169 1 24 15 14 29 59 170 1 13 13 26 60 171 1 23 12 9 21 62 174 1 27 63 176 1 25 65 178 1 22 66 179 1 25 68 186 1 21 69 187 1

1 No data available for shaded cells. Gender: 1=Male and 2=Female Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

87

Table 10. Available Assessment Scores for Spring 2004 (Cont.)1

SP 2004 Hope Spring 2004

No Student ID Gender SES Hope-P Hope-A Hope-Total

70 190 1 71 191 1 23 75 203 2 12 11 23 77 205 2 23 12 13 25 79 207 2 24 91 219 2 21 96 225 2 26 12 12 24 97 226 2 22 10 12 22 99 228 2 24

100 230 2 12 12 24 101 231 2 13 13 26 106 240 2 27 11 12 23 110 244 2 23 115 250 2 116 251 2 24 118 253 2 28 120 255 2 26 135 322 1 20

Total Scores 36 34 34 34

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

Table 11a. Available Assessment Scores for Fall 2004 for SES, SWLS, Hope and MSCS Scales1

Student Fall 2004 Hope Score Fall 2004 MSCS Score Fall 2004 ID Gender SES SWLS Hope -P Hope-A Hope -T Social Comp Affect Academic Family Physical Total

7 100 1 25 27 15 12 27 145 145 145 120 100 123 133 8 101 1 19 15 12 12 24 76 80 81 84 82 72 74 9 102 1 26 33 15 14 29 91 109 114 111 120 101 109

14 108 1 28 28 15 15 30 115 115 114 113 120 95 114 17 111 1 24 26 16 12 28 128 87 92 118 110 95 106 18 112 1 26 27 13 13 26 108 100 96 100 85 111 99 19 113 1 27 22 12 12 24 97 97 97 83 81 98 90 20 114 1 24 28 14 12 26 100 100 100 96 90 98 97 21 115 1 23 22 11 14 25 96 105 117 107 91 111 105 23 117 1 29 33 12 12 24 128 132 134 131 120 126 135 26 120 1 23 29 14 12 26 105 118 114 117 113 96 114 27 121 1 26 117 114 111 109 101 103 111 30 124 1 25 29 15 16 31 115 111 109 105 102 117 113 33 131 1 29 33 11 15 26 117 121 113 109 125 128 121 34 132 1 25 120 106 128 115 113 130 123 38 136 1 24 117 127 124 127 125 114 126 39 137 1 26 10 16 15 31 96 88 105 91 74 115 92 43 141 1 27 34 13 10 23 103 86 104 96 92 104 97 47 150 1 24 26 12 12 24 106 90 85 95 79 78 85 58 169 1 24 12 15 12 27 117 97 97 100 74 94 93 60 171 1 23 32 13 15 32 112 93 105 91 120 119 108 61 172 1 24 93 111 107 98 96 108 103 62 174 1 27 25 12 13 25 100 96 94 90 87 101 93 63 176 1 25 30 11 14 26 97 81 113 96 100 120 104 64 177 1 25 118 108 108 111 105 117 115 65 178 1 22 30 11 16 27 114 109 114 121 110 128 120 66 179 1 25 31 13 12 25 115 124 134 107 104 118 119

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. 88

Table 11a. Available Assessment Scores for Fall 2004 for SES, SWLS, Hope and MSCS Scales (Cont.)1

Student

Fall 2004 Hope Score Fall 2004 MSCS Score Fall 2004

No ID Gender SES SWLS Hope -P Hope-A Hope -Total Social Comp Affect Academic Family Physical Total67 184 1 26 118 96 100 95 103 111 104 68 186 1 21 33 15 16 31 126 123 116 117 102 128 119 69 187 1 23 27 111 112 105 93 101 114 106 70 190 1 21 11 99 86 100 83 81 107 93 71 191 1 23 29 13 13 26 100 103 101 102 91 103 99 73 201 2 124 94 97 93 92 104 101 77 205 2 23 19 11 13 24 79 207 2 24 23 11 12 23 117 109 103 118 106 123 113 84 212 2 99 114 122 117 120 112 114 85 213 2 85 80 87 80 77 76 81 87 215 2 24 96 100 98 100 89 98 97 91 219 2 21 24 12 13 25 106 114 94 127 113 79 106 92 220 2 21 103 90 91 109 74 111 96 93 222 2 25 82 87 92 88 105 112 94 96 225 2 26 33 12 12 24 83 68 56 65 52 69 66 97 226 2 22 25 12 14 26 102 109 108 105 97 111 106 99 228 2 24 24 12 13 25 62 99 108 108 104 89 95

106 240 2 27 12 8 10 18 110 244 2 23 30 11 13 24 112 247 2 102 100 101 104 94 100 100 116 251 2 24 31 11 12 23 117 252 2 102 109 108 106 106 114 108 118 253 2 28 29 8 12 20 111 97 104 102 95 103 102 119 254 2 75 105 103 90 104 90 94 120 255 2 26 29 14 15 29 126 275 2 17 135 322 1 20 12 15 27 109 88 81 96 82 96 92 138 325 1? 83 83 87 90 89 80 85

Total Scores 47 38 36 36 36 49 49 49 49 49 49 49 1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

89

Table 11b. Available Assessment Scores for Fall 2004 for FES Scales1

FES Score Fall 2004

No Student ID Gender COH EXP CONF IND AO ICO ARO MRE ORG CONT7 100 1 5.2 4.7 4.9 6.1 7.2 5.8 5.9 5.1 5.8 5.4 8 101 1 5.2 4.7 4.9 3.7 6.6 5.2 4.3 5.1 4.8 5.9 9 102 1 4.5 4.7 3.3 3.7 4.1 4.7 4.8 5.6 4.2 4.9

14 108 1 5.9 4.0 3.9 6.1 5.3 5.2 5.9 6.6 4.8 5.4 17 111 1 5.9 3.4 5.4 5.3 5.9 6.3 5.2 5.3 5.8 7.0 18 112 1 19 113 1 3.1 2.8 7.0 2.9 4.1 5.2 4.8 4.6 3.7 5.9 20 114 1 5.2 5.3 4.9 6.9 5.9 4.7 4.3 4.6 3.7 7.0 21 115 1 1.8 2.8 7.5 2.9 4.7 2.5 4.3 3.2 3.7 5.4 23 117 1 6.5 4.0 5.4 4.5 5.9 6.3 5.9 5.6 6.3 6.5 26 120 1 5.9 4.7 4.9 3.7 5.9 6.3 6.4 6.6 6.3 7.0 27 121 1 30 124 1 4.5 4.7 6.0 4.5 6.6 5.8 6.4 6.6 3.7 6.5 33 131 1 5.2 2.2 4.9 5.3 5.9 5.8 5.9 6.1 6.3 6.5 34 132 1 38 136 1 39 137 1 2.5 4.0 7.5 5.3 5.3 4.1 4.3 5.1 3.2 7.6 43 141 1 4.5 4.7 5.4 5.3 5.3 4.7 5.9 6.6 4.8 6.5 47 150 1 3.8 5.3 4.9 3.7 5.3 4.7 3.3 5.6 5.3 4.9 58 169 1 60 171 1 4.5 2.2 4.9 6.1 7.2 5.8 5.3 6.1 4.8 7.0

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control.

First 3 columns are identical to those in Table 6a 90

Table 11b. Available Assessment Scores for Fall 2004 for FES Scales (Cont.)1

FES Score Fall 2004No

Student ID Gender COH EXP CONF IND AO ICO ARO MRE ORG CONT61 172 1 62 174 1 63 176 1 5.2 3.4 4.9 5.3 5.9 4.1 5.9 5.1 4.2 3.2 64 177 1 65 178 1 5.2 2.8 6.5 3.7 6.6 5.2 5.9 6.6 5.3 6.5 66 179 1 4.5 2.2 4.9 5.3 6.6 3.6 5.9 6.1 4.8 5.4 67 184 1 68 186 1 4.5 4.0 4.4 2.9 5.3 4.7 4.8 6.6 4.2 7.0 69 187 1 70 190 1 71 191 1 5.2 4.0 5.4 7.0 6.6 5.2 5.3 6.6 5.3 7.0 73 201 2 77 205 2 4.0 3.3 4.5 4.7 3.6 4.8 6.6 3.2 5.9 79 207 2 5.3 4.9 1.3 7.2 4.1 5.3 5.6 2.6 6.5 84 212 2 2.8 7.0 5.3 4.1 3.0 3.8 2.7 3.7 3.8 85 213 2 87 215 2 91 219 2 2.8 6.5 5.3 5.3 5.2 5.9 5.6 2.6 4.3 92 220 2 93 222 2 96 225 2 3.4 3.3 2.9 4.7 4.7 3.8 6.1 6.3 5.9 97 226 2 2.8 7.0 2.9 5.9 4.1 3.3 4.6 3.7 7.6 99 228 2 2.0 7.0 4.5 6.6 5.8 5.9 6.6 4.8 7.0

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control.

First 3 columns are identical to those in Table 6a 91

Table 11b. Available Assessment Scores for Fall 2004 for FES Scales1 (Cont.)

FES Score Fall 2004No

Student ID Gender COH CONF IND AO ICO ARO MRE ORG CONTEXP106 240 2 2.8 8.0 2.1 5.3 3.0 3.3 5.6 3.2 7.6 110 244 2 4.0 4.9 6.5 4.5 4.1 5.9 5.1 4.8 4.3 112 247 2 116 251 2 4.0 5.4 6.1 5.3 6.3 5.9 4.6 5.8 4.3 117 252 2 118 253 2 3.4 5.4 4.5 4.7 4.1 4.8 6.1 4.2 4.3 119 254 2 120 255 2 4.7 4.4 5.3 5.9 5.8 6.4 6.1 5.8 4.3 126 275 2 135 322 1 3.1 3.4 6.5 4.5 4.7 5.2 5.3 6.1 5.3 7.0 138 325 1?

Total Scores 34 34 34 34 34 34 34 34 34 34

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control. First 3 columns are identical to those in Table 6a 92

Table 12. Available Assessment Scores for Spring 20051

SP 2005 MSCS Spring 2005

No Student

ID Gender SES Social Comp Affect Academic Family Physical Total7 100 1 25 93 85 77 8 101 1 36 91 93 95 93 86 9 102 1 22 110 111 112 90 102

12 105 1 25 14 108 1 28 104 102 100 94 119 17 111 1 24 103 103 102 18 112 1 25 105 106 107 98 100 19 113 1 25 100 101 102 98 98 20 114 1 28 104 105 106 95 102 21 115 1 23 23 117 1 26 118 116 114 26 120 1 23 112 112 112 27 121 1 24 108 107 107 30 124 1 25 116 118 119 33 131 1 29 130 133 135 113 107 34 132 1 27 130 133 135 38 136 1 24 129 130 131 39 137 1 26 122 127 132 141 133 43 141 1 26 123 128 133 47 150 1 24 112 116 119 49 152 1 20 58 169 1 26 125 130 134 60 171 1 23 151 159 168 61 172 1 24 144 151 159 62 174 1 25 138 146 154 95 95 63 176 1 25 155 166 177

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

1 No data available for shaded cells. Gender: 1=Male and 2=Female.

93

Table 12. Available Assessment Scores for Spring 2005 (Cont.)1

SP 2005 MSCS Spring 2005

NoStudent

ID Gender SES Social Comp Affect Academic Family Physical Total64 177 1 27 148 155 162 65 178 1 19 157 165 173 128 132 66 179 1 25 115 115 119 110 101 115 102 67 184 1 26 68 186 1 26 128 134 125 123 110 126 131 69 187 1 23 70 190 1 29 71 191 1 23 75 203 2 25 79 207 2 23 84 212 2 28 85 213 2 24 87 215 2 23 91 219 2 24 92 220 2 28 93 222 2 31 96 225 2 24 84 83 78 90 97 103 86 97 226 2 23 112 97 75 85 69 85 79 99 228 2 27 112 99 113 107 99 114 109

106 240 2 24 106 96 106 104 97 87 99 110 244 2 27 111 246 2 24 114 249 2 22 116 251 2 23 117 252 2 24 118 253 2 90 100 94 93 94 81 91 119 254 2 28

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

1 No data available for shaded cells. Gender: 1=Male and 2=Female. 94

Table 12. Available Assessment Scores for Spring 2005 (Cont.)1

SP 2005 MSCS Spring 2005

NoStudent

ID Gender

SES Social Comp Affect Academic Family Physical Total121 257 2 20 122 264 2 20 99 86 107 83 86 111 95 123 270 2 24 124 271 2 24 125 272 2 22 127 276 2 25 128 277 2 27 129 278 2 23 130 281 2 23 135 322 1 20 136 323 1 23 137 324 1 22 138 325 1? 26 139 326 2 23 142 403 1 22 90 87 83 86 94 81 85 143 408 1 24 144 409 1 26 145 410 1 24 146 411 1 25 147 412 1 25 148 413 1 23 149 414 1 21 150 415 1 29 151 416 1 109 108 113 109 145 124 117 152 418 1 80 87 74 82 74 53 67

Total Scores 76 11 11 36 36 36 21 21 1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

95

Table 13a. Available Assessment Scores for Fall 2005 for SWLS and MSCS Scales1

FL 2005 MSCS Fall 2005

No Student

ID Gender SWLS Social Comp Affect Academic Family Physical Total7 100 1 7 8 101 1 34 88 87 88 85 45 90 74 9 102 1 96 99 110 109 106 104 106

12 105 1 15 14 108 1 17 111 1 18 112 1 33 96 112 106 111 89 100 90 19 113 1 22 20 114 1 33 97 102 110 98 99 91 101 21 115 1 22 96 105 117 107 91 111 104.5 23 117 1 30 106 117 119 107 125 128 118 27 121 1 33 122 115 110 111 113 28 122 1 34 132 1 30 106 118 114 104 101 138 115 38 136 1 30 87 121 112 106 117 94 107 43 141 1 23 99 92 97 96 96 120 101 44 143 1 26 117 108 103 96 93 119 106 49 152 1 35 122 75 119 82 106 118 106 58 169 1 23 117 115 104 82 78 109 98 62 174 1 31 114 109 100 98 110 118 111 64 177 1 27 114 102 106 98 108 107 107 65 178 1 32 105 114 116 120 130 121 119 66 179 1 29 130 126 141 125 125 122 133 67 184 1 18 118 96 100 95 103 111 104 68 186 1 28 124 117 118 110 101 126 98 69 187 1 27 111 112 105 93 101 114 106

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

1 No data available for shaded cells. Gender: 1=Male and 2=Female.

96

Table 13a. Available Assessment Scores for Fall 2005 for SWLS and MSCS Scales (Cont.)1

FL 2005 MSCS Fall 2005

NoStudent

ID Gender SWLS Social Comp Affect Academic Family Physical Total70 190 1 31 115 106 131 119 113 114 119 71 191 1 29 100 103 101 102 91 103 99 73 201 2 83 83 87 90 89 80 85 75 203 2 28 141 108 109 109 92 111 111 79 207 2 19 84 212 2 27 85 213 2 19 118 118 116 145 103 120 125 86 214 2 29 87 215 2 100 96 104 107 99 101 102 91 219 2 26 96 100 98 100 89 98 97 92 220 2 28 120 141 101 117 110 93 222 2 24 124 103 112 111 100 117 114 96 225 2 26 115 111 109 105 102 117 119 97 226 2 15 115 111 109 105 102 117 119 99 228 2 24 132 109 100 138 122

106 240 2 17 97 102 97 117 98 81 99 110 244 2 12 111 246 2 27 118 115 113 118 101 113 119 113 248 2 102 100 101 104 94 100 100 114 249 2 14 93 94 97 98 90 96 95 115 250 2 17 116 251 2 111 101 102 99 95 117 252 2 31 118 253 2 102 109 108 106 106 114 107.5 119 254 2 29 111 97 104 102 95 103 102 120 255 2 75 105 103 90 104 90 94 121 257 2 32 120 123 110 85 85 108 105

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

1 No data available for shaded cells. Gender: 1=Male and 2=Female. 97

Table 13a. Available Assessment Scores for Fall 2005 for SWLS and MSCS Scales (Cont.)1

FL 2005 MSCS Fall 2005

NoStudent

ID Gender

SWLS Social Comp Affect Academic Family Physical Total122 264 2 21 112 92 107 98 97 108 103 123 270 2 22 112 108 94 105 100 105 105 124 271 2 26 103 121 110 95 109 107 109 125 272 2 29 87 109 96 120 113 135 113 126 275 2 122 106 101 115 113 107 113 127 276 2 28 120 118 141 110 113 130 126 128 277 2 28 112 94 114 95 103 103 105 129 278 2 25 103 100 108 90 117 103 105 130 281 2 25 105 94 118 96 90 109 103 131 282 2 109 80 109 81 80 107 92 135 322 1 17 109 88 81 96 82 96 92 136 323 1 14 96 77 84 80 81 79 78 137 324 1 22 74 87 91 84 97 107 89 138 325 1? 77 105 118 134 97 114 108 139 326 2 29 140 327 2 32 141 328 2 18 45 67 70 83 69 66 60 142 403 1 13 143 408 1 27 111 100 108 111 96 105 106 153 420 1 20 102 81 75 120 83 114 94 154 422 1 14 99 97 94 86 82 84 90 155 425 1 10 88 92 88 77 86 96 86 156 435 1 14 93 103 88 83 102 96 94 157 436 1 31 117 123 125 145 125 122 130 158 437 1 19 70 83 88 84 71 65 69 159 438 1 20 45 55 65 70 70 53 54

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

1 No data available for shaded cells. Gender: 1=Male and 2=Female. 98

Table 13a. Available scale scores for Fall 2005 (Cont.)1

FL 2005 MSCS Fall 2005Student

ID Gender

No SWLS Social Comp Affect Academic Family Physical Total160 439 1 17 57 66 75 72 62 64 68 161 440 1 30 59 61 77 70 65 75 60 162 441 1 32 112 112 105 113 117 132 117 163 442 1 15 42 48 33 45 52 49 83 164 443 1 19 75 62 70 80 81 66 66 165 444 1 25 132 108 109 106 106 118 116 166 447 1 31 115 106 131 119 113 114 119

Total Scores 73 70 70 70 71 71 70 67 1 No data available for shaded cells. Gender: 1=Male and 2=Female.

Col.1 (No) - break in sequence from 1 to 166 indicates missing score.

99

Table 13b. Available Assessment Scores for Fall 2005 for FES Scales1

FES Score Fall 2005

No Student ID Gender COH EXP CONF IND AO ICO ARO MRE ORG CONT7 100 1 5.2 4.7 4.9 6.1 7.2 5.8 5.9 5.1 5.8 5.48 101 1 3.1 1.6 4.9 4.5 4.1 3.6 5.3 5.1 3.2 4.99 102 1 5.9 4.7 3.3 5.3 5.6 5.2 4.8 6.1 4.2 4.9

12 105 1 3.8 2.8 7.0 9.5 5.3 3.0 2.3 5.1 5.8 7.614 108 1 5.9 4.0 3.9 6.1 5.3 5.2 5.9 6.6 4.8 5.417 111 1 5.9 3.4 5.4 5.3 5.9 6.3 5.2 5.3 5.8 7.018 112 1 5.9 3.4 7.3 4.5 7.2 5.8 4.8 6.1 4.8 7.019 113 1 3.8 4.7 6.0 3.7 4.7 4.7 4.8 5.1 4.8 5.420 114 1 6.5 6.5 4.4 6.1 7.2 5.2 5.3 5.6 3.2 6.521 115 1 4.3 3.2 3.7 5.423 117 1 5.9 4.0 4.4 5.3 5.3 6.9 5.3 5.1 5.8 5.427 121 1 28 122 1 5.9 4.0 3.3 3.7 4.1 4.1 3.3 6.6 5.8 4.334 132 1 4.5 4.0 4.4 6.0 5.3 4.7 5.3 6.6 6.9 4.938 136 1 5.9 4.7 4.9 2.9 5.3 5.8 6.4 5.9 5.3 5.943 141 1 5.2 3.4 7.5 3.7 6.6 5.2 5.9 6.1 4.2 7.044 143 1 3.1 4.0 6.5 4.5 5.9 4.7 3.8 5.6 4.2 5.949 152 1 5.9 4.7 6.0 3.7 5.9 5.2 5.3 4.6 6.3 5.458 169 1 1.1 2.8 7.0 2.9 3.5 1.9 2.8 6.1 3.2 5.962 174 1 5.2 5.3 6.5 4.5 5.9 5.2 5.3 5.6 5.3 5.464 177 1 6.5 5.3 4.4 3.7 6.6 5.8 6.4 6.1 6.9 5.965 178 1 5.2 4.0 5.4 4.5 6.6 6.1 6.4 6.6 5.3 5.466 179 1 5.2 4.7 3.9 5.3 5.9 5.2 4.8 6.6 5.8 4.9

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control.

First 3 columns are identical to those in Table 8a 100

Table 13b. Available scale scores for Fall 2005 (Cont.) 1

FES Score Fall 2005No

Student ID Gender COH EXP CONF IND AO ICO ARO MRE ORG CONT67 184 1 3.8 2.8 7.5 3.7 5.9 3.6 5.3 6.1 3.2 5.4 68 186 1 5.9 6.5 4.4 5.3 5.3 6.3 6.4 7.1 6.9 7.0 69 187 1 4.8 6.1 5.3 6.5 70 190 1 5.2 4.7 4.9 4.5 6.6 5.8 5.3 5.6 5.3 4.9 71 191 1 5.3 6.6 5.3 7.0 73 201 2 75 203 2 3.1 2.8 6.0 4.5 5.9 4.1 4.8 6.6 5.8 7.0 79 207 2 4.8 6.6 3.2 5.9 84 212 2 3.8 5.9 6.5 5.4 5.3 4.1 5.9 5.1 4.0 5.1 85 213 2 3.1 5.3 6.0 4.5 5.3 5.8 5.9 6.1 4.2 5.9 86 214 2 87 215 2 4.5 5.9 6.0 5.3 5.3 5.8 5.3 5.1 4.8 5.9 91 219 2 6.4 6.6 5.3 5.4 92 220 2 3.1 3.4 4.4 4.5 7.2 6.3 4.3 6.1 5.8 6.5 93 222 2 3.8 4.0 6.5 4.5 6.6 5.8 5.3 5.6 3.7 6.5 96 225 2 2.5 2.2 7.0 4.5 5.9 5.8 5.9 6.1 6.9 6.8 97 226 2 1.1 3.4 7.1 2.9 5.9 4.1 4.3 6.1 3.2 7.6 99 228 2 3.8 4.7 4.9 4.5 5.3 4.1 5.3 6.1 5.3 6.5

106 240 2 6.5 5.3 4.4 6.1 5.9 6.9 6.9 6.6 6.9 5.9 110 244 2 3.3 5.6 3.2 7.6 111 246 2 5.2 4.7 5.4 5.3 5.0 5.2 4.3 4.1 2.1 3.8 113 248 2 114 249 2 3.3 3.6 2.1 4.9 115 250 2

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control. First 3 columns are identical to those in Table 8a 101

Table 13b. Available scale scores for Fall 2005 (Cont.) 1

FES Score Fall 2005No

Student ID Gender COH EXP CONF IND AO ICO ARO MRE ORG CONT116 251 2 5.2 3.4 3.9 6.1 6.3 4.1 4.8 5.1 5.8 5.1 117 252 2 5.9 4.6 5.8 4.3 118 253 2 119 254 2 4.8 6.1 4.2 4.3 120 255 2 121 257 2 6.5 5.9 4.9 3.7 5.3 6.3 6.4 5.6 4.8 5.4 122 264 2 3.3 6.1 6.5 123 270 2 6.5 4.0 4.4 5.3 6.6 6.3 6.9 5.6 5.8 5.4 124 271 2 6.5 4.0 4.4 6.9 4.7 6.3 5.9 6.6 6.3 6.5 125 272 2 6.5 5.3 3.9 4.5 6.6 6.9 6.9 6.1 5.8 6.5 126 275 2 127 276 2 5.2 2.8 5.4 5.3 5.3 3.6 5.3 6.1 4.8 5.9 128 277 2 4.5 4.7 6.0 4.5 4.1 5.2 5.9 4.6 4.2 3.8 129 278 2 5.9 5.3 4.4 4.5 5.9 4.7 5.3 7.1 5.8 4.9 130 281 2 5.9 5.9 8.0 3.7 5.3 5.8 6.4 6.1 6.3 3.8 131 282 2 135 322 1 5.3 6.1 5.3 7.0 136 323 1 3.1 3.4 6.0 2.1 6.6 4.7 4.3 6.6 3.2 7.0 137 324 1 4.5 4.7 4.9 2.9 5.3 5.8 6.9 6.6 5.3 5.4 138 325 1? 139 326 2 5.2 5.3 6.0 2.9 3.5 4.7 5.3 4.2 5.9 140 327 2 3.8 4.7 6.0 3.7 5.3 4.7 4.8 5.6 5.3 5.1

1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control. First 3 columns are identical to those in Table 8a 102

Table 13b. Available scale scores for Fall 2005 (Cont.) 1

FES Score Fall 2005 No EXPStudent ID Gender COH CONF IND AO ICO ARO MRE ORG CONT

141 328 2 4.5 5.9 6.0 4.5 4.7 3.6 5.9 6.1 5.3 4.9 142 403 1 3.8 3.4 6.0 2.9 3.5 4.1 4.8 6.1 3.2 5.4 143 408 1 5.2 4.0 4.4 5.3 6.6 5.2 2.8 5.6 5.8 6.5 153 420 1 3.8 3.4 6.5 2.9 5.3 6.3 4.3 4.6 4.8 3.8 154 422 1 3.8 3.4 5.4 4.5 6.6 4.7 4.8 5.6 6.3 5.7 155 425 1 3.1 2.8 7.0 4.5 5.9 4.7 3.8 6.1 5.3 7.0 156 435 1 5.9 3.4 4.4 7.0 6.6 6.3 5.9 5.1 5.3 5.4 157 436 1 5.9 4.7 4.4 6.1 6.6 5.8 5.9 6.6 5.8 6.5 158 437 1 5.9 4.0 5.4 3.7 4.7 5.2 6.9 5.9 5.8 7.0 159 438 1 6.5 4.7 3.9 4.5 7.2 5.2 5.3 6.1 6.3 5.9 160 439 1 5.2 5.3 6.0 5.3 6.6 5.2 4.8 5.6 5.3 7.0 161 440 1 5.2 4.0 3.9 6.1 6.6 6.3 5.3 3.2 5.3 5.4 162 441 1 5.9 4.7 5.4 5.3 5.9 5.8 3.8 6.1 4.8 6.5 163 442 1 3.8 2.8 7.0 9.5 5.3 3.0 2.3 5.1 5.8 7.6 164 443 1 1.1 1.8 7.0 2.9 5.3 2.5 3.8 4.1 5.3 7.6 165 444 1 3.8 5.3 6.0 6.1 7.2 4.1 5.3 4.6 4.8 6.2 166 447 1 5.2 4.7 4.9 4.5 6.6 5.8 5.3 5.6 5.3 4.9

Total Scores 65 65 65 65 65 65 76 75 75 76 1 No data available for shaded cells. Gender: 1=Male and 2=Female. Col.1 (No) - break in sequence from 1 to 166 indicates missing score. COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; ORG-Organization; CONT-Control. First 3 columns are identical to those in Table 8a

103

Table 14. Available Data for Grades1

Std Fall 2002 Spring 2003 Fall 2003 Spring 2004 Fall 2004 Spring 2005 Fall 2005 ID Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci

81 93 90 84 68 28 47

103 73 75 85 104 84 85 107 96 91 85 108 81 86 70 76 73 110 80 65 111 85 77 79 67 66 69 87 84 88 87 70 112 72 81 76 76 73 72 83 113 67 79 72 85 92 114 67 72 83 72 76 115 82 88 87 82 56 90 70 87 128 77 69 129 63 76 73 79 42 132 70 75 135 60 54 137 85 52 65 73 141 76 143 82 82 85 88 88 88 88 88 88 144 72 92 80 161 44 74 71 162 163 38 169 23 56 186 71 33 190 62 201 79 73 74 80 84 72 202 78 68 203 77 92 77 93 95 94 205 90 71 70

1 Std ID – Student Identification Number. Eng –English Literature. Maths – Mathematics. Sci. - Science.

104

Table 14. Available Data for Grades (Cont.) 1

Std Fall 2002 Spring 2003 Fall 2003 Spring 2004 Fall 2004 Spring 2005 Fall 2005 ID Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci Eng Math Sci207 86 75 73 92 79 212 82 70 220 70 223 85 82 78 225 89 88 72 70 92 72 226 63 230 73 90 232 84 69 82 235 81 80 236 93 57 240 41 246 94 97 61 251 60 71 264 65 83 65 83 270 79 68 271 272 98 65 82 n 8 11 11 14 17 5 2 9 6 9 10 6 4 10 4 2 1 2 7 8 6

1 Std ID – Student Identification Number. Eng –English Literature. Maths – Mathematics. Sci. - Science. The letter n at the bottom of table column 1 is the total number of available scores per semester and subject.

105

Table 15. Descriptive Statistics and Shapiro-Wilk Test Results for Semester Assessment Data1

Descriptive Statistics Shapiro-Wilk Test

# SCAL

E Sub-Scale Semester Mean Median Mean-Median StdErr StdDev Kurtosis Skewness n W Pr<W

1 FES ARO Fall 2004 5.3 5.6 -0.3 0.2 0.9 0.938 -0.976 34 0.9203 0.0165 2 FES CONT Fall 2004 6.2 6.5 -0.3 0.2 1.0 1.784 -1.191 22 0.8783 0.0112 3 FES MRE Fall 2004 5.2 5.5 -0.3 0.2 1.2 -0.616 -0.672 34 0.9028 0.0054 4 FES AO Fall 2005 5.7 5.9 -0.2 0.1 0.9 -0.056 -0.476 65 0.9349 0.0018 5 FES ARO Fall 2005 5.1 5.3 -0.2 0.1 1.1 0.205 -0.594 76 0.9444 0.0021 6 FES COH Fall 2005 4.8 5.2 -0.4 0.2 1.4 0.245 -0.810 65 0.906 0.0001 7 FES CONF Fall 2005 5.4 5.4 0.0 0.1 1.2 -0.900 0.177 65 0.9568 0.0218 8 FES CONT Fall 2005 5.8 5.9 -0.1 0.1 1.0 -0.575 -0.099 76 0.9575 0.0115 9 FES ICO Fall 2005 5.1 5.2 -0.1 0.1 1.1 0.294 -0.694 65 0.9479 0.0077 10 FES IND Fall 2005 4.8 4.5 0.3 0.2 1.4 2.779 1.089 65 0.9000 0.0001 11 FES MRE Fall 2005 5.7 6.1 -0.4 0.1 0.8 1.205 -1.056 75 0.8989 0.0000 12 FES ORG Fall 2005 5.0 5.3 -0.3 0.1 1.1 -0.061 -0.613 75 0.9308 0.0005 13 HOPE Hope-P Spring 2003 13.2 13.0 0.2 0.4 2.5 5.301 -1.470 31 0.8374 0.0003 14 HOPE Hope-A Fall 2003 13.3 13.0 0.3 0.3 1.9 -0.843 -0.231 41 0.9390 0.0290 15 HOPE Hope-A Spring 2004 12.6 13.0 -0.4 0.2 1.3 1.139 -0.432 34 0.9174 0.0137 16 HOPE Hope-A Fall 2004 13.1 13.0 0.1 0.3 1.6 -0.526 0.226 36 0.9008 0.0036 17 HOPE Hope-P Fall 2004 12.6 12.0 0.6 0.3 1.9 0.184 -0.222 36 0.9262 0.0192 18 MSCS Family Fall 2003 98.5 96.0 2.5 2.2 16.0 -1.303 0.260 51 0.9231 0.0027 19 MSCS Affect Fall 2005 102.7 105.5 -2.8 2.1 17.5 3.068 -0.986 70 0.9355 0.0012 20 MSCS Comp Fall 2005 100.3 103.0 -2.7 2.1 17.6 0.935 -0.861 70 0.9457 0.0040 21 MSCS Physical Fall 2005 104.0 107.0 -3.0 2.2 18.8 0.873 -0.959 70 0.9338 0.0010 22 MSCS Social Fall 2005 102.1 105.5 -3.4 2.5 20.8 1.202 -1.082 70 0.9107 0.0001 23 MSCS Total Fall 2005 101.0 104.5 -3.5 2.1 16.9 0.704 -0.844 67 0.9364 0.0018

1 Hypothesis that assessment scores are normally distributed is rejected for those Scales with highlighted cells since Pr<W <<0.05. StdErr – standard error; StdDev – standard deviation; n – sample numbers. AO-Achievement Orientation; ARO-Active-Religious Emphasis; COH – Cohesion; CONF-Conflict; CONT-Control; EXP-Expressiveness; ICO-Intellectual-Recreational Orientation; IND-Independence; MRE – Moral Religious Emphasis; ORG-Organization. 106

Table 15. Descriptive Statistics and Shapiro-Wilk Test Results for Semester Assessment Data (Cont.)1

Descriptive Statistics Shapiro-Wilk Test

# SCALE Sub-Scale Semester Mean Median Mean-Median StdErr StdDev Kurtosis Skewness n W Pr<W

24 SES SES Fall 2003 25.9 29.0 -3.1 1.0 6.8 0.155 -0.930 50 0.9025 0.0006 25 SES SES Spring 2005 24.6 24.0 0.6 0.3 2.7 3.503 1.039 76 0.9349 0.0004 26 SWLS SWLS Fall 2004 25.7 27.5 -1.8 1.1 6.7 0.240 -1.050 38 0.88 0.0007 27 SWLS SWLS Fall 2005 24.2 26.0 -1.8 0.8 6.7 -0.694 -0.512 73 0.951 0.0061 28 HOPE Hope-P Spring 2004 12.3 12.0 0.3 0.3 1.5 -0.205 0.178 34 0.9325 0.0371 29 HOPE Hope-P Fall 2003 12.3 12.0 0.3 0.3 1.8 -0.042 0.353 41 0.9441 0.0436 30 HOPE Hope-A Fall 2002 12.6 13.0 -0.4 0.4 1.9 0.261 -0.869 23 0.9121 0.0452 31 HOPE Hope-Total Fall 2002 25.4 26.0 -0.6 0.5 2.6 0.363 -0.929 23 0.9133 0.0478 32 FES AO Fall 2004 5.3 5.3 0.0 0.2 1.1 -0.661 -0.328 34 0.9510 0.1315 33 FES COH Fall 2004 4.2 4.5 -0.3 0.2 1.2 -0.790 -0.242 34 0.9589 0.2261 34 FES CONF Fall 2004 5.0 4.9 0.1 0.2 1.4 0.915 -0.562 34 0.9410 0.0659 35 FES EXP Fall 2004 4.4 4.6 -0.1 0.3 1.5 -0.013 0.544 34 0.9499 0.1216 36 FES ICO Fall 2004 5.0 5.2 -0.2 0.2 1.0 -0.149 -0.650 34 0.9388 0.0567 37 FES IND Fall 2004 5.0 5.3 -0.3 0.2 1.2 -0.571 -0.009 34 0.9607 0.2547 38 FES ORG Fall 2004 5.1 4.8 0.3 0.2 1.2 -0.393 0.584 34 0.9427 0.0744 39 FES EXP Fall 2005 4.2 4.0 0.2 0.1 1.1 -0.347 -0.147 65 0.9662 0.0688 40 HOPE Hope-P Fall 2002 12.9 13.0 -0.1 0.3 1.5 -0.115 0.150 23 0.9627 0.5202 41 HOPE Hope-A Spring 2003 13.8 14.0 -0.2 0.3 1.6 -0.420 -0.393 31 0.9339 0.0561 42 HOPE Hope-Total Spring 2003 27.0 27.0 0.0 0.6 3.4 0.853 -0.569 31 0.9406 0.0859 43 HOPE Hope-Total Fall 2003 25.6 25.0 0.6 0.5 3.2 -0.744 0.022 41 0.9796 0.6622 44 HOPE Hope-Total Spring 2004 24.9 24.5 0.4 0.4 2.4 -0.761 0.433 34 0.9478 0.1057 45 HOPE Hope-Total Fall 2004 25.9 26.0 -0.1 0.5 3.0 0.693 -0.021 36 0.9499 0.1038

1Hypothesis that assessment scores are normally distributed is rejected for those Scales with slightly highlighted cells since Pr<W <<0.05. Assessment scores with heavier shade for cells in Pr<W column can be considered as approximately normally distributed since Pr<W ~0.05 Assessment scores with no shading for cells in Pr<W column are considered as normally distributed since Pr<W >0.05 StdErr – standard error; StdDev – standard deviation; n – sample numbers. AO-Achievement Orientation; COH – Cohesion; CONF-Conflict; EXP – Expressiveness; ICO-Intellectual-Recreational Orientation; IND-Independence; ORG-Organization; CONT-Control. 107

Table 15. Descriptive Statistics and Shapiro-Wilk Test Results for Semester Assessment Data (Cont.)1

Descriptive Statistics

Shapiro-Wilk Test

# SCALE Sub-Scale Semester Mean Median Mean-Median StdErr StdDev Kurtosis Skewness n W Pr<W

46 MSCS Academic Spring 2003 102.0 102.0 0.0 3.1 16.9 0.141 0.227 29 0.9724 0.6267 47 MSCS Affect Spring 2003 101.4 103.0 -1.6 3.7 19.9 3.310 -0.804 29 0.9291 0.0522 48 MSCS Comp Spring 2003 98.9 96.0 2.9 3.4 18.0 -0.395 0.093 29 0.9856 0.9518 49 MSCS Family Spring 2003 101.1 99.0 2.1 3.2 17.3 -0.124 -0.248 29 0.9371 0.0840 50 MSCS Physical Spring 2003 104.9 107.0 -2.1 3.0 16.2 -1.054 0.008 29 0.9597 0.3235 51 MSCS Social Spring 2003 104.0 102.0 2.0 3.7 20.0 -0.314 0.612 29 0.9375 0.0862 52 MSCS Total Spring 2003 102.3 103.0 -0.7 3.1 16.9 0.760 0.114 29 0.9811 0.8652 53 MSCS Academic Fall 2003 104.5 106.0 -1.5 2.1 15.0 -0.707 -0.136 51 0.9795 0.5161 54 MSCS Affect Fall 2003 104.7 106.0 -1.3 2.5 17.5 -0.474 0.278 51 0.9752 0.3573 55 MSCS Comp Fall 2003 102.9 105.0 -2.1 2.3 16.4 -0.651 -0.087 51 0.9809 0.5778 56 MSCS Physical Fall 2003 104.5 103.0 1.5 2.4 17.0 -0.916 0.178 51 0.969 0.2004 57 MSCS Social Fall 2003 105.2 103.0 2.2 2.3 16.7 -0.184 0.370 51 0.9766 0.4049 58 MSCS Total Fall 2003 104.1 103.0 1.1 2.4 17.2 -0.432 0.040 51 0.9818 0.6175 59 MSCS Academic Fall 2004 102.6 102.0 0.6 1.9 13.6 0.072 -0.157 49 0.9879 0.8877 60 MSCS Affect Fall 2004 104.4 104.0 0.4 2.2 15.2 1.877 -0.092 49 0.9679 0.2002 61 MSCS Comp Fall 2004 102.4 100.0 2.4 2.2 15.1 0.319 0.315 49 0.9869 0.8572 62 MSCS Family Fall 2004 97.9 100.0 -2.1 2.2 15.6 0.205 -0.379 49 0.9732 0.3221 63 MSCS Physical Fall 2004 105.1 107.0 -1.9 2.2 15.3 -0.192 -0.527 49 0.9608 0.1024 64 MSCS Social Fall 2004 104.9 105.0 -0.1 2.2 15.6 0.656 -0.302 49 0.9790 0.5231 65 MSCS Total Fall 2004 103.4 104.0 -0.6 2.0 14.0 0.490 -0.122 49 0.9883 0.9046

1 Assessment scores with no shading for cells in Pr<W column are considered as normally distributed since Pr<W >0.05. StdErr – standard error; StdDev – standard deviation; n – sample numbers.

108

1Table 15. Descriptive Statistics and Shapiro-Wilk Test Results for Semester Assessment Data (Cont.)

Shapiro-Wilk Test Descriptive Statistics

Mean-Median

StdErr Sub-Scale Semester Mean Median StdDev Kurtosis Skewness n W Pr<W# SCALE

MSCS Academic Spring 2005 66 115.8 110.5 5.3 4.0 23.8 -0.399 0.601 36 0.9407 0.0533 MSCS Affect Spring 2005 67 114.3 112.6 1.8 3.6 21.5 -0.286 0.123 36 0.9750 0.5761 MSCS Comp Spring 2005 68 99.3 97.0 2.3 4.6 15.1 1.674 1.273 11 0.8847 0.1193 MSCS Family Spring 2005 69 117.6 110.9 6.7 4.7 28.1 -0.444 0.522 36 0.9503 0.1006 MSCS Physical Spring 2005 70 101.2 98.0 3.2 4.4 20.0 0.582 -0.149 21 0.9703 0.7404 MSCS Social Spring 2005 71 102.3 106.0 -3.7 4.5 14.9 -0.848 0.015 11 0.9548 0.7059 MSCS Total Spring 2005 72 101.7 100.0 1.7 3.8 17.5 -0.142 0.268 21 0.9590 0.4961 MSCS Academic Fall 2005 73 100.2 101.0 -0.8 2.0 17.0 1.339 -0.086 71 0.9757 0.1770 MSCS Family Fall 2005 74 97.4 99.0 -1.6 2.1 17.3 0.915 -0.539 71 0.9767 0.2018 SES SES Fall 2002 75 25.0 25.0 0.0 0.5 2.2 -0.652 -0.240 24 0.9602 0.4417 SES SES Fall 2004 76 24.4 24.0 0.40 0.33 2.27 -0.121 -0.120 47 0.7966 0.4602 SES SES Spring 2003 77 23.7 24.0 -0.3 0.5 2.7 0.442 -0.490 31 0.9670 0.4406 SES SES Spring 2004 78 24.5 24.0 0.5 0.4 2.4 -0.266 -0.138 36 0.9767 0.6325 SWLS SWLS Spring 2003 79 25.6 25.0 0.6 1.0 5.7 -0.643 -0.149 32 0.9709 0.5239

1 Assessment scores with no shading for cells in Pr<W column are considered as normally distributed since Pr<W >0.05. StdErr – standard error; StdDev – standard deviation; n – sample numbers.

109

110

Table 16a. Numbers of Available Sets of Instrument Scores by Semester

Instrument Measure

Fall 2002

Spring 2003

Fall 2003

Spring 2004

Fall 2004

Spring 2005

Fall 2005

Total

FES 10 10 20 (10)

Hope 3 3 3 3 3 15 (3)

MSCS 7 7 7 7 7 35 (7)

SES 1 1 1 1 1 1 6 (1)

SWLS 1 1 1 3 (1)

Total 4 12 11 4 22 8 18 79

Table 16b. Breakdown of Normality of Available Sets of Instrument Scores by Semester

Semester Non-normal Normal Total Percent Normal Fall 2002 0 4 4 100.0 Spring 2003 1 11 12 91.7 Fall 2003 3 8 11 72.7 Spring 2004 1 3 4 75.0 Fall 2004 6 16 22 72.7 Spring 2005 1 7 8 87.5 Fall 2005 15 3 18 16.7 Total 27 52 79 65.4

111

Table 17a. Input Data for Hope-Total for Analysis of Variance for Hypothesis 41

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL02 26 1 1 10 FL03 30 1 3 10 FL04 29 1 5 10 FL02 23 1 1 10 FL03 26 1 3 10 FL04 27 1 5 10 FL02 29 1 1 10 FL03 22 1 3 10 SP03 32 2 1 1 FL02 19 1 1 10 FL03 27 1 3 10 SP03 32 2 1 1 FL02 21 1 1 10 FL03 26 1 3 10 SP03 26 2 1 1 FL02 25 1 1 10 FL03 28 1 3 10 SP03 25 2 1 1 FL02 27 1 1 10 FL03 31 1 3 10 SP03 23 2 1 1 FL02 28 1 1 10 FL03 21 1 3 10 FL03 28 2 1 2 FL02 27 1 1 10 FL03 25 1 3 10 FL03 31 2 1 2 FL02 28 1 1 10 FL03 24 1 3 10 FL03 25 2 1 2 FL02 27 1 1 10 SP04 25 1 4 10 FL03 20 2 1 2 FL02 28 1 1 10 SP04 24 1 4 10 FL03 23 2 1 2 FL02 25 1 1 10 SP04 26 1 4 10 FL03 29 2 2 1 FL02 26 1 1 10 SP04 22 1 4 10 FL03 32 2 2 1 FL02 28 1 1 10 SP04 23 1 4 10 FL03 30 2 2 1 FL02 28 1 1 10 SP04 23 1 4 10 FL03 26 2 2 1 FL02 26 1 1 10 SP04 27 1 4 10 SP04 23 2 2 2 FL02 25 1 1 10 SP04 26 1 4 10 SP04 28 2 2 2 FL02 25 1 1 10 SP04 24 1 4 10 SP04 27 2 2 2 FL02 23 1 1 10 SP04 26 1 4 10 SP04 25 2 2 2 FL02 24 1 1 10 FL04 24 1 5 10 SP04 22 2 3 1 FL02 26 1 1 10 FL04 31 1 5 10 SP04 29 2 3 1 FL02 21 1 1 10 FL04 23 1 5 10 SP04 21 2 3 1 SP03 25 1 2 10 FL04 25 1 5 10 SP04 23 2 3 1 SP03 26 1 2 10 FL04 26 1 5 10 FL04 24 2 3 2 SP03 30 1 2 10 FL04 27 1 5 10 FL04 27 2 3 2 SP03 30 1 2 10 FL04 25 1 5 10 FL04 32 2 3 2 SP03 27 1 2 10 FL04 31 1 5 10 FL04 18 2 3 2 SP03 25 1 2 10 FL04 26 1 5 10 SP03 27 3 1 1 SP03 28 1 2 10 FL04 25 1 5 10 SP03 24 3 1 1 FL03 29 1 3 10 FL04 23 1 5 10 SP03 29 3 1 1 FL03 24 1 3 10 FL04 20 1 5 10 FL03 25 3 1 2 1 See footnote at the end of the Table

112

Table 17a. Input Data for Hope-Total for Analysis of Variance for Hypothesis 4 (Cont.)1

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL03 22 3 1 2 FL03 29 4 1 2 FL02 26 5 1 1 FL03 28 3 1 2 FL03 27 4 1 2 FL02 21 5 1 1 SP04 22 3 1 3 FL03 25 4 1 2 SP03 30 5 1 2 SP04 24 3 1 3 FL03 25 4 1 2 SP03 30 5 1 2 SP04 28 3 1 3 FL03 22 4 1 2 SP03 25 5 1 2 FL03 23 3 2 1 SP04 29 4 1 3 FL03 25 5 1 3 FL03 19 3 2 1 SP04 30 4 1 3 FL03 29 5 1 3 SP04 23 3 2 2 SP04 25 4 1 3 FL03 23 5 1 3 SP04 25 3 2 2 SP04 24 4 1 3 SP04 26 5 1 4 FL04 24 3 2 3 SP04 22 4 1 3 SP04 27 5 1 4 FL04 24 3 2 3 FL04 29 4 1 4 SP04 23 5 1 4 SP03 32 4 1 1 FL04 30 4 1 4 FL04 27 5 1 5 SP03 32 4 1 1 FL04 26 4 1 4 FL04 26 5 1 5 SP03 23 4 1 1 FL04 31 4 1 4 FL04 25 5 1 5 SP03 25 4 1 1 FL04 26 4 1 4 SP03 27 4 1 1 FL02 24 5 1 1 1 Data are organized in 5 columns with column headings of Semester (Sm) Score (Scr), the semester count (Sm Ct), Replication (Rp), and semester Number (Sm No). The semester count indicates the maximum number of semesters X the student participated in, while Semester number indicates which of these (1, 2, ..,X) the score belongs to. The latter is used to organize the data in X-groups (Fig. 1.1 Data Structure for ANOVA for Hypothesis IV.

113

Table 17b. Input Data for MSCS-Total for Analysis of Variance for Hypothesis 41

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No SP03 117 1 1 10 SP05 85 1 4 10 FL05 83 1 5 10SP03 104 1 1 10 SP05 117 1 4 10 FL05 66 1 5 10SP03 128 1 1 10 SP05 67 1 4 10 FL05 116 1 5 10SP03 128 1 1 10 FL05 106 1 5 10 FL05 119 1 5 10SP03 103 1 1 10 FL05 119 1 5 10 SP03 115 2 1 1SP03 103 1 1 10 FL05 100 1 5 10 SP03 115 2 1 1FL03 103 1 2 10 FL05 95 1 5 10 SP03 105 2 1 1FL03 101 1 2 10 FL05 94 1 5 10 SP03 95 2 1 1FL03 92 1 2 10 FL05 105 1 5 10 SP03 145 2 1 1FL03 117 1 2 10 FL05 105 1 5 10 SP03 115 2 1 1FL03 96 1 2 10 FL05 109 1 5 10 SP03 74 2 1 1FL03 142 1 2 10 FL05 113 1 5 10 SP03 89 2 1 1FL03 73 1 2 10 FL05 113 1 5 10 FL03 118 2 1 2FL03 91 1 2 10 FL05 126 1 5 10 FL03 110 2 1 2FL03 78 1 2 10 FL05 105 1 5 10 FL03 118 2 1 2FL03 130 1 2 10 FL05 105 1 5 10 FL03 91 2 1 2FL03 115 1 2 10 FL05 103 1 5 10 FL03 142 2 1 2FL03 93 1 2 10 FL05 92 1 5 10 FL03 127 2 1 2FL03 123 1 2 10 FL05 78 1 5 10 FL03 97 2 1 2FL03 83 1 2 10 FL05 89 1 5 10 FL03 114 2 1 2FL03 111 1 2 10 FL05 60 1 5 10 FL03 106 2 2 1FL03 121 1 2 10 FL05 106 1 5 10 FL03 112 2 2 1FL03 94 1 2 10 FL05 94 1 5 10 FL04 114 2 2 2FL03 103 1 2 10 FL05 90 1 5 10 FL04 111 2 2 2FL03 97 1 2 10 FL05 86 1 5 10 FL04 92 2 3 1FL03 111 1 2 10 FL05 94 1 5 10 FL04 95 2 3 1FL04 85 1 3 10 FL05 130 1 5 10 SP05 133 2 3 2FL04 108 1 3 10 FL05 69 1 5 10 SP05 109 2 3 2FL04 103 1 3 10 FL05 54 1 5 10 SP05 99 2 4 1FL04 104 1 3 10 FL05 68 1 5 10 SP05 95 2 4 1FL04 100 1 3 10 FL05 60 1 5 10 FL05 99 2 4 2FL04 108 1 3 10 FL05 117 1 5 10 FL05 103 2 4 21 See footnote at the end of the Table

114

Table 17b. Input Data for MSCS-Total for Analysis of Variance for Hypothesis 4 (Cont.) 1

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No SP03 107 3 1 1 FL04 105 3 1 3 FL04 90 4 1 3SP03 97 3 1 1 FL04 121 3 1 3 FL04 121 4 1 3SP03 102 3 1 1 FL04 123 3 1 3 SP05 98 4 1 4SP03 86 3 1 1 FL04 101 3 1 3 SP05 107 4 1 4SP03 102 3 1 1 FL04 93 3 2 1 FL03 80 4 2 1SP03 94 3 1 1 FL04 120 3 2 1 FL03 106 4 2 1SP03 114 3 1 1 FL04 119 3 2 1 FL03 99 4 2 1SP03 76 3 1 1 FL04 119 3 2 1 FL04 74 4 2 2SP03 84 3 1 1 FL04 102 3 2 1 FL04 109 4 2 2FL03 105 3 1 2 SP05 95 3 2 2 FL04 97 4 2 2FL03 118 3 1 2 SP05 132 3 2 2 SP05 86 4 2 3FL03 84 3 1 2 SP05 102 3 2 2 SP05 102 4 2 3FL03 73 3 1 2 SP05 131 3 2 2 SP05 102 4 2 3FL03 103 3 1 2 SP05 91 3 2 2 FL05 74 4 2 4FL03 72 3 1 2 FL05 111 3 2 3 FL05 106 4 2 4FL03 115 3 1 2 FL05 119 3 2 3 FL05 101 4 2 4FL03 85 3 1 2 FL05 133 3 2 3 SP03 96 5 1 1FL03 85 3 1 2 FL05 98 3 2 3 FL03 103 5 1 2FL04 133 3 1 3 FL05 107.5 3 2 3 FL04 99 5 1 3FL04 106 3 1 3 SP03 102 4 1 1 SP05 100 5 1 4FL04 113 3 1 3 SP03 114 4 1 1 FL05 90 5 1 5FL04 114 3 1 3 FL03 103 4 1 2 FL04 90 3 1 3 FL03 115 4 1 2 1 Data are organized in 5 columns with column headings of Semester (Sm) Score (Scr), the semester count (Sm Ct), Replication (Rp), and semester Number (Sm No). The semester count indicates the maximum number of semesters X the student participated in, while Semester number indicates which of these (1, 2, ..,X) the score belongs to. The latter is used to organize the data in X-groups (Fig. 1.1 Data Structure for ANOVA for Hypothesis IV.

115

Table 17c. Input Data for SES for Analysis of Variance for Hypothesis 41

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL02 27 1 1 10 FL03 33 1 3 10 SP05 24 1 4 10FL02 27 1 1 10 FL03 33 1 3 10 SP05 22 1 4 10FL02 22 1 1 10 FL03 31 1 3 10 SP05 24 1 4 10FL02 24 1 1 10 FL03 32 1 3 10 SP05 26 1 4 10FL02 25 1 1 10 FL03 21 1 3 10 SP05 24 1 4 10FL02 21 1 1 10 FL03 19 1 3 10 SP05 25 1 4 10FL02 26 1 1 10 SP05 20 1 4 10 SP05 25 1 4 10FL02 25 1 1 10 SP05 28 1 4 10 SP05 23 1 4 10FL02 26 1 1 10 SP05 24 1 4 10 SP05 21 1 4 10FL02 23 1 1 10 SP05 24 1 4 10 SP05 29 1 4 10FL02 24 1 1 10 SP05 22 1 4 10 FL02 27 2 1 1SP03 24 1 2 10 SP05 24 1 4 10 FL02 26 2 1 1SP03 27 1 2 10 SP05 28 1 4 10 FL03 29 2 1 2SP03 17 1 2 10 SP05 20 1 4 10 FL03 23 2 1 2SP03 25 1 2 10 SP05 20 1 4 10 SP03 22 2 2 1SP03 23 1 2 10 SP05 24 1 4 10 SP03 29 2 2 1SP03 25 1 2 10 SP05 24 1 4 10 SP03 22 2 2 1SP03 24 1 2 10 SP05 22 1 4 10 SP03 26 2 2 1FL03 25 1 3 10 SP05 25 1 4 10 SP03 27 2 2 1FL03 24 1 3 10 SP05 27 1 4 10 FL03 25 2 2 2FL03 32 1 3 10 SP05 23 1 4 10 FL03 11 2 2 2FL03 24 1 3 10 SP05 23 1 4 10 FL03 24 2 2 2FL03 21 1 3 10 SP05 23 1 4 10 FL03 34 2 2 2FL03 30 1 3 10 SP05 22 1 4 10 FL03 21 2 2 2FL03 29 1 3 10 SP05 26 1 4 10 SP04 28 2 3 1FL03 13 1 3 10 SP05 23 1 4 10 SP04 26 2 3 1FL03 29 1 3 10 SP05 24 1 4 10 FL04 28 2 3 2FL03 28 1 3 10 SP05 25 1 4 10 FL04 26 2 3 2FL03 34 1 3 10 SP05 26 1 4 10 FL04 26 2 4 1FL03 29 1 3 10 SP05 22 1 4 10 FL04 25 2 4 1FL03 14 1 3 10 SP05 17 1 4 10 FL04 24 2 4 1FL03 28 1 3 10 SP05 24 1 4 10 FL04 24 2 4 11 See footnote at the end of the Table

116

Table 17c. Input Data for SES for Analysis of Variance for Hypothesis 4 (Cont.)1

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 25 2 4 1 SP04 23 3 4 1 SP05 27 3 4 3 FL04 26 2 4 1 SP04 26 3 4 1 SP05 24 3 4 3 SP05 24 2 4 2 SP04 24 3 4 1 SP05 23 3 4 3 SP05 27 2 4 2 SP04 27 3 4 1 SP05 20 3 4 3 SP05 24 2 4 2 SP04 24 3 4 1 FL03 24 4 1 1 SP05 24 2 4 2 SP04 20 3 4 1 FL03 33 4 1 1 SP05 27 2 4 2 FL04 26 3 4 2 FL03 29 4 1 1 SP05 26 2 4 2 FL04 24 3 4 2 FL03 14 4 1 1 FL02 24 3 1 1 FL04 24 3 4 2 SP04 27 4 1 2 FL02 24 3 1 1 FL04 23 3 4 2 SP04 29 4 1 2 FL02 24 3 1 1 FL04 27 3 4 2 SP04 27 4 1 2 SP03 25 3 1 2 FL04 25 3 4 2 SP04 21 4 1 2 SP03 23 3 1 2 FL04 22 3 4 2 FL04 27 4 1 3 SP03 25 3 1 2 FL04 25 3 4 2 FL04 29 4 1 3 FL03 30 3 1 3 FL04 21 3 4 2 FL04 27 4 1 3 FL03 29 3 1 3 FL04 23 3 4 2 FL04 21 4 1 3 FL03 33 3 1 3 FL04 26 3 4 2 SP05 25 4 1 4 SP03 24 3 2 1 FL04 24 3 4 2 SP05 26 4 1 4 FL03 29 3 2 2 FL04 27 3 4 2 SP05 26 4 1 4 SP05 25 3 2 3 FL04 24 3 4 2 SP05 24 4 1 4 FL03 18 3 3 1 FL04 20 3 4 2 SP03 21 5 1 1 SP04 23 3 3 2 SP05 26 3 4 3 SP03 23 5 1 1 FL04 23 3 3 3 SP05 24 3 4 3 SP03 24 5 1 1 SP04 26 3 4 1 SP05 26 3 4 3 SP03 25 5 1 1 SP04 24 3 4 1 SP05 23 3 4 3 SP03 19 5 1 1 SP04 24 3 4 1 SP05 25 3 4 3 SP03 22 5 1 1 SP04 23 3 4 1 SP05 25 3 4 3 FL03 17 5 1 2 SP04 27 3 4 1 SP05 19 3 4 3 FL03 34 5 1 2 SP04 25 3 4 1 SP05 25 3 4 3 FL03 34 5 1 2 SP04 22 3 4 1 SP05 26 3 4 3 FL03 31 5 1 2 SP04 25 3 4 1 SP05 23 3 4 3 FL03 25 5 1 2 SP04 21 3 4 1 SP05 24 3 4 3 FL03 33 5 1 2 1 See footnote at the end of the Table

117

Table 17c. Input Data for SES for Analysis of Variance for Hypothesis 4 (Cont.)1

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No SP04 19 5 1 3 FL02 27 6 1 1 SP04 25 6 1 4SP04 26 5 1 3 FL02 21 6 1 1 SP04 24 6 1 4SP04 28 5 1 3 FL02 25 6 1 1 SP04 26 6 1 4SP04 23 5 1 3 FL02 29 6 1 1 SP04 24 6 1 4SP04 25 5 1 3 FL02 28 6 1 1 SP04 23 6 1 4SP04 29 5 1 3 FL02 28 6 1 1 SP04 24 6 1 4FL04 19 5 1 4 SP03 18 6 1 2 FL04 25 6 1 5FL04 26 5 1 4 SP03 24 6 1 2 FL04 24 6 1 5FL04 28 5 1 4 SP03 24 6 1 2 FL04 26 6 1 5FL04 23 5 1 4 SP03 26 6 1 2 FL04 24 6 1 5FL04 25 5 1 4 SP03 27 6 1 2 FL04 23 6 1 5FL04 29 5 1 4 SP03 28 6 1 2 FL04 24 6 1 5SP05 36 5 1 5 FL03 29 6 1 3 SP05 25 6 1 6SP05 22 5 1 5 FL03 33 6 1 3 SP05 24 6 1 6SP05 28 5 1 5 FL03 23 6 1 3 SP05 25 6 1 6SP05 23 5 1 5 FL03 29 6 1 3 SP05 28 6 1 6SP05 25 5 1 5 FL03 14 6 1 3 SP05 23 6 1 6SP05 29 5 1 5 FL03 29 6 1 3 SP05 23 6 1 61 Data are organized in 5 columns with column headings of Semester (Sm) Score (Scr), the semester count (Sm Ct), Replication (Rp), and semester Number (Sm No). The semester count indicates the maximum number of semesters X the student participated in, while Semester number indicates which of these (1, 2, ..,X) the score belongs to. The latter is used to organize the data in X-groups (Fig. 1.1 Data Structure for ANOVA for Hypothesis IV.

118

Table 17d. Input Data for SWLS for Analysis of Variance for Hypothesis 41

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No SP03 23 1 1 10 FL05 24 1 3 10 FL05 19 1 3 10 SP03 27 1 1 10 FL05 27 1 3 10 FL05 25 1 3 10 SP03 20 1 1 10 FL05 14 1 3 10 FL05 31 1 3 10 SP03 31 1 1 10 FL05 31 1 3 10 SP03 33 2 1 1 SP03 26 1 1 10 FL05 29 1 3 10 SP03 25 2 1 1 SP03 34 1 1 10 FL05 32 1 3 10 SP03 27 2 1 1 SP03 33 1 1 10 FL05 21 1 3 10 SP03 22 2 1 1 SP03 20 1 1 10 FL05 22 1 3 10 SP03 22 2 1 1 SP03 31 1 1 10 FL05 26 1 3 10 SP03 22 2 1 1 SP03 16 1 1 10 FL05 29 1 3 10 FL04 33 2 1 2 SP03 35 1 1 10 FL05 28 1 3 10 FL04 28 2 1 2 SP03 27 1 1 10 FL05 28 1 3 10 FL04 26 2 1 2 SP03 17 1 1 10 FL05 25 1 3 10 FL04 29 2 1 2 SP03 23 1 1 10 FL05 25 1 3 10 FL04 29 2 1 2 SP03 25 1 1 10 FL05 14 1 3 10 FL04 33 2 1 2 FL04 10 1 2 10 FL05 22 1 3 10 SP03 25 2 2 1 FL04 26 1 2 10 FL05 29 1 3 10 SP03 21 2 2 1 FL04 32 1 2 10 FL05 32 1 3 10 SP03 20 2 2 1 FL04 30 1 2 10 FL05 18 1 3 10 FL05 15 2 2 2 FL04 19 1 2 10 FL05 13 1 3 10 FL05 26 2 2 2 FL04 29 1 2 10 FL05 27 1 3 10 FL05 28 2 2 2 FL04 29 1 2 10 FL05 20 1 3 10 FL04 22 2 3 1 FL05 33 1 3 10 FL05 14 1 3 10 FL04 33 2 3 1 FL05 30 1 3 10 FL05 10 1 3 10 FL04 12 2 3 1 FL05 30 1 3 10 FL05 14 1 3 10 FL04 25 2 3 1 FL05 35 1 3 10 FL05 31 1 3 10 FL04 30 2 3 1 FL05 27 1 3 10 FL05 19 1 3 10 FL04 31 2 3 1 FL05 18 1 3 10 FL05 20 1 3 10 FL04 33 2 3 1 FL05 27 1 3 10 FL05 17 1 3 10 FL04 27 2 3 1 FL05 19 1 3 10 FL05 30 1 3 10 FL04 11 2 3 1 FL05 29 1 3 10 FL05 32 1 3 10 FL04 29 2 3 1 FL05 28 1 3 10 FL05 15 1 3 10 FL04 24 2 3 1 1 See footnote at the end of the Table

119

Table 17d. Input Data for MSCS-Total for Analysis of Variance for Hypothesis 4 (Cont.)1

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 24 2 3 1 FL05 26 2 3 2 FL04 15 3 1 2 FL04 12 2 3 1 FL05 24 2 3 2 FL04 27 3 1 2 FL04 30 2 3 1 FL05 17 2 3 2 FL04 28 3 1 2 FL04 31 2 3 1 FL05 12 2 3 2 FL04 22 3 1 2 FL04 17 2 3 1 FL05 17 2 3 2 FL04 23 3 1 2 FL05 22 2 3 2 FL05 17 2 3 2 FL04 33 3 1 2 FL05 30 2 3 2 SP03 25 3 1 1 FL04 25 3 1 2 FL05 23 2 3 2 SP03 29 3 1 1 FL05 34 3 1 3 FL05 31 2 3 2 SP03 21 3 1 1 FL05 33 3 1 3 FL05 32 2 3 2 SP03 34 3 1 1 FL05 33 3 1 3 FL05 29 2 3 2 SP03 30 3 1 1 FL05 22 3 1 3 FL05 28 2 3 2 SP03 30 3 1 1 FL05 19 3 1 3 FL05 27 2 3 2 SP03 31 3 1 1 FL05 26 3 1 3 FL05 31 2 3 2 SP03 13 3 1 1 FL05 15 3 1 3 FL05 29 2 3 2 FL04 27 3 1 2 FL04 15 3 1 2 1 Data are organized in 5 columns with column headings of Semester (Sm) Score (Scr), the semester count (Sm Ct), Replication (Rp), and semester Number (Sm No). The semester count indicates the maximum number of semesters X the student participated in, while Semester number indicates which of these (1, 2, ..,X) the score belongs to. The latter is used to organize the data in X-groups (Fig. 1.1 Data Structure for ANOVA for Hypothesis IV.

120

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 41

FES-COH

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 1.8 1 1 10 FL05 5.2 1 2 10 FL04 5.9 2 1 1 FL04 5.9 1 1 10 FL05 6.5 1 2 10 FL04 5.9 2 1 1 FL04 4.5 1 1 10 FL05 6.5 1 2 10 FL04 3.1 2 1 1 FL04 5.2 1 1 10 FL05 6.5 1 2 10 FL04 5.2 2 1 1 FL04 2.5 1 1 10 FL05 6.5 1 2 10 FL04 6.5 2 1 1 FL04 3.8 1 1 10 FL05 5.2 1 2 10 FL04 4.5 2 1 1 FL04 4.5 1 1 10 FL05 4.5 1 2 10 FL04 5.2 2 1 1 FL04 5.2 1 1 10 FL05 5.9 1 2 10 FL04 4.5 2 1 1 FL04 5.2 1 1 10 FL05 5.9 1 2 10 FL04 4.5 2 1 1 FL04 4.0 1 1 10 FL05 3.1 1 2 10 FL04 2.8 2 1 1 FL04 5.3 1 1 10 FL05 4.5 1 2 10 FL04 3.4 2 1 1 FL04 2.8 1 1 10 FL05 5.2 1 2 10 FL04 2.8 2 1 1 FL04 4.0 1 1 10 FL05 3.8 1 2 10 FL04 2.0 2 1 1 FL04 3.4 1 1 10 FL05 4.5 1 2 10 FL04 2.8 2 1 1 FL04 4.7 1 1 10 FL05 3.8 1 2 10 FL04 4.0 2 1 1 FL04 3.1 1 1 10 FL05 5.2 1 2 10 FL05 5.2 2 1 2 FL05 3.8 1 2 10 FL05 3.8 1 2 10 FL05 3.1 2 1 2 FL05 5.9 1 2 10 FL05 3.8 1 2 10 FL05 5.9 2 1 2 FL05 5.9 1 2 10 FL05 3.1 1 2 10 FL05 5.9 2 1 2 FL05 4.5 1 2 10 FL05 5.9 1 2 10 FL05 5.9 2 1 2 FL05 5.9 1 2 10 FL05 5.9 1 2 10 FL05 3.8 2 1 2 FL05 3.1 1 2 10 FL05 5.9 1 2 10 FL05 6.5 2 1 2 FL05 5.9 1 2 10 FL05 6.5 1 2 10 FL05 5.9 2 1 2 FL05 1.1 1 2 10 FL05 5.2 1 2 10 FL05 5.2 2 1 2 FL05 5.2 1 2 10 FL05 5.2 1 2 10 FL05 5.2 2 1 2 FL05 6.5 1 2 10 FL05 5.9 1 2 10 FL05 5.2 2 1 2 FL05 3.8 1 2 10 FL05 3.8 1 2 10 FL05 5.9 2 1 2 FL05 5.2 1 2 10 FL05 1.1 1 2 10 FL05 3.8 2 1 2 FL05 3.1 1 2 10 FL05 3.8 1 2 10 FL05 2.5 2 1 2 FL05 3.1 1 2 10 FL05 5.2 1 2 10 FL05 1.1 2 1 2 FL05 4.5 1 2 10 FL04 5.2 2 1 1 FL05 3.8 2 1 2 FL05 3.1 1 2 10 FL04 5.2 2 1 1 FL05 6.5 2 1 2 FL05 3.8 1 2 10 FL04 4.5 2 1 1 FL05 5.2 2 1 2 1 See footnote at the end of the Table

121

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-EXP

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 2.8 1 1 10 FL05 4.7 1 2 10 FL04 4.0 2 1 1 FL04 4.7 1 1 10 FL05 5.9 1 2 10 FL04 3.4 2 1 1 FL04 4.7 1 1 10 FL05 4.0 1 2 10 FL04 2.8 2 1 1 FL04 2.2 1 1 10 FL05 4.0 1 2 10 FL04 5.3 2 1 1 FL04 4.0 1 1 10 FL05 5.3 1 2 10 FL04 4.0 2 1 1 FL04 5.3 1 1 10 FL05 2.8 1 2 10 FL04 4.7 2 1 1 FL04 2.2 1 1 10 FL05 4.7 1 2 10 FL04 2.8 2 1 1 FL04 3.4 1 1 10 FL05 5.3 1 2 10 FL04 2.2 2 1 1 FL04 4.0 1 1 10 FL05 5.9 1 2 10 FL04 4.0 2 1 1 FL04 3.3 1 1 10 FL05 3.4 1 2 10 FL04 7.0 2 1 1 FL04 4.9 1 1 10 FL05 4.7 1 2 10 FL04 3.3 2 1 1 FL04 6.5 1 1 10 FL05 5.3 1 2 10 FL04 7.0 2 1 1 FL04 4.9 1 1 10 FL05 4.7 1 2 10 FL04 7.0 2 1 1 FL04 5.4 1 1 10 FL05 5.9 1 2 10 FL04 8.0 2 1 1 FL04 4.4 1 1 10 FL05 3.4 1 2 10 FL04 5.4 2 1 1 FL04 3.4 1 1 10 FL05 4.0 1 2 10 FL05 4.7 2 1 2 FL05 2.8 1 2 10 FL05 3.4 1 2 10 FL05 1.6 2 1 2 FL05 3.4 1 2 10 FL05 3.4 1 2 10 FL05 4.7 2 1 2 FL05 4.0 1 2 10 FL05 2.8 1 2 10 FL05 4.0 2 1 2 FL05 4.0 1 2 10 FL05 3.4 1 2 10 FL05 3.4 2 1 2 FL05 4.7 1 2 10 FL05 4.7 1 2 10 FL05 4.7 2 1 2 FL05 4.0 1 2 10 FL05 4.0 1 2 10 FL05 6.5 2 1 2 FL05 4.7 1 2 10 FL05 4.7 1 2 10 FL05 4.0 2 1 2 FL05 2.8 1 2 10 FL05 5.3 1 2 10 FL05 3.4 2 1 2 FL05 5.3 1 2 10 FL05 4.0 1 2 10 FL05 4.0 2 1 2 FL05 5.3 1 2 10 FL05 4.7 1 2 10 FL05 4.7 2 1 2 FL05 2.8 1 2 10 FL05 2.8 1 2 10 FL05 6.5 2 1 2 FL05 4.7 1 2 10 FL05 1.8 1 2 10 FL05 5.9 2 1 2 FL05 2.8 1 2 10 FL05 5.3 1 2 10 FL05 2.2 2 1 2 FL05 5.3 1 2 10 FL05 4.7 1 2 10 FL05 3.4 2 1 2 FL05 5.9 1 2 10 FL04 4.7 2 1 1 FL05 4.7 2 1 2 FL05 3.4 1 2 10 FL04 4.7 2 1 1 FL05 5.3 2 1 2 FL05 4.0 1 2 10 FL04 4.7 2 1 1 FL05 3.4 2 1 2 1 See footnote at the end of the Table

122

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-CONF

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 7.5 1 1 10 FL05 5.4 1 2 10 FL04 3.9 2 1 1 FL04 4.9 1 1 10 FL05 4.9 1 2 10 FL04 5.4 2 1 1 FL04 6.0 1 1 10 FL05 4.4 1 2 10 FL04 7.0 2 1 1 FL04 4.9 1 1 10 FL05 4.4 1 2 10 FL04 4.9 2 1 1 FL04 7.5 1 1 10 FL05 3.9 1 2 10 FL04 5.4 2 1 1 FL04 4.9 1 1 10 FL05 5.4 1 2 10 FL04 5.4 2 1 1 FL04 4.9 1 1 10 FL05 6.0 1 2 10 FL04 6.5 2 1 1 FL04 4.9 1 1 10 FL05 4.4 1 2 10 FL04 4.9 2 1 1 FL04 5.4 1 1 10 FL05 8.0 1 2 10 FL04 4.4 2 1 1 FL04 4.5 1 1 10 FL05 6.0 1 2 10 FL04 5.3 2 1 1 FL04 1.3 1 1 10 FL05 4.9 1 2 10 FL04 2.9 2 1 1 FL04 5.3 1 1 10 FL05 6.0 1 2 10 FL04 2.9 2 1 1 FL04 6.5 1 1 10 FL05 6.0 1 2 10 FL04 4.5 2 1 1 FL04 4.5 1 1 10 FL05 6.0 1 2 10 FL04 2.1 2 1 1 FL04 5.3 1 1 10 FL05 6.0 1 2 10 FL04 6.1 2 1 1 FL04 6.5 1 1 10 FL05 4.4 1 2 10 FL05 4.9 2 1 2 FL05 7.0 1 2 10 FL05 6.5 1 2 10 FL05 4.9 2 1 2 FL05 7.3 1 2 10 FL05 5.4 1 2 10 FL05 3.3 2 1 2 FL05 3.3 1 2 10 FL05 7.0 1 2 10 FL05 3.9 2 1 2 FL05 4.4 1 2 10 FL05 4.4 1 2 10 FL05 5.4 2 1 2 FL05 4.9 1 2 10 FL05 4.4 1 2 10 FL05 6.0 2 1 2 FL05 6.5 1 2 10 FL05 5.4 1 2 10 FL05 4.4 2 1 2 FL05 6.0 1 2 10 FL05 3.9 1 2 10 FL05 4.4 2 1 2 FL05 7.0 1 2 10 FL05 6.0 1 2 10 FL05 7.5 2 1 2 FL05 6.5 1 2 10 FL05 3.9 1 2 10 FL05 5.4 2 1 2 FL05 4.4 1 2 10 FL05 5.4 1 2 10 FL05 3.9 2 1 2 FL05 7.5 1 2 10 FL05 7.0 1 2 10 FL05 4.4 2 1 2 FL05 4.9 1 2 10 FL05 7.0 1 2 10 FL05 6.5 2 1 2 FL05 6.0 1 2 10 FL05 6.0 1 2 10 FL05 7.0 2 1 2 FL05 6.0 1 2 10 FL05 4.9 1 2 10 FL05 7.1 2 1 2 FL05 6.0 1 2 10 FL04 4.9 2 1 1 FL05 4.9 2 1 2 FL05 4.4 1 2 10 FL04 4.9 2 1 1 FL05 4.4 2 1 2 FL05 6.5 1 2 10 FL04 3.3 2 1 1 FL05 3.9 2 1 2 1 See footnote at the end of the Table

123

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-IND

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 2.9 1 1 10 FL05 5.3 1 2 10 FL04 6.1 2 1 1FL04 3.7 1 1 10 FL05 3.7 1 2 10 FL04 5.3 2 1 1FL04 4.5 1 1 10 FL05 5.3 1 2 10 FL04 2.9 2 1 1FL04 5.3 1 1 10 FL05 6.9 1 2 10 FL04 6.9 2 1 1FL04 5.3 1 1 10 FL05 4.5 1 2 10 FL04 4.5 2 1 1FL04 3.7 1 1 10 FL05 5.3 1 2 10 FL04 5.3 2 1 1FL04 6.1 1 1 10 FL05 4.5 1 2 10 FL04 3.7 2 1 1FL04 5.3 1 1 10 FL05 4.5 1 2 10 FL04 5.3 2 1 1FL04 7.0 1 1 10 FL05 3.7 1 2 10 FL04 2.9 2 1 1FL04 4.7 1 1 10 FL05 2.1 1 2 10 FL04 4.1 2 1 1FL04 7.2 1 1 10 FL05 2.9 1 2 10 FL04 4.7 2 1 1FL04 5.3 1 1 10 FL05 2.9 1 2 10 FL04 5.9 2 1 1FL04 4.5 1 1 10 FL05 3.7 1 2 10 FL04 6.6 2 1 1FL04 4.7 1 1 10 FL05 4.5 1 2 10 FL04 5.3 2 1 1FL04 5.9 1 1 10 FL05 2.9 1 2 10 FL04 5.3 2 1 1FL04 4.5 1 1 10 FL05 5.3 1 2 10 FL05 6.1 2 1 2FL05 9.5 1 2 10 FL05 2.9 1 2 10 FL05 4.5 2 1 2FL05 4.5 1 2 10 FL05 4.5 1 2 10 FL05 5.3 2 1 2FL05 3.7 1 2 10 FL05 4.5 1 2 10 FL05 6.1 2 1 2FL05 6.0 1 2 10 FL05 7.0 1 2 10 FL05 5.3 2 1 2FL05 2.9 1 2 10 FL05 6.1 1 2 10 FL05 3.7 2 1 2FL05 4.5 1 2 10 FL05 3.7 1 2 10 FL05 6.1 2 1 2FL05 3.7 1 2 10 FL05 4.5 1 2 10 FL05 5.3 2 1 2FL05 2.9 1 2 10 FL05 5.3 1 2 10 FL05 3.7 2 1 2FL05 4.5 1 2 10 FL05 6.1 1 2 10 FL05 4.5 2 1 2FL05 3.7 1 2 10 FL05 5.3 1 2 10 FL05 5.3 2 1 2FL05 3.7 1 2 10 FL05 9.5 1 2 10 FL05 5.3 2 1 2FL05 4.5 1 2 10 FL05 2.9 1 2 10 FL05 5.4 2 1 2FL05 4.5 1 2 10 FL05 6.1 1 2 10 FL05 4.5 2 1 2FL05 4.5 1 2 10 FL05 4.5 1 2 10 FL05 2.9 2 1 2FL05 5.3 1 2 10 FL04 6.1 2 1 1 FL05 4.5 2 1 2FL05 4.5 1 2 10 FL04 3.7 2 1 1 FL05 6.1 2 1 2FL05 4.5 1 2 10 FL04 3.7 2 1 1 FL05 6.1 2 1 21 See footnote at the end of the Table

124

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-AO

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 4.7 1 1 10 FL05 5.0 1 2 10 FL04 5.3 2 1 1 FL04 5.9 1 1 10 FL05 5.3 1 2 10 FL04 5.9 2 1 1 FL04 6.6 1 1 10 FL05 6.6 1 2 10 FL04 4.1 2 1 1 FL04 5.9 1 1 10 FL05 4.7 1 2 10 FL04 5.9 2 1 1 FL04 5.3 1 1 10 FL05 6.6 1 2 10 FL04 5.9 2 1 1 FL04 5.3 1 1 10 FL05 5.3 1 2 10 FL04 5.3 2 1 1 FL04 7.2 1 1 10 FL05 4.1 1 2 10 FL04 6.6 2 1 1 FL04 5.9 1 1 10 FL05 5.9 1 2 10 FL04 6.6 2 1 1 FL04 6.6 1 1 10 FL05 5.3 1 2 10 FL04 5.3 2 1 1 FL04 3.6 1 1 10 FL05 6.6 1 2 10 FL04 3.0 2 1 1 FL04 4.1 1 1 10 FL05 5.3 1 2 10 FL04 4.7 2 1 1 FL04 5.2 1 1 10 FL05 3.5 1 2 10 FL04 4.1 2 1 1 FL04 4.1 1 1 10 FL05 5.3 1 2 10 FL04 5.8 2 1 1 FL04 4.1 1 1 10 FL05 4.7 1 2 10 FL04 3.0 2 1 1 FL04 5.8 1 1 10 FL05 3.5 1 2 10 FL04 6.3 2 1 1 FL04 4.7 1 1 10 FL05 6.6 1 2 10 FL05 7.2 2 1 2 FL05 5.3 1 2 10 FL05 5.3 1 2 10 FL05 4.1 2 1 2 FL05 7.2 1 2 10 FL05 6.6 1 2 10 FL05 5.6 2 1 2 FL05 4.1 1 2 10 FL05 5.9 1 2 10 FL05 5.3 2 1 2 FL05 5.3 1 2 10 FL05 6.6 1 2 10 FL05 5.9 2 1 2 FL05 5.3 1 2 10 FL05 6.6 1 2 10 FL05 4.7 2 1 2 FL05 5.9 1 2 10 FL05 4.7 1 2 10 FL05 7.2 2 1 2 FL05 5.9 1 2 10 FL05 7.2 1 2 10 FL05 5.3 2 1 2 FL05 3.5 1 2 10 FL05 6.6 1 2 10 FL05 6.6 2 1 2 FL05 5.9 1 2 10 FL05 6.6 1 2 10 FL05 6.6 2 1 2 FL05 6.6 1 2 10 FL05 5.9 1 2 10 FL05 5.9 2 1 2 FL05 5.9 1 2 10 FL05 5.3 1 2 10 FL05 5.3 2 1 2 FL05 6.6 1 2 10 FL05 5.3 1 2 10 FL05 5.3 2 1 2 FL05 5.9 1 2 10 FL05 7.2 1 2 10 FL05 5.9 2 1 2 FL05 5.3 1 2 10 FL05 6.6 1 2 10 FL05 5.9 2 1 2 FL05 5.3 1 2 10 FL04 7.2 2 1 1 FL05 5.3 2 1 2 FL05 7.2 1 2 10 FL04 6.6 2 1 1 FL05 5.9 2 1 2 FL05 6.6 1 2 10 FL04 4.1 2 1 1 FL05 6.3 2 1 2 1 See footnote at the end of the Table

125

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-ICO

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 2.5 1 1 10 FL05 5.2 1 2 10 FL04 5.2 2 1 1 FL04 6.3 1 1 10 FL05 6.3 1 2 10 FL04 6.3 2 1 1 FL04 5.8 1 1 10 FL05 6.3 1 2 10 FL04 5.2 2 1 1 FL04 5.8 1 1 10 FL05 6.3 1 2 10 FL04 4.7 2 1 1 FL04 4.1 1 1 10 FL05 6.9 1 2 10 FL04 6.3 2 1 1 FL04 4.7 1 1 10 FL05 3.6 1 2 10 FL04 4.7 2 1 1 FL04 5.8 1 1 10 FL05 5.2 1 2 10 FL04 5.2 2 1 1 FL04 4.1 1 1 10 FL05 4.7 1 2 10 FL04 3.6 2 1 1 FL04 5.2 1 1 10 FL05 5.8 1 2 10 FL04 4.7 2 1 1 FL04 4.8 1 1 10 FL05 4.7 1 2 10 FL04 3.8 2 1 1 FL04 5.3 1 1 10 FL05 5.8 1 2 10 FL04 3.8 2 1 1 FL04 5.9 1 1 10 FL05 4.7 1 2 10 FL04 3.3 2 1 1 FL04 5.9 1 1 10 FL05 4.7 1 2 10 FL04 5.9 2 1 1 FL04 4.8 1 1 10 FL05 3.6 1 2 10 FL04 3.3 2 1 1 FL04 6.4 1 1 10 FL05 4.1 1 2 10 FL04 5.9 2 1 1 FL04 5.2 1 1 10 FL05 5.2 1 2 10 FL05 5.8 2 1 2 FL05 3.0 1 2 10 FL05 6.3 1 2 10 FL05 3.6 2 1 2 FL05 5.8 1 2 10 FL05 4.7 1 2 10 FL05 5.2 2 1 2 FL05 4.1 1 2 10 FL05 4.7 1 2 10 FL05 5.2 2 1 2 FL05 4.7 1 2 10 FL05 6.3 1 2 10 FL05 6.3 2 1 2 FL05 5.8 1 2 10 FL05 5.8 1 2 10 FL05 4.7 2 1 2 FL05 4.7 1 2 10 FL05 5.2 1 2 10 FL05 5.2 2 1 2 FL05 5.2 1 2 10 FL05 5.2 1 2 10 FL05 6.9 2 1 2 FL05 1.9 1 2 10 FL05 5.2 1 2 10 FL05 5.2 2 1 2 FL05 5.2 1 2 10 FL05 6.3 1 2 10 FL05 6.1 2 1 2 FL05 5.8 1 2 10 FL05 5.8 1 2 10 FL05 5.2 2 1 2 FL05 3.6 1 2 10 FL05 3.0 1 2 10 FL05 6.3 2 1 2 FL05 5.8 1 2 10 FL05 2.5 1 2 10 FL05 4.1 2 1 2 FL05 4.1 1 2 10 FL05 4.1 1 2 10 FL05 5.8 2 1 2 FL05 5.8 1 2 10 FL05 5.8 1 2 10 FL05 4.1 2 1 2 FL05 5.8 1 2 10 FL04 5.8 2 1 1 FL05 4.1 2 1 2 FL05 6.3 1 2 10 FL04 5.2 2 1 1 FL05 6.9 2 1 2 FL05 5.8 1 2 10 FL04 4.7 2 1 1 FL05 4.1 2 1 2 1 See footnote at the end of the Table

126

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-ARO

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 6.4 1 1 10 FL05 3.3 1 2 10 FL04 5.2 2 1 1 FL04 6.4 1 1 10 FL05 6.9 1 2 10 FL04 4.8 2 1 1 FL04 5.9 1 1 10 FL05 5.9 1 2 10 FL04 4.3 2 1 1 FL04 4.3 1 1 10 FL05 6.9 1 2 10 FL04 4.3 2 1 1 FL04 3.3 1 1 10 FL05 5.3 1 2 10 FL04 5.9 2 1 1 FL04 5.3 1 1 10 FL05 5.9 1 2 10 FL04 5.9 2 1 1 FL04 5.9 1 1 10 FL05 5.3 1 2 10 FL04 5.9 2 1 1 FL04 6.6 1 1 10 FL05 6.4 1 2 10 FL04 5.9 2 1 1 FL04 6.1 1 1 10 FL05 4.3 1 2 10 FL04 4.8 2 1 1 FL04 6.1 1 1 10 FL05 6.9 1 2 10 FL04 5.3 2 1 1 FL05 2.3 1 2 10 FL05 5.3 1 2 10 FL04 5.6 2 1 1 FL05 4.8 1 2 10 FL05 4.8 1 2 10 FL04 2.7 2 1 1 FL05 3.3 1 2 10 FL05 5.9 1 2 10 FL04 5.6 2 1 1 FL05 5.3 1 2 10 FL05 4.8 1 2 10 FL04 6.1 2 1 1 FL05 6.4 1 2 10 FL05 2.8 1 2 10 FL04 4.6 2 1 1 FL05 3.8 1 2 10 FL05 4.3 1 2 10 FL04 6.6 2 1 1 FL05 5.3 1 2 10 FL05 4.8 1 2 10 FL04 5.6 2 1 1 FL05 2.8 1 2 10 FL05 3.8 1 2 10 FL04 5.1 2 1 1 FL05 5.3 1 2 10 FL05 5.9 1 2 10 FL04 4.6 2 1 1 FL05 6.4 1 2 10 FL05 5.9 1 2 10 FL04 5.3 2 1 1 FL05 5.3 1 2 10 FL05 6.9 1 2 10 FL05 5.9 2 1 2 FL05 4.8 1 2 10 FL05 5.3 1 2 10 FL05 5.3 2 1 2 FL05 5.3 1 2 10 FL05 4.8 1 2 10 FL05 4.8 2 1 2 FL05 4.8 1 2 10 FL05 5.3 1 2 10 FL05 5.9 2 1 2 FL05 5.9 1 2 10 FL05 3.8 1 2 10 FL05 5.2 2 1 2 FL05 5.3 1 2 10 FL05 2.3 1 2 10 FL05 4.8 2 1 2 FL05 4.3 1 2 10 FL05 3.8 1 2 10 FL05 5.3 2 1 2 FL05 5.3 1 2 10 FL05 5.3 1 2 10 FL05 4.3 2 1 2 FL05 4.3 1 2 10 FL05 5.3 1 2 10 FL05 5.3 2 1 2 FL05 3.3 1 2 10 FL04 5.9 2 1 1 FL05 5.9 2 1 2 FL05 5.9 1 2 10 FL04 4.3 2 1 1 FL05 6.4 2 1 2 FL05 4.8 1 2 10 FL04 4.8 2 1 1 FL05 4.8 2 1 2 FL05 6.4 1 2 10 FL04 5.9 2 1 1 FL05 6.4 2 1 2 1 See footnote at the end of the Table

127

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-ARO

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL05 5.3 2 1 2 FL05 5.9 2 1 2 FL05 3.3 2 1 2 FL05 4.8 2 1 2 FL05 4.3 2 1 2 FL05 4.8 2 1 2 FL05 5.9 2 1 2 FL05 5.3 2 1 2 FL05 5.3 2 1 2 FL05 6.4 2 1 2 FL05 6.9 2 1 2

FES-MRE FL04 6.6 1 1 10 FL05 5.1 1 2 10 FL05 5.1 1 2 10 FL04 6.6 1 1 10 FL05 6.1 1 2 10 FL05 6.6 1 2 10 FL04 6.1 1 1 10 FL05 5.6 1 2 10 FL05 5.9 1 2 10 FL04 5.1 1 1 10 FL05 4.1 1 2 10 FL05 6.1 1 2 10 FL04 5.6 1 1 10 FL05 3.6 1 2 10 FL05 5.6 1 2 10 FL04 6.1 1 1 10 FL05 4.6 1 2 10 FL05 3.2 1 2 10 FL04 5.1 1 1 10 FL05 6.1 1 2 10 FL05 6.1 1 2 10 FL04 3.2 1 1 10 FL05 5.6 1 2 10 FL05 5.1 1 2 10 FL04 4.2 1 1 10 FL05 6.1 1 2 10 FL05 4.1 1 2 10 FL04 5.8 1 1 10 FL05 5.6 1 2 10 FL05 4.6 1 2 10 FL05 5.1 1 2 10 FL05 6.6 1 2 10 FL05 5.6 1 2 10 FL05 6.1 1 2 10 FL05 6.1 1 2 10 FL04 5.6 2 1 1 FL05 6.6 1 2 10 FL05 6.1 1 2 10 FL04 6.6 2 1 1 FL05 6.6 1 2 10 FL05 4.6 1 2 10 FL04 5.3 2 1 1 FL05 5.9 1 2 10 FL05 7.1 1 2 10 FL04 4.6 2 1 1 FL05 5.6 1 2 10 FL05 6.1 1 2 10 FL04 4.6 2 1 1 FL05 4.6 1 2 10 FL05 6.6 1 2 10 FL04 3.2 2 1 1 FL05 6.1 1 2 10 FL05 6.6 1 2 10 FL04 5.6 2 1 1 FL05 5.6 1 2 10 FL05 5.6 1 2 10 FL04 6.6 2 1 1 FL05 6.1 1 2 10 FL05 6.1 1 2 10 FL04 6.6 2 1 1 FL05 6.1 1 2 10 FL05 6.1 1 2 10 FL04 6.1 2 1 1 FL05 6.1 1 2 10 FL05 5.6 1 2 10 FL04 6.6 2 1 1 FL05 5.6 1 2 10 FL05 4.6 1 2 10 FL04 6.6 2 1 1 FL05 6.6 1 2 10 FL05 5.6 1 2 10 FL04 2.6 2 1 1 FL05 6.1 1 2 10 FL05 6.1 1 2 10 FL04 3.7 2 1 1 1 See footnote at the end of the Table

128

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-MRE

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL05 5.1 1 2 10 FL04 6.6 2 1 1 FL05 3.2 2 1 2 FL05 6.6 1 2 10 FL04 6.1 2 1 1 FL05 5.1 2 1 2 FL05 5.9 1 2 10 FL04 6.6 2 1 1 FL05 6.1 2 1 2 FL05 6.1 1 2 10 FL04 6.6 2 1 1 FL05 6.6 2 1 2 FL05 5.6 1 2 10 FL04 2.6 2 1 1 FL05 6.6 2 1 2 FL05 3.2 1 2 10 FL04 3.7 2 1 1 FL05 7.1 2 1 2 FL05 6.1 1 2 10 FL04 2.6 2 1 1 FL05 6.6 2 1 2 FL05 5.1 1 2 10 FL04 6.3 2 1 1 FL05 6.6 2 1 2 FL05 4.1 1 2 10 FL04 3.7 2 1 1 FL05 5.1 2 1 2 FL05 4.6 1 2 10 FL04 4.8 2 1 1 FL05 6.6 2 1 2 FL05 5.6 1 2 10 FL04 3.2 2 1 1 FL05 6.1 2 1 2 FL04 5.6 2 1 1 FL04 4.8 2 1 1 FL05 6.1 2 1 2 FL04 6.6 2 1 1 FL04 5.8 2 1 1 FL05 6.1 2 1 2 FL04 5.3 2 1 1 FL04 6.1 2 1 1 FL05 6.6 2 1 2 FL04 4.6 2 1 1 FL05 6.1 2 1 2 FL05 5.6 2 1 2 FL04 4.6 2 1 1 FL05 6.6 2 1 2 FL05 5.1 2 1 2 FL04 3.2 2 1 1 FL05 5.3 2 1 2 FL05 6.1 2 1 2 FL04 5.6 2 1 1 FL05 5.1 2 1 2 FL05 3.2 2 1 2 FL04 6.6 2 1 1 FL05 5.6 2 1 2 FL05 5.1 2 1 2

FES-ORG FL04 6.3 1 1 10 FL05 5.8 1 2 10 FL05 3.2 1 2 10 FL04 3.7 1 1 10 FL05 4.8 1 2 10 FL05 5.3 1 2 10 FL04 6.3 1 1 10 FL05 5.8 1 2 10 FL05 5.3 1 2 10 FL04 3.2 1 1 10 FL05 6.9 1 2 10 FL05 5.8 1 2 10 FL04 5.3 1 1 10 FL05 5.3 1 2 10 FL05 4.2 1 2 10 FL04 4.8 1 1 10 FL05 4.2 1 2 10 FL05 4.8 1 2 10 FL04 4.2 1 1 10 FL05 6.3 1 2 10 FL05 5.8 1 2 10 FL04 5.9 1 1 10 FL05 3.2 1 2 10 FL05 3.7 1 2 10 FL04 4.3 1 1 10 FL05 5.3 1 2 10 FL05 2.1 1 2 10 FL04 4.3 1 1 10 FL05 6.9 1 2 10 FL05 2.1 1 2 10 1 See footnote at the end of the Table

129

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-ORG

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL05 5.8 1 2 10 FL05 5.8 1 2 10 FL04 5.3 2 1 1FL05 4.2 1 2 10 FL05 5.3 1 2 10 FL05 5.8 2 1 2FL05 4.8 1 2 10 FL05 4.8 1 2 10 FL05 3.2 2 1 2FL05 5.8 1 2 10 FL05 5.3 1 2 10 FL05 4.2 2 1 2FL05 6.3 1 2 10 FL04 5.8 2 1 1 FL05 4.8 2 1 2FL05 5.8 1 2 10 FL04 4.8 2 1 1 FL05 5.8 2 1 2FL05 4.8 1 2 10 FL04 4.2 2 1 1 FL05 4.8 2 1 2FL05 4.2 1 2 10 FL04 4.8 2 1 1 FL05 3.2 2 1 2FL05 5.8 1 2 10 FL04 5.8 2 1 1 FL05 3.7 2 1 2FL05 6.3 1 2 10 FL04 3.7 2 1 1 FL05 5.8 2 1 2FL05 3.2 1 2 10 FL04 3.7 2 1 1 FL05 4.2 2 1 2FL05 5.3 1 2 10 FL04 3.7 2 1 1 FL05 5.3 2 1 2FL05 4.2 1 2 10 FL04 6.3 2 1 1 FL05 5.8 2 1 2FL05 5.3 1 2 10 FL04 4.8 2 1 1 FL05 6.9 2 1 2FL05 5.3 1 2 10 FL04 5.3 2 1 1 FL05 5.3 2 1 2FL05 3.2 1 2 10 FL04 4.8 2 1 1 FL05 3.2 2 1 2FL05 5.8 1 2 10 FL04 4.2 2 1 1 FL05 4.0 2 1 2FL05 4.8 1 2 10 FL04 5.3 2 1 1 FL05 5.3 2 1 2FL05 6.3 1 2 10 FL04 6.5 2 1 1 FL05 6.9 2 1 2FL05 5.3 1 2 10 FL04 3.8 2 1 1 FL05 3.2 2 1 2FL05 5.3 1 2 10 FL04 4.3 2 1 1 FL05 5.3 2 1 2FL05 5.8 1 2 10 FL04 5.9 2 1 1 FL05 6.9 2 1 2FL05 5.8 1 2 10 FL04 7.6 2 1 1 FL05 3.2 2 1 2FL05 6.3 1 2 10 FL04 7.0 2 1 1 FL05 5.8 2 1 2FL05 5.3 1 2 10 FL04 7.6 2 1 1 FL05 5.3 2 1 2FL05 5.3 1 2 10 FL04 4.3 2 1 1 FL05 4.8 1 2 10 FL04 4.3 2 1 1 1 See footnote at the end of the Table

130

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-CONT

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL04 7.0 1 1 10 FL05 3.8 1 2 10 FL05 6.2 1 2 10 FL04 6.5 1 1 10 FL05 4.9 1 2 10 FL05 4.9 1 2 10 FL04 6.5 1 1 10 FL05 5.1 1 2 10 FL04 5.4 2 1 1 FL04 7.6 1 1 10 FL05 4.3 1 2 10 FL04 5.9 2 1 1 FL04 4.9 1 1 10 FL05 4.3 1 2 10 FL04 4.9 2 1 1 FL04 7.0 1 1 10 FL05 5.4 1 2 10 FL04 5.4 2 1 1 FL04 3.2 1 1 10 FL05 6.5 1 2 10 FL04 7.0 2 1 1 FL05 7.6 1 2 10 FL05 5.4 1 2 10 FL04 5.9 2 1 1 FL05 7.0 1 2 10 FL05 6.5 1 2 10 FL04 7.0 2 1 1 FL05 4.3 1 2 10 FL05 6.5 1 2 10 FL04 5.4 2 1 1 FL05 4.9 1 2 10 FL05 5.9 1 2 10 FL04 6.5 2 1 1 FL05 5.9 1 2 10 FL05 3.8 1 2 10 FL04 6.5 2 1 1 FL05 5.9 1 2 10 FL05 4.9 1 2 10 FL04 6.5 2 1 1 FL05 5.4 1 2 10 FL05 3.8 1 2 10 FL04 5.4 2 1 1 FL05 5.9 1 2 10 FL05 7.0 1 2 10 FL04 7.0 2 1 1 FL05 5.4 1 2 10 FL05 5.4 1 2 10 FL04 7.0 2 1 1 FL05 5.9 1 2 10 FL05 5.9 1 2 10 FL04 7.0 2 1 1 FL05 5.4 1 2 10 FL05 5.1 1 2 10 FL05 5.4 2 1 2 FL05 6.5 1 2 10 FL05 4.9 1 2 10 FL05 4.9 2 1 2 FL05 4.9 1 2 10 FL05 5.4 1 2 10 FL05 4.9 2 1 2 FL05 7.0 1 2 10 FL05 6.5 1 2 10 FL05 5.4 2 1 2 FL05 5.9 1 2 10 FL05 3.8 1 2 10 FL05 7.0 2 1 2 FL05 5.1 1 2 10 FL05 5.7 1 2 10 FL05 5.4 2 1 2 FL05 5.9 1 2 10 FL05 7.0 1 2 10 FL05 6.5 2 1 2 FL05 5.9 1 2 10 FL05 5.4 1 2 10 FL05 5.4 2 1 2 1 See footnote at the end of the Table

131

Table 17e. Input Data for FES for Analysis of Variance for Hypothesis 4 (Cont.)1

FES-CONT

Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No Sm Scr Sm Ct Rp Sm

No FL05 5.4 1 2 10 FL05 6.5 1 2 10 FL05 5.4 2 1 2 FL05 6.5 1 2 10 FL05 7.0 1 2 10 FL05 7.0 2 1 2 FL05 6.5 1 2 10 FL05 5.9 1 2 10 FL05 5.4 2 1 2 FL05 6.8 1 2 10 FL05 7.0 1 2 10 FL05 4.9 2 1 2 FL05 7.6 1 2 10 FL05 5.4 1 2 10 FL05 7.0 2 1 2 FL05 6.5 1 2 10 FL05 6.5 1 2 10 FL05 7.0 2 1 2 FL05 5.9 1 2 10 FL05 7.6 1 2 10 FL05 7.0 2 1 2 FL05 7.6 1 2 10 FL05 7.6 1 2 10 1 Data are organized in 5 columns with column headings of Semester (Sm) Score (Scr), the semester count (Sm Ct), Replication (Rp), and semester Number (Sm No). The semester count indicates the maximum number of semesters X the student participated in, while Semester number indicates which of these (1, 2, ..,X) the score belongs to. The latter is used to organize the data in X-groups (Fig. 1.1 Data Structure for ANOVA for Hypothesis IV.

132

Table 18. Summary Shapiro-Wilk Normality Test for Data Used for Testing Hypothesis 41

Variable N W P-value Variable N W P-value Variable N W P-valueX-Group 1 X-Group 2 X-Group 3

Hope 26 0.9373 0.1157 Hope 26 0.9769 0.8035 Hope 13 0.8742 0.0595 MSCS 34 0.9654 0.3477 MSCS 34 0.9794 0.7531 MSCS 20 0.9677 0.7056 SES 51 0.9558 0.0552 SES 51 0.9338 0.007 SES 36 0.9632 0.2686 SWLS 33 0.9238 0.0233 SWLS 33 0.9134 0.0121 SWLS 7 0.8935 0.2936

COH 18 0.9507 0.436 COH 18 0.8736 0.0204 X-Group 4 EXP 18 0.9394 0.2833 EXP 18 0.9545 0.4994 Hope 8 0.9415 0.6262CONF 18 0.9621 0.6429 CONF 18 0.9225 0.1431 MSCS 6 0.7564 0.023IND 18 0.95 0.4245 IND 18 0.8921 0.042 SES 16 0.9222 0.1827

AO 18 0.9326 0.2155 AO 18 0.9517 0.452 X-Group 5 ICO 18 0.9271 0.1727 ICO 18 0.9387 0.2757 Hope 3 1 1ARO 24 0.9152 0.0459 ARO 24 0.9572 0.3849 MSCS 1 M M MRE 22 0.8932 0.0218 MRE 22 0.8469 0.003 SES 12 0.793 0.0078ORG 24 0.9195 0.0569 ORG 24 0.9118 0.0386 SES 11 0.9169 0.2938

CONT 15 0.8597 0.0239 CONT 15 0.7781 0.002 X-Group 6 SES 6 0.8616 0.19481 See Fig. Hypo4-1 about designation of X groups

133

Table 19. ANOVA Output Summary, Hypothesis 4, With Respect to Type III Sum of Squares for Semester Number as Source of Variation1

Measure DF Type III SS Mean Square F Value Pr > F MSCS-Total 5 1748.031 349.606 1.24 0.2944

Hope-Total 5 25.595 5.119 0.55 0.7396

SES 6 35.200 5.867 0.43 0.8603

SWLS 3 24.074 8.025 0.20 0.8982

FES-COH 2 2.055 1.027 0.56 0.5704

FES-EXP 2 3.747 1.873 1.24 0.2947

FES-CONF 2 9.518 4.759 3.19 0.0455

FES-AO 2 2.125 1.063 1.02 0.3658

FES-ICO 2 1.444 0.722 0.65 0.5221

FES-ARO 2 1.192 0.596 0.56 0.5756

FES-MRE 2 8.163 4.081 4.16 0.0184

FES-ORG 2 0.726 0.363 0.28 0.7565

FES-CONT 2 1.357 0.679 0.68 0.5096

FES-IND 2 1.349 0.674 0.39 0.6784

1The distribution of the dataset for ANOVA are shown as boxplots for each measure In Fig. Hypo4-2 to Fig. Hypo4-6

134

Table 20. Means and Standard Deviations for MSCS Normative Data1

Full MSCS Subscale Name Acronym Mean: N=2501 SD

Social SOC 75.34 11.22

Competence COMP 75.31 9.87

Affect AFF 74.09 12.93

Academic ACA 73.94 11.37

Family FAM 84.45 14.54

Physical PHY 73.57 12.09

Total TOT 457.35 56.79 1Table 2.4 of Chapter 2 (Bracken, 1992)

Total subscale scores are row scores

135

Table 21. Results of One-Sample T Test Comparing Normative Data for MSCS Measure with Scores for Spring 2003, Fall 2003, Fall 2004, Spring 2005, Fall 2005 and Scores Pooled Across These Semesters1

MSCS 95% Confidence

Interval

Measure HO: MU = Mean SE Lower Upper T DF P-valueSpring 2003 Social 75.34 104.00 3.72 96.39 111.61 7.71 28 <0.0001 Competence 75.31 98.86 3.35 92.00 105.73 7.03 28 <0.0001 Affect 74.09 101.41 3.70 93.83 109.00 7.38 28 <0.0001 Academic 73.94 102.03 3.13 95.62 108.45 8.98 28 <0.0001 Family 84.45 101.07 3.22 94.47 107.66 5.16 28 <0.0001 Physical 73.57 104.86 3.02 98.69 111.04 10.38 28 <0.0001 Total 457.35 463.17 11.45 439.72 486.63 0.51 28 0.3075 Fall 2003 Social 75.34 105.24 2.35 100.52 109.95 12.75 50 <0.0001 Competence 75.31 102.92 2.30 98.30 107.54 12.00 50 <0.0001 Affect 74.09 104.71 2.46 99.77 109.64 12.47 50 <0.0001 Academic 73.94 104.51 2.10 100.29 108.73 14.56 50 <0.0001 Family 84.45 98.55 2.24 94.05 103.05 6.29 50 <0.0001 Physical 73.57 104.55 2.39 99.76 109.34 12.99 50 <0.0001 Total 457.35 470.38 8.74 452.83 487.93 1.49 50 0.0711 Fall 2004 Social 75.34 104.92 2.23 100.44 109.39 13.29 48 <0.0001 Competence 75.31 102.37 2.16 98.02 106.71 12.53 48 <0.0001 Affect 74.09 104.43 2.17 100.07 108.79 13.98 48 <0.0001 Academic 73.94 102.63 1.94 98.73 106.54 14.78 48 <0.0001 Family 84.45 97.88 2.23 93.39 102.36 6.02 48 <0.0001 Physical 73.57 105.10 2.19 100.70 109.50 14.42 48 <0.0001 Total 457.35 468.85 7.45 453.87 483.83 1.54 48 0.0647 1 MU is the normative mean for a measure, while Mean, SE (Standard Error) and 95% confidence interval refer to scores observed during this study

136

Table 21. Results of One-Sample T Test Comparing Normative Data for MSCS Measure with Scores for Spring 2003, Fall 2003, Fall 2004, Spring 2005, Fall 2005 and Scores Pooled Across These Semesters (Cont.)1

Measure HO: MU = Mean SE Lower Upper T DF P-valueSpring 2005 Social 75.34 102.27 4.49 92.28 112.27 6.00 10 0.0001 Competence 75.31 99.27 4.56 89.11 109.44 5.25 10 0.0002 Affect 74.09 114.31 3.59 107.02 121.59 11.21 35 <0.0001 Academic 73.94 115.83 3.96 107.80 123.87 10.59 35 <0.0001 Family 84.45 117.53 4.69 108.01 127.05 7.06 35 <0.0001 Physical 73.57 101.19 4.36 92.10 110.28 6.34 20 <0.0001 Total 457.35 461.21 14.09 431.82 490.61 0.27 20 0.3934 Fall 2005 Social 75.34 101.27 2.58 96.12 106.42 10.04 70 <0.0001 Competence 75.31 99.83 2.13 95.58 104.08 11.50 70 <0.0001 Affect 74.09 102.21 2.11 98.01 106.42 13.34 70 <0.0001 Academic 73.94 99.93 2.00 95.94 103.92 12.99 71 <0.0001 Family 84.45 97.01 2.06 92.90 101.13 6.09 71 <0.0001 Physical 73.57 103.46 2.28 98.92 108.01 13.11 70 <0.0001 Total 457.35 455.98 8.59 438.84 473.12 -0.16 67 0.5632 Pooled Social 75.34 103.50 1.28 100.97 106.03 21.96 210 <0.0001 Competence 75.31 101.00 1.15 98.73 103.28 22.27 210 <0.0001 Affect 74.09 104.96 1.20 102.59 107.32 25.71 235 <0.0001 Academic 73.94 104.15 1.16 101.87 106.42 26.14 236 <0.0001 Family 84.45 101.14 1.30 98.58 103.69 12.86 236 <0.0001 Physical 73.57 104.05 1.18 101.73 106.36 25.92 220 <0.0001 Total 457.35 463.70 4.26 455.30 472.10 1.49 217 0.0687 1 MU is the normative mean for a measure, while Mean, SE (Standard Error) and 95% confidence interval refer to scores observed during this study

137

Table 22. Means and Standard Deviations for FES Normative Data for Normal and Stressed Individuals1

Full FES

Normal IndividualsN=5331

Distressed IndividualsN=1515

Subscale Name Acronym Mean SD Mean SD

Cohesion COH 6.80 2.26 5.45 2.67

Expressiveness EXP 5.27 2.14 4.55 1.98

Conflict CONF 3.54 2.24 4.36 2.58

Independence IND 6.35 1.61 5.84 1.81

Achievement Orientation AO 5.90 1.65 6.07 1.78

Intellectual-Cultural Orientation ICO 5.60 2.32 4.49 2.35

Active-Recreational Orientation ARO 6.09 1.94 4.73 2.30

Moral-Religious Emphasis MRE 5.57 2.15 5.09 2.19

Organization ORG 5.67 2.17 5.41 2.29

Control CONT 4.72 2.10 4.96 2.14 1Moos, 2002, p. 111, Appendix C, Table C1

Table 23. Results of One-Sample T Test Comparing Normative Data for FES Measure for Normal and Distressed Individuals With Scores from Fall 2004, Fall 2005 and Scores Pooled Across These Semesters1

H0:mu= Descriptive Statistics T P-Value

Measure Normal Distressed Mean SE Lower Upper Normal Distressed DF Normal Distressed

Fall 2004 COH 6.80 5.45 4.23 0.207 3.811 4.654 -12.38 -5.87 33 0.0000 0.0000 EXP 5.27 4.55 4.44 0.251 3.934 4.955 -3.29 -0.42 33 0.0024 0.6758 CONF 3.54 4.36 4.99 0.236 4.505 5.466 6.12 2.65 33 0.0000 0.0123 IND 6.35 5.84 4.97 0.200 4.561 5.374 -6.92 -4.37 33 0.0000 0.0010 AO 5.90 6.07 5.31 0.195 4.919 5.711 -3.01 -3.88 33 0.0050 0.0005 ICO 5.60 4.49 5.01 0.169 4.661 5.350 -3.51 3.05 33 0.0013 0.0045 ARO 6.09 4.73 5.33 0.156 5.015 5.650 -4.85 3.86 33 0.0000 0.0005 MRE 5.57 5.09 5.18 0.214 4.747 5.618 -1.81 0.43 33 0.0794 0.6691 ORG 5.67 5.41 5.06 0.198 4.659 5.465 -3.07 -1.76 33 0.0042 0.0879 CONT 4.72 4.96 6.16 0.218 5.706 6.613 6.60 5.50 21 0.0000 0.0000 Fall 2005 COH 6.80 5.45 4.75 0.169 4.411 5.086 -12.14 -4.15 65 0.0000 0.0001 EXP 5.27 4.55 4.27 0.137 3.998 4.544 -7.30 -2.04 65 0.0000 0.0456 CONF 3.54 4.36 5.46 0.141 5.174 5.738 13.56 7.76 65 0.0000 0.0000 IND 6.35 5.84 4.75 0.169 4.414 5.086 -9.50 -6.47 65 0.0000 0.0000 AO 5.90 6.07 5.72 0.115 5.489 5.950 -1.56 -3.04 65 0.1229 0.0017 ICO 5.60 4.49 5.09 0.134 4.820 5.356 -3.82 4.46 65 0.0003 0.0000 ARO 6.09 4.73 5.12 0.123 4.873 5.361 -7.93 3.15 76 0.0000 0.0023 MRE 5.57 5.09 5.73 0.095 5.536 5.914 1.64 6.71 75 0.1058 0.0000 ORG 5.67 5.41 5.03 0.129 4.774 5.286 -4.98 7.76 75 0.0000 0.0000 CONT 4.72 4.96 5.83 0.112 5.607 6.053 9.90 -2.95 76 0.0000 0.0042 1 See footnote at the end of the Table

138

Table 23. Results of One-Sample T Test Comparing Normative Data for FES Measure for Normal and Distressed Individuals With Scores from Fall 2004, Fall 2005 and Scores Pooled Across These Semesters (Cont.) 1

H0:mu= Descriptive Statistics T P-Value

Measure Normal Distressed Mean SE Lower Upper Normal Distressed DF Normal Distressed

Pooled COH 6.80 5.45 4.57 0.134 4.308 4.838 -16.67 -6.56 99 0.0000 0.0000 EXP 5.27 4.55 4.33 0.124 4.085 4.576 -7.60 -1.78 99 0.0000 0.0785 CONF 3.54 4.36 5.30 0.124 5.049 5.543 14.12 7.52 99 0.0000 0.0000 IND 6.35 5.84 4.82 0.130 4.566 5.082 -11.73 -7.81 99 0.0000 0.0000 AO 5.90 6.07 5.58 0.102 5.379 5.785 -3.11 -4.78 99 0.0024 0.0000 ICO 5.60 4.49 5.06 0.105 4.851 5.269 -5.13 5.42 99 0.0000 0.0000 ARO 6.09 4.73 5.18 0.098 4.989 5.377 -9.28 4.63 110 0.0000 0.0000 MRE 5.57 5.09 5.56 0.096 5.368 5.747 -0.13 4.89 109 0.8943 0.0000 ORG 5.67 5.41 5.04 0.107 4.827 5.253 -5.87 -3.45 109 0.0000 0.0008 CONT 4.72 4.96 5.90 0.100 5.704 6.102 11.81 9.41 98 0.0000 0.0000 1 COH – Cohesion; EXP-Expressiveness; CONF-Conflict; IND-Independence; AO-Achievement Orientation. ICO-Intellectual-Recreational Orientation; ARO-Active-Religious Emphasis; MRE-Moral Religious Emphasis; ORG-Organization; CONT-Control. Values in columns headed Ho:mu= represent means of normative data. SE–standard error; T-t statistics; DF-degree of freedom. Null hypothesis that means are equal is rejected when P-value < 0.05. Hence null hypothesis is accepted for measures with cells shaded for p-values.

139

Fig. 1.1. Data Structure for Each Measure for Analysis of Variance of Hypothesis IV. The Y-Group indicates groups with single or multiple semester scores. Numbers inside boxes indicate semester numbers. The X-Grouping is a mechanism to pool the scores of each semester separately.

Appendix A

140

Group A

Group B

Group C

Group D

Group E

Group F

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6Y-Group

X-Group

Data Structure for Each Measure for ANOVA, Hypothesis IV

1

1 2

1 2 3

1

1

1

2

2

2

3

3

3

4

54

4 5 6

Control

Fig. 1.2. Distribution of Hope-Total and MSCE-Total Scores by Semester Number for Analysis for Hypothesis IV. Dashed and solidlines in boxes are means and medians, respectively.

Appendix A

141

Semester Number

Hop

e-To

tal S

core

15

20

25

30

35

1 1 2 3 4 5

N = 66 26 26 13 8 3

(Control)

Semester Number

MSC

S-To

tal S

core

40

60

80

100

120

140

160

(Control)1 1 2 3 4

6203434N = 68

Fig. 1.3. Distribution of SES and SWLS Scores by Semester Number for analysis of Hypothesis IV. Dashed and solid lines in boxes are means and medians, respectively.

Appendix A

142

Semester Number

SES

Scor

e

10

15

20

25

30

35

40

1 1 2 3 4 5(Control)

N = 74 51 51 36 16 12

Semester Number

SWLS

Sco

re

5

10

15

20

25

30

35

40

1 1 2 3N = 67 33 33 7

(Control)

Fig. 1.4. Distribution of COH, EXP, CONF, and IND Scores by Semester Number for Analysis of Hypothesis IV. Dashed and solid lines in boxes are means and medians, respectively.

Appendix A

143

FES Measure and Semester Number

COH

and

EX

P Sc

ore

0

2

4

6

8

10

COH COH COH EXP EXP EXP1 1 2 1 1 2

(Control) (Control)

N = 63 6318 1818 18

FES Measure and Semester Number

CO

NF

and

IND

Sco

re

0

2

4

6

8

10

CONF CONF CONF IND IND IND1 1 2 1 1 2

(Control) (Control)

N = 63 6318 1818 18

Fig. 1.5. Distribution of AO, ICO, ARO, and MRE Scores by Semester Number for Analysis of Hypothesis IV. Dashed and solid lines in boxes are means and medians, respectively.

Appendix A

144

FES Measure and Semester Number

AO

and

ICO

Sco

re

0

2

4

6

8

10

AO AO AO ICO ICO ICO1 1 2 1 1 2

(Control) (Control)

N = 63 6318 1818 18

FES Measure and Semester Number

AR

O a

nd M

RE S

core

0

2

4

6

8

10

ARO ARO ARO MRE MRE MRE1 1 2 1 1 2

(Control) (Control)

N = 62 6124 2224 22

Fig. 1.6. Distribution of ORG and CONT Scores by Semester Number for Analysis of Hypothesis IV. Dashed and solid lines in boxes are means and medians, respectively.

Appendix A

145

FES Measure and Semester Number

OR

G a

nd C

ON

T Sc

ore

0

2

4

6

8

10

ORG ORG ORG CONT CONT CONT1 1 2 1 1 2

(Control) (Control)

N = 61 6824 1524 15

Fig. 2.1. Means with 95% Confidence Interval for MSCS Assessment Measure Scores for Normative (n=2501) and Semester data, and forData Pooled Across Semesters.

Appendix B

146

MSC

S-To

tal R

ow S

core

420

430

440

450

460

470

480

490

500

Normal Spring 2003

Fall2003

Fall2004

Spring 2005

Fall2005

Pooled

Data Source

N = 2501 29 51 49 21 68 218

Fig. 2.2. Means with 95% Confidence Interval for Normative FES Assessment Measure Scores for Normal (n=5331) and Distressed (n=1515) Individuals.

Appendix B

147

FES Assessment Measure

FES

Ass

essm

ent M

easu

re S

core

3

4

5

6

7

8

Normal IndividualsStressed Individuals

COH EXP CONF IND AO ICO ARO MRE ORG CONT

Fig. 2.3. Means with 95% Confidence Interval Comparing FES Assessment Measure Scores for Fall 2004 and Fall 2005 with Normative Data for Normal Individuals.

Appendix B

148

FES Assessment Measure

3

4

5

6

7

Normal IndividualFall 2005

FES

Ass

essm

ent M

easu

re S

core

3

4

5

6

7

Normal IndividualFall 2004

COH EXPCONF IND AO ICO ARO MREORG CONT

COH EXPCONF IND AO ICOARO MRE ORG CONT

Fig. 2.4. Means with 95% Confidence Interval Comparing FES Assessment Measure Scores for Fall 2004 and Fall 2005 with Normative Data for Distressed Individuals.

Appendix B

149

FES Assessment Measure

3

4

5

6

7

Distressed IndividualFall 2005

FES

Ass

essm

ent M

easu

re S

core

3

4

5

6

7

Distressed IndividualFall 2004

COH EXPCONF IND AO ICO AROMRE ORG CONT

COH EXP CONFIND AO ICO ARO MRE ORG CONT