New Chi-Square Procedure

To help you get the most out of IBM SPSS Statistics, the Version 1 SPSS experts want to highlight the new Chi-Square procedure introduced in Version 31. This test is now available as a standalone option under Analyze > Descriptive Statistics > ChiSquare, making it easier to access and apply directly.

In IBM SPSS Statistics, you can work within a single, integrated interface to perform a wide range of statistical and analytical tasks. Whether you’re running descriptive statistics, regression models, advanced statistical procedures, or machine learning algorithms, SPSS provides a unified environment that supports both novice users and experienced analysts. It enables you to create publication-ready charts, tables, and decision trees—all within one tool. With its intuitive design, SPSS Statistics is easy to use, supports automation of analyses, integrates with open-source tools like R and Python, and offers built-in guidance to help you at every step.

The Chi-Square test is a statistical hypothesis test used to determine whether a significant association exists between two categorical variables. It compares observed frequencies in a contingency table to expected frequencies under the assumption of independence. If the observed and expected values differ significantly, the test suggests a relationship between the variables.

Having both the new Chi-Square procedure and the traditional Crosstabs with Chi-Square gives users valuable flexibility. The new procedure focuses solely on the test itself, offering a streamlined output that’s ideal when analysts want to highlight just the statistical result, without additional percentages or cell counts. This is particularly useful in reports or presentations where clarity and brevity are key, or when the audience is primarily interested in whether a relationship exists between variables. Meanwhile, Crosstabs remains available for users who need a more detailed breakdown of the data. This enhancement reflects SPSS’s ability to quickly generate and automate statistical tests and results. By refining the default output and offering tailored procedures, users can interpret relationships between categorical variables more easily and present their findings with greater clarity. Whether you’re preparing reports, teaching, or presenting to stakeholders, these options help make complex statistical results more accessible and impactful.