HOW TO RUN
CHI SQUARE TEST
IN SPSS

1. Using Chi-Square Statistic in Research

This easy tutorial will show you how to run the Chi-Square test in SPSS, and how to interpret the result.

The chi-square test of independence uses to investigate the relationship between two categorical variables that have two or more categories. In addition, the test compares the proportions, that is, the frequency of cases observed in each of the categories with values that would be expected to have if there is no relationship between the variables.

First of all, the chi-square test based on a contingency table that shows the intersection of each category of one variable with each group of the other variable.

2. The Assumptions of the Chi-Square Test include:

• The data in the cells should be frequencies or counts of cases rather than percentages or some other transformation of the data.
• The levels (or categories) of the variables are mutually exclusive. In other words, a particular subject fits into one and only one level of each of the variables.
• Each subject may contribute data to one and only one cell in the χ². If, for example, the same subjects are tested over time such that the comparisons are of the same subjects at Time 1, Time 2, Time 3, etc., then χ² may not be used.

The Study Groups must be independent for Chi-Square Test

• The study groups must be independent. That is to say, a different test must be used if the two groups are related. For example, a different test must be used if the researcher’s data consists of paired samples, such as in studies in which a parent is paired with his or her child.

• There are 2 variables, and both are measured as categories, usually at the nominal level. However, data may be ordinal data. Interval or ratio data that have been collapsed into ordinal categories may also be used. While Chi-square has no rule about limiting the number of cells (by limiting the number of categories for each variable), a very large number of cells (over 20) can make it difficult to meet assumption #6 below and to interpret the meaning of the results.

• The value of the cell expects should be 5 or more in at least 80% of the cells, and no cell should have an expected of less than one (3). This assumption is most likely to be met if the sample size equals at least the number of cells multiplied by 5. Essentially, this assumption specifies the number of cases (sample size) needed to use the χ² for any number of cells in that χ². This requirement will be fully explained in the example of the calculation of the statistic in the case study example. (Mary L. McHugh, 2013)

3. An Example for the Chi-Square Test

Null hypothesis:

The education level is not independent of gender.

Alternative hypothesis:

The education level is independent of gender.

This easy tutorial will show you how to run the Chi-Square test in SPSS, and how to interpret the result.

  A chi-square test for independence was computed to determine whether education (primary school, secondary school, BA, Master, Ph.D.) is independent of gender (male, female). The results are not significant, χ²(4) = 1.111, p = .892, Cramer’s V/phi = .892. We fail to reject the null hypothesis that education is the same across gender (male, female) and conclude that education is not related to gender.

Visit our Reporting Chi-Square Test Page for more details.