The Chi-Square (X2) statistic may be used to determine if two categorical (nominal or ordinal variables with less than 5 rankings) variables are related. For example, you may hypothesize that gender influences a person’s political party identification. You can determine some of this information by looking at the cross tabulation and comparing the percentages of men and women for each party identification. This statistic involves comparing your actual results with the results you would expect to have if there were NO difference between women and men in terms of their political party affiliation.
The following example uses the GSS 2008 (1500 cases) database.
Click Analyze, Descriptive Statistics, Crosstabs. Click on your dependent variable name and place it in the “row” box and then select your independent variable and place it in the “column” box. For this example, our independent variable is SEX and dependent variable is PARTYID.
Your screen should look like this:
Now click the Statistics button and select Chi-Square.
Click OK. Then click Cells. Check to ensure that Observed is in the “count” box and that Row, Column and Total boxes are all checked in the “percentage” box.
Click OK. Your output should look like this:
Interpreting Chi-square test for independence
One of the requirements for Chi-Square is that each and every cell has a frequency of 5 or greater. You first need to check to see if the data in your table meet this requirement. Look for footnote underneath the Chi-square Tests box. Our output includes this information in footnote ‘a’; none of our cells have a frequency less than 5 and therefore we have not violated this chi-square assumption.
Now look at the “Pearson Chi-Square Asymp. Sig (2 sided)”*. Since Chi-Square is testing the null hypothesis, the Sig value must be .05 or less for there to be a significant statistical for the relationship between the variables. In this example, the Sig. is .001, so there is very strong statical significance for the relationship between gender and political party identification.
*Special note: If you are testing two variables that each have two categories, the chi-square value may be overestimated. The Yates’ Continuity of Correction is the better statistic to use. For example, you may hypothesize that there is a relationship between gender and believing that a pregnant woman should be able to obtain an abortion for any reason. The GSS database includes a variable ABANY with yes and no as possible answers (refusing to answer, don’t know, and no answer are treated as missing responses). Here is the Chi-Squares Test output for these two variables:
Note that the Chi-square’s significance is .084 and so there is no statistical significance between the relationship between gender and belief that a woman should be able to obtain an abortion for any reason. Look at the “Continuity Correction” line below. This will appear if you are examing variables that each have 2 possible responses. The corrected significance is .096; therefore, this also suggests that there is no statistical significance between the relationship of the two variables. Most of the time the difference between the corrected continuity and Chi-square is very small. It is important however to know that you should examine the corrected continuity statistic if you are using chi-square with a 2×2 table and your chi-square significance is approximately .05.
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SPSS eTutor by Dee Britton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.