Chapter 16: Analysing Survey Data

1. Chapter 16: Analysing Survey Data

2. Contents • Survey data analysis and types of research • Spreadsheet analysis • Statistical package for the social sciences (SPSS) • Preparation • SPSS procedures • The analysis process

3. Figure 16.1 Survey data analysis and types of research

4. 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)

5. Example using data from Campus Life questionnaire (Figure 10.21) • FREQUENCY procedure in Microsoft Excel used to produce: • frequency counts of coded variables • averages for numerical variables (age, spend) Figure 16.2 Spreadsheet analysis

6. Statistical package for the social sciences (SPSS) • Software package produced by SPSS Inc., owned by IBM • Can be used to analyse 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

7. 1. Preparation 2. Frequencies 3. Descriptives 4. Multiple 5. Recode 6. Means response 9. Statistics 7. Weighting 8. Crosstabs - see Chapter 17 10. Graphics SPSS procedures covered Figure 16.4 Survey analysis – overview

8. Preparation: cases and variables: fromFigure 10.21

9. 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

10. 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.

11. Types of measure • Nominal: Described in words – e.g. male/female • Ordinal: Ranked: 1, 2, 3… means 1st, 2nd, 3rd… • Scale: Fully numeric

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

13. Variable View screen

14. Data View • Data entered directly on the Data View screen, or • Can be imported from a spreadsheet file

15. Data View screen

16. 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 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

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

18. Figure 16.11 Descriptives: output: first few variables

19. 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 Figure 16.6

20. Frequencies for all variables: see Appendix 16.1 Figure 16.12 Frequencies: output

21. 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

22. Figure 16.13 Multiple response output

23. Recode • Grouping/Re-grouping variable categories, especially: • presentational: numerical variables • theoretical e.g. 5 categories of tourism or just two: leisure vs non-leisure? • Comparison – with other research • statistical reasons – see Chapter 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

24. Figure 16.14 Recode: output

25. 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’

26. Mean, median, mode: using ‘frequencies’ procedure

27. Means procedure Mean expenditure by student status

28. 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 Chapter 17 • Procedure: ‘Crosstabs’

29. Crosstabs

30. Crosstabs (Continued): three variables

31. Weighting • Weighting discussed in Chapter 13 • ‘Weight cases’ procedure • e.g. 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

32. Graphics • Types: • bar graph • stacked bar graph • pie chart • line graph • scatter plot • Different graph types suited to different data types

33. – * Grouped Figure 16.18 Data types and graphics

34. Figure 16.19Bar chart

35. Stacked bar chart

36. Pie chart

37. Line graph

38. Scatterplot