Chi Square test

The Chi Square Test procedure tabulates a variable into categories and computes a chi-square statistic. The chi-squared test is a statistical hypothesis test used to determine whether a significant association exists between two categorical variables. The test evaluates whether the observed frequencies in the data differ from the expected frequencies under the null hypothesis, which assumes no association between the variables.

This procedure requires Statistics Base Edition.

Example

In market research, Analysts need to understand consumer preferences. For this, they compare observed customer behavior with expected patterns to create marketing strategies effectively.

Statistics

The Pearson chi-square, the likelihood-ratio chi-square, Fisher's exact test, and Yates' corrected chi-square (continuity correction).

Data considerations and assumptions

Use ordered or unordered numeric categorical variables (ordinal or nominal levels of measurement). To convert string variables to numeric variables, use the Automatic Recode procedure, which is available on the Transform menu.

Nonparametric tests do not require assumptions about the shape of the underlying distribution. The data are assumed to be a random sample. The expected frequencies for each category should be at least 1. No more than 20% of the categories should have expected frequencies of less than 5.

From the menu, click Analyze > Descriptive Statistics > ChiSquare.

1. Select the variables and add them to Row(s) and Column(s).
2. Click OK. The procedure runs by considering the default settings.