Download - PART III: DATA ANALYSIS
PART III:DATA ANALYSIS
Structure
10. Questionnaires
9. Qualitative
13. Sampling
11. Experimental
8. Observation
7. Secondary data
16. Survey data
15. Qualitative
18. Research report
14. Secondary
4. Research ethics
3. Starting out
6. Reviewing lit.
5. Range of methods
2. Approaches
1. Introduction
PART I PREPARATIONPART II DATA COLLECTION
12. Case studies
PART III ANALYSIS
PART IV COMMUICATE RESULTS
17. Statistical
Chapter 14:Analysing secondary
data
CONTENTS
• This chapter comprises 5 case studies dealing with:• 14.1. The Spirit Level and sport: International data on
income inequality and sport participation • 14.2. Estimating demand for a sports facility• 14.3. Facility utilisation• 14.4. Facility catchment or market area• 14.5. Olympic medals• 14.6. The colour red and sporting success
Children’s play safety (CS 11.6A)
• Secondary data: reports of accidents in school playgrounds in Toronto (collected routinely for insurance etc. purposes)
• 86 playgrounds deemed unsafe and provided with new equipment = treatment group
• 225 playgrounds deemed safe: no action taken = control group
• Injuries per 1000 students for 10 months before and after the replacement of equipment.
• Treatment group: Injury rates declined.• Control group: Injury rates actually increased.
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.1 The Spirit Level and sport
• The Spirit Level Wilkinson & Pickett (2009):– secondary data from UN etc. – countries with more equal income distribution
perform better on a wide range of human welfare measures
– sport not covered• Sport participation data explored here
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Income inequality & sport participation: Europe, 2009 (Fig. 14.1)
3.0 4.0 5.0 6.0 7.0 8.0 9.030
40
50
60
70
80
90
100
AUT
BEL
DEN
FIN FRAGER
GRE
IRE
ITA
NL
POR
SPA
SWE
UK
R² = 0.374816130600407
More equal Income distribution More unequal
% p
artic
ipati
ng in
spor
t
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.2 Estimating demand for a sports facility
C. Estimate total
demand from local population
A. Age-specific
participation rates (National Survey)
E. Capacity of
existing facilities
D. Typical facility capacity
H. No. of new facilities to cater for unmet
demand
F. Compare
G. Unmet demand
B. Population by age-
groups (Census)
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Estimating demand for a sports facility contd
C. Secondary data: National survey + CensusD. Estimated total demand: 4600 visits per weekD. Typical facility capacity: 500 visits/weekE. Capacity of 4 existing facilities: 2000 visits/weekF. Comparison: 4600 and 2000 visits/weekG. Unmet demand: 2600 visits/weekH. No. of new facilities to cater for unmet demand:
2600/500 = 5.2. Five courts.
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.3 Facility utilisation
• Secondary data: ticket/bookings data for different areas in a multi-purpose facility
• See Table 14.4 and Fig. 14.4
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Facility utilisation contd (Fig. 14.3)
Mon Tues Wed Thurs Fri Sat Sun0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Area AArea B%
Util
ised
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.4 Facility catchment area:
• Secondary data: customer address data – from bookings or membership records
• See Fig. 14.4
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Facility catchment area contd (Fig. 14.4)
2 km
4 km
Facility One member
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.5 Olympic medals
• Measuring countries’ success/rank order:• Gold medals won• Total medals, gold, silver and bronze, won• Points: for example: gold = 3 points, silver = 2 points, bronze = 1
point• Medals per million population• Points per million population• Medals per $billion GDP• Points per $billion GDP• Medals per $1000 GDP per head• Points per $1000 GDP per head
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Gold medal rank
United States 1China 2Great Britain 3Russia 4South Korea 5Germany 6France 7Italy 8Hungary 9Australia 10
London 2012 Olympic Games: top 10 gold medal winners (Table 14.6)
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
Medals Medals per million popn
Medals per $bn GDP
Medals per $000 GDP/
headGold Total Points Medals Points Medals Points Medals Points
United States 1 1 1 49 44 66 8 9 8China 2 2 2 75 69 67 2 2 2Great Britain 3 4 4 20 18 41 10 11 10Russia 4 3 3 32 31 36 5 5 5South Korea 5 9 9 33 30 47 20 26 20Germany 6 5 5 37 34 54 15 17 15France 7 8 6 38 33 50 22 23 22Italy 8 10 10 41 37 53 26 25 26Hungary 9 15 12 7 5 16 21 24 21Australia 10 7 8 8 9 35 30 31 30
Ranks on 9 measures of performance (Table 14.7)
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge
CS 14.6 The colour red and sporting success
• Hill and Barton (2005):• Red = ‘maleness’ in many animals• Anger associated with red• Analysis of 4 combat sports in the 2004 Olympics, where
contestants are randomly assigned red and blue outfits.• Contestants wearing red consistently won more fights.• Confirmed using English soccer league data from 1947-• Challenged by Rowe et al.• See also CS 11.6b
A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge