A Step by Step Guide for SPSS and Exercise Studies

150 Statistical tests
Nonparametric Tests/Chi-square
This test is employed to compare two or more categories of one or more variables.
For example, you may want to examine whether a sample of 100 pupils differ in
their choice of favourite football club. After carrying out a frequency count, you
find out that 32 pupils support club A, 26 support club B, 19 support club C, 14
support club D, and 9 support club E. In the data file you can create a variable
with 100 cases (rows) that will represent the club preference of each pupil.
Alternatively, you can create another variable (clubs) with five rows. Type in the
total number of preferences for each of the five clubs, and then use the weight
cases option of the Data menu to indicate that each row represents a total score
rather than an individual case (Figure 45).
Move the clubs variable into the Test variable list (Dialog box 98). Use
Options to ask for descriptive statistics and specify how to handle missing
values. If you want to restrict the comparison to, say, the first three clubs only
(A, B, and C), select use specified range under Expected Range and type 1 and 3
as the Lower and Upper values. Then click OK.
The output in Table 58 shows the observed number of preferences for each
club. If there were no significant differences in club preference, the expected
number of preferences for each club would have been 20. The chi-square test
examines the significance of the differences between the expected and the actual
(observed) preferences.
The results show that the chi-square value (x 2 (4) ˆ 16.9) is significant
(p ˆ .002), which means that there is a significant difference in club preference
(Table 59). Club A is the most popular club and club E is the least popular.
Residual represent the difference between the observed and expected
frequencies. For a chi-square analysis, a relatively large sample size is
Dialog box 98