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

Li-Yen R. Hu, Yulei He, Katherine E. Irimata and Vladislav Beresovsky

The American Statistician, 2025, vol. 79, issue 4, 480-491

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2025.2501800 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:79:y:2025:i:4:p:480-491

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2025.2501800

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().