c hapter 16: analysing survey data

Chapter 16: Analysing

survey data

CONTENTS

• Survey data analysis and types of research• Spreadsheet analysis• Statistical Package for the Social Sciences (SPSS) • Preparation• SPSS procedures• The analysis process

Survey data analysis and types of research (Fig. 16.1)

Research type SPSS proceduresDescriptive Frequencies, Means

Explanatory Crosstabulation, Comparison of means, regression

Evaluative Frequencies – compared with targets or benchmarksCrosstabulations – comparing user/customer-groupsMeans – compared with some benchmark or target

A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge

Explanatory research and causality

• Necessary conditions:• Associations between variables (A changes with B)• Time priority (B happens after A)• Non-spurious relationships (relationships ‘make sense’)• Rationale/theory (there should be an explanation)


Spreadsheet analysis (Fig. 16.1)

• Example using data from Campus Sporting Life questionnaire (Fig. 10.21)

• FREQUENCY procedure in Microsoft Excel used to produce:– frequency counts of coded variables– averages for numerical variables (age, spend)


Statistical Package for the Social Sciences (SPSS)

• Software package produced by SPSS inc., owned by IBM

• Analysis of questionnaire-based and other data– organised as cases with specified variables

• SPSS is effective and one of the most popular packages. Its use in this book does not imply endorsement as ‘the best’ package.


SPSS procedures covered (Fig. 16.4)

3. DESCRIPTIVES

4. MULTIPLE RESPONSE

5. RECODE

7. WEIGHTING

6. MEANS

8. CROSSTABS 9. STATISTICS - see Chapter 17

10. GRAPHICS

1. PREPARATION

2. FREQUENCIES


Preparation: cases and variables: from Fig. 10.21

VARIABLES

qno status cafebar music sport travel cheap Etc

CASES

1 2 1 1 0 0 12 2 1 1 1 0 13 3 1 0 0 0 24 4 0 0 0 0 2

5 3 1 0 0 1 1

6 3 1 1 1 0 27 2 1 0 0 0 38 2 1 0 1 0 3

Etc


Information required for each variable in the questionnaire

• Name• Type – numeric, string (letters) or date• Width – max. no. of characters• Decimal places• Label – longer version of name• Values – for coded variables• Missing – blanks, no answer, etc. • Columns – no. of columns in Data View screen (see below)• Alignment – left, right, centre (in Data View)• Measure/data type – nominal, ordinal, scale


Variable names

• Up to 8 characters (no spaces), beginning with a letter

• Not allowed: ALL AND BY EQ GT LE LT NE NOT OR TO WITH

• Can be:– Short version of item description (as used here), or– Var01, var02, var03 etc. or– Q1a, Q1b, Q2, Q3 etc


Types of measure

• Nominal: described in words – eg. male/female• Ordinal: Ranked: 1, 2, 3 … means 1st, 2nd, 3rd ….• Scale: fully numeric


Variable View

• Information on variables is entered in the SPSS ‘Variable View’ screen


Variable view screen (Fig. 16.8)

Data View

• Data entered directly on the Data View screen, or

• Can be imported from a spreadsheet file


Data View screen (Fig. 16.9)

Note to teachers• It is not envisaged that SPSS detailed procedures

would be the subject of a PowerPoint presentation: students would benefit most from following the procedures in practical sessions

• A copy of the Campus Sporting Life data files is available on the book website

• However, teachers may wish to discuss the nature/ purpose of the various procedures.

• Slides are therefore included with the outputs from the procedures


Descriptives: N, Minimum, Maximum, Mean & Standard Deviation for each variable.


Descriptives: output: first few variables (Fig. 16.11)

N Min. Max. MeanStd.

DeviationStudent status 15 1 4 2.53 .915Campus pool in last 4 wks 15 0 1 .87 .352Campus gym in last 4 wks 15 0 1 .53 .516Campus squash in last 4 wks 15 0 1 .33 .488Spectators in last 4 wks 15 0 1 .13 .352Free/cheap (rank) 15 1 3 1.80 .775Daytime events (rank) 15 2 5 3.73 .961Not available elsewhere (rank) 15 1 3 1.60 .737Socialising (rank) 15 1 5 3.20 1.082Quality of presentation (rank) 15 4 5 4.67 .488Entertainment exp./month 15 25 300 115.00 87.076Relaxation opportunities – imp. 15 1 3 2.20 .676Etc.


Frequencies

• Simple counts/percentages of variables• Nominal/ordinal: straightforward• Numeric may need to be grouped – see Recode• Frequencies form the basis for a statistical

summary/appendix – see Fig. 16.6


Frequencies: output (Fig. 16.12)

Student status Frequency PercentValid

PercentCumulative

PercentValid F/T student/no paid work 2 13.3 13.3 13.3

F/T student/paid work 5 33.3 33.3 46.7P/T student - F/T job 6 40.0 40.0 86.7P/T student/Other 2 13.3 13.3 100.0Total 15 100.0 100.0

Frequencies for all variables: see Appendix 16.1


Multiple Response

• Two types of ‘Multiple Response’• Dichotomy: Q. 2: use of services: 4 ‘yes/no’ variables– Best combined into one table

• Category: Q. 6: Suggestions: up to three responses per respondent = 3 variables– Best combined into one table


Multiple Response output: Fig. 16.13

Dichotomy label Name Count Pct of Responses

Pct ofCases

Campus pool in last 4 wks pool 13 46.4 92.9Campus gym in last 4 wks gym 8 28.6 57.1Campus squash in last 4 wks squash 5 17.9 35.7Spectate, campus in last 4 wks spectate 2 7.1 14.3Total responses 28 100.0 200.0

Category label Code Count

Pct of Responses

Pct of Cases

Programme content 1 7 31.8 58.3Timing 2 6 27.3 50.0Facilities 3 3 13.6 25.0Costs 4 4 18.2 33.3Organisation 5 2 9.1 16.7 Total responses 22 100.0 183.3


Recode• Grouping/Re-grouping variable categories, especially:

– presentational: numerical variables – theoretical eg. 5 categories of tourism or just two: leisure vs

non-leisure?– Comparison – with other research– statistical reasons – see Ch. 17

• Examples:– uncoded, ‘spend’ has 9 different answers (see Appendix

16.1): recode into 4 groups– Student status has 2 F/T and 2 P/T categories: recode into F/T

and P/T


Recode: output (Fig. 16.14)

Spend recoded Frequency Percent Valid Percent Cumulative Percent

£ 0-50 4 26.7 26.7 26.7 £ 51-100 6 40.0 40.0 66.7 £ 101-200 2 13.3 13.3 80.0 £ 201+ 3 20.0 20.0 100.0 Total 15 100.0 100.0

Status recoded Frequency Percent Valid Percent Cumulative Percent

Full-time student 7 46.7 46.7 46.7 Part-time student 8 53.3 53.3 100.0 Total 15 100.0 100.0


Measures of central tendency: Mean, Median, Mode

• Mean = average • Median = middle value when all cases ranked in order• Mode = most popular value• Only valid with scale and ordinal variables• Options:– Add to ‘Frequencies’ procedure– Use ‘Means’


Mean, median, mode: ‘frequencies’ procedure (Fig. 16.15)

Additional output from ‘Frequencies’

Relaxation opportunities -

importance

Social interaction - importance

Fitness - importance

N Valid 15 15 15Missing 0 0 0

Mean 2.20 2.67 1.47Median 2.0 3.0 1.0Mode 2 3 1


Means procedure (Fig. 16.15)

Student status Mean N Std. Deviation*F/T student/no paid work 102.50 2 67.175F/T student/paid work 120.00 5 83.666P/T student - F/T job 99.17 6 76.643P/T student/Other 162.50 2 194.454Total 115.00 15 87.076

Mean expenditure by student status


Crosstabulation

• Table showing relationships between two or more variables

• Table can include one or more of the following:– counts– row %– column %– total % – statistical tests – see Ch. 17

• Procedure: ‘Crosstabs’


Crosstabs

Student status by attended live campus music: counts only

Live campus music in last 4 wks

TotalNo YesStudent status

F/T student/no paid work 1 1 2F/T student/paid work 3 2 5P/T student - F/T job 2 4 6P/T student/Other 1 1 2

Total 7 8 15

Student status by attended live campus music: row percentages

Live campus music in last 4 wks

TotalNo YesStudent status

F/T student/no paid work 50.0% 50.0% 100.0%F/T student/paid work 60.0% 40.0% 100.0%P/T student - F/T job 33.3% 66.7% 100.0%P/T student/Other 50.0% 50.0% 100.0%

Total 46.7% 53.3% 100.0%A. J. Veal & S. Darcy (2014) Research Methods for Sport Studies and Sport Management: A practical guide. London: Routledge

Crosstabs contd: three variables

Gender Live campus music/4 wks

TotalNo YesMale Student

statusF/T student/no paid wk 1 1 2P/T student - F/T job 2 3 5P/T student/Other 0 1 1

Total 3 5 8 Female Student

statusF/T student/paid work 3 2 5P/T student - F/T job 0 1 1P/T student/Other 1 0 1

Total 4 3 7


Weighting

• Weighting discussed in Ch. 13• ‘Weight cases’ procedure• eg. if Masters students under-sampled:– suppose masters students need to be given a

weight of 1.3– create new variable wt– for Masters students wt = 1.3; all others: wt = 1– In ‘Weight cases’: weight by wt


Graphics

• Types:– bar graph– stacked bar graph– pie chart– line graph– scatter plot

• Different graph types suited to different data types


Data types and graphics (Fig. 16.18)

Data typeNominal Ordinal Scale

Data characteristics Qualitative categories

Ranks Numerical

Example questions in Fig. 10.20 1, 2, 6 3, 5 4Mean/average possible No Yes YesTypes of graphic Bar graph Yes Yes Yes* Pie chart Yes Yes Yes* Line graph No No Yes Scatter gram No No Yes

* Grouped


Bar chart


Stacked bar chart


Pie chart


Line graph


Scatterplot


c hapter 16: analysing survey data

Documents

sport studies

sport management

research methods

practical guide

routledge3explanatory

data view screen

routledge4spreadsheet

routledge5statistical