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Much Ado About Survey Tables: A Comparison of Chi-Square Tests and Software to Analyze Categorical Survey Data

Author

Listed:
  • Li-Yen R. Hu
  • Yulei He
  • Katherine E. Irimata
  • Vladislav Beresovsky

Abstract

Chi-square tests are often employed to examine the association of categorical variables, the homogeneity of proportions between two or more samples, and the goodness-of-fit for a specified distribution. To account for the complex design of survey data, variants of Chi-square tests as well as software packages that implement these tests have been developed. Nevertheless, from a survey practitioner’s perspective, there is a lack of applied literature that reviews and compares alternative options of survey Chi-square tests and their associated programming and output. This article aims to fill such a gap. Many modern statistical software packages for survey analysis are capable of computing either the Wald Chi-square test or the Rao-Scott Chi-square test, along with other types of Chi-square tests, including the Rao-Scott likelihood ratio Chi-square test and the Wald log-linear Chi-square test. This article focuses on these four types of Chi-square tests, and examines four statistical packages that compute them in SAS®, R, Python, and SUDAAN®. While the same type of tests using different packages yield similar results, different types of Chi-square tests may yield variations in p-values when conducting the same comparison. Sample programming code is included in Appendix for readers’ reference.

Suggested Citation

  • Li-Yen R. Hu & Yulei He & Katherine E. Irimata & Vladislav Beresovsky, 2025. "Much Ado About Survey Tables: A Comparison of Chi-Square Tests and Software to Analyze Categorical Survey Data," The American Statistician, Taylor & Francis Journals, vol. 79(4), pages 480-491, October.
    • Handle: RePEc:taf:amstat:v:79:y:2025:i:4:p:480-491
    DOI: 10.1080/00031305.2025.2501800
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