Conducting a Chi Square Test of Independence in SPSS with Only Frequencies

Quantitative Results
Statistical Analysis

So let’s say you had some data one two nominal variables and wanted to test the independence between the two variables. However, instead of having the data on an individual-level scale, you had it in the form like what is shown below—in a cross-tabular form. You could compute the chi square test statistic by hand, but this can be cumbersome. However, there is a very simple way in SPSS to conduct the chi square test using only the numbers shown below.

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Group
Fruit chosen
Group 1
Group 2
Group 3
Apple
10
7
22
Orange
8
19
9
Grape
17
13
12

So with this data, we have three groups (1, 2, and 3), and three fruits (apple, orange, and grape). The first thing to do to conduct the chi square test of independence in SPSS would be to set up the two grouping variables. So in SPSS, one variable will be “Apple, Apple, Apple, Orange, Orange, Orange, Grape, Grape, Grape” while the other variable will be “1, 2, 3, 1, 2, 3, 1, 2, 3” as shown below.

By doing so, we have taken care of each combination of fruit chosen and group. Now the frequencies for each group combination are needed. So the first combination is “Apple, Group 1,” which has a frequency of 10. So simply input “10” for the first item in a new variable. Continue to do this until each fruit chosen and group combination has the correct frequency, as shown below.

 
Once this is done, simply go to Data -> Weight Cases… Select Weight cases by, and use the new “Frequency” variable. Next, to actually perform the chi square, simply go to Analyze -> Descriptive statistics -> Crosstabs. Put the Fruit in Row(s): and the Group in Column(s):. Then in the Statistics… dialogue box, check the Chi-square box. Hit Continue and then OK.  The results of the chi square show the same cross-tabulation box as we had above as well as the actual chi square statistic. By doing it in SPSS instead of by hand or using Excel, we can get the test statistic information we’re looking for much faster.
Fruit * Group Crosstabulation
Count
Group
Total
1.00
2.00
3.00
Fruit
Apple
10
7
22
39
Grape
17
13
12
42
Orange
8
19
9
36
Total
35
39
43
117
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
15.659a
4
.004
Likelihood Ratio
15.184
4
.004
N of Valid Cases
117
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 10.77.