CHI-SQUARE TEST: TWO MORE TYPES OF KEY INFORMATION THAT SHOULD BE REPORTED BY THE RESEARCHERS WHEN THERE IS A STATISTICALLY SIGNIFICANT ASSOCIATION BETWEEN TWO CATEGORICAL VARIABLES

Article Sidebar

PDF
Published: Apr 12, 2022
Keywords:
Chi-square Association Independence Effect Size Categorical Variables

Main Article Content

Samroeng Kaiyawong
Faculty of Business Administration, Chaiyaphum Rajabhat University

Abstract

The chi-square test is a statistical procedure commonly used for testing the association between two categorical variables measured at nominal or ordinal scale. The null hypothesis of the chi-square test states that there is no association between two categorical variables. The alternative hypothesis states that there is an association between two categorical variables. If the result of chi-square test is statistically significant, the researchers should analyze and report how the levels of one categorical variable depend on the levels of the other categorical variable by analyzing standardized residual in each cell. Researchers, in addition, should analyze and report the effect size which is a statistic used to evaluate the magnitude of association between two categorical variables. The research result will be evaluated as practically significant if the magnitude of association between two categorical variables is large enough to create the value in the real life setting of a person who draws out the implications of research results.

Article Details

How to Cite
Kaiyawong, S. (2022). CHI-SQUARE TEST: TWO MORE TYPES OF KEY INFORMATION THAT SHOULD BE REPORTED BY THE RESEARCHERS WHEN THERE IS A STATISTICALLY SIGNIFICANT ASSOCIATION BETWEEN TWO CATEGORICAL VARIABLES. Panyapiwat Journal, 14(1), 333–346. retrieved from https://so05.tci-thaijo.org/index.php/pimjournal/article/view/247450
Issue
Vol. 14 No. 1 (2022): January-April
Section
Academic Article
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

I and co-author(s) certify that articles of this proposal had not yet been published and is not in the process of publication in journals or other published sources. I and co-author accept the rules of the manuscript consideration. Both agree that the editors have the right to consider and make recommendations to the appropriate source. With this rights offering articles that have been published to Panyapiwat Institute of Management. If there is a claim of copyright infringement on the part of the text or graphics that appear in the article. I and co-author(s) agree on sole responsibility.

References

Amidei, J., Piwek, P., & Willis, A. (2019). Agreement is overrated: A plea for correlation to assess human evaluation reliability. In K. V. Deemter, & C. Lin (Eds.), The 12th International Conference on Natural Language Generation (pp. 344-354). Association for Computational Linguistics. https://aclanthology.org/W19-8642

Berenson, M. L., Levine, D. M., & Krehbiel, T. C. (2012). Basic business statistics: Concepts and applications (12th ed.). Prentice Hall.

Bluman, A. G. (2009). Elementary statistics: A step by step approach (7th ed.). Mcgraw-Hill.

Goodman, L. A., & Kruskal, W. H. (1954). Measures of association for cross classifications. Journal of the American Statistical Association, 49, 732-764.

Kaiyawong, S. (2017). Effect size: The most important product of hypothesis testing. Panyapiwat Journal, 9(1), 276-287. [in Thai]

Kirk, R. (2008). Statistics: An introduction (5th ed.). Thomson.

Rea, L. M., & Parker, R. A. (1992). Designing and conducting survey research. Jossey-Boss.

Weiers, R. M. (2011). Introduction to business statistics (7th ed.). South-Western.

Weiss, N. A. (2012). Introductory statistics (9th ed.). Addison-Wesley.