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Chi-Square Test for Association using SPSS

Introduction

The chi-square test for independence, also called Pearson's chi-square test or the chi-square test of association, is used to discover if there is a relationship between two categorical variables.

SPSS

Assumptions

When you choose to analyse your data using a chi-square test for independence, you need to make sure that the data you want to analyse "passes" two assumptions. You need to do this because it is only appropriate to use a chi-square test for independence if your data passes these two assumptions. If it does not, you cannot use a chi-square test for independence. These two assumptions are:

In the section, Procedure, we illustrate the SPSS procedure to perform a chi-square test for independence. First, we introduce the example that is used in this guide.

SPSS

Example

Educators are always looking for novel ways in which to teach statistics to undergraduates as part of a non-statistics degree course (e.g., psychology). With current technology, it is possible to present how-to guides for statistical programs online instead of in a book. However, different people learn in different ways. An educator would like to know whether gender (male/female) is associated with the preferred type of learning medium (online vs. books). Therefore, we have two nominal variables: Gender (male/female) and Preferred Learning Medium (online/books).

SPSS

Setup in SPSS

In SPSS, we created two variables so that we could enter our data: Gender and Preferred Learning Medium. In our enhanced linear regression guide, we show you how to correctly enter data in SPSS to run a chi-square test for independence. Alternately, we have a generic, "quick start" guide to show you how to enter data into SPSS, available here.

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Test Procedure in SPSS

The 11 steps below show you how to analyse your data using a chi-square test for independence in SPSS. At the end of these 11 steps, we show you how to interpret the results from your chi-square test for independence.

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Output

You will be presented with some tables in the Output Viewer under the title "Crosstabs". The tables of note are presented below:

The Crosstabulation Table (Gender*Preferred Learning Medium Crosstabulation)


The Chi-Square Test For Independence Output

Published with written permission from SPSS Inc., an IBM Company.

This table allows us to understand that both males and females prefer to learn using online materials vs. books.

The Chi-Square Tests Table


The Chi-Square Test For Independence Output

Published with written permission from SPSS Inc., an IBM Company.

When reading this table we are interested in the results for the Continuity correction. We can see here that χ(1) = 0.487, p = 0.485. This tells us that there is no statistically significant association between Gender and Preferred Learning Medium. That is, both Males and Females equally prefer online learning vs. books.

The Symmetric Measures Table


The Chi-Square Test For Independence Output

Published with written permission from SPSS Inc., an IBM Company.

Phi and Cramer's V are both tests of the strength of association. We can see that the strength of association between the variables is very weak.

Bar chart


The Chi-Square Test For Independence Output

Published with written permission from SPSS Inc., an IBM Company.

It can be easier to visualize data than read tables. The clustered bar chart option allows a relevant graph to be produced that highlights the group categories and the frequency of counts in these groups.

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