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Tests of Symmetry in Three-Way Contingency Tables

Published online by Cambridge University Press:  01 January 2025

Campbell B. Read*
Affiliation:
Southern Methodist University
*
Requests for reprints should be sent to Dr. C. B. Read, Department of Statistics, Southern Methodist University, Dallas, Texas 75275.

Abstract

Three-dimensional contingency tables are analyzed, with one variable (e.g., sex) as a factor, and with a natural relation between the other variables (e.g. left and right eye vision). Models of special interest, like symmetry and proportional symmetry between the related variables, and homogeneity across the factor levels, are investigated. Maximum likelihood estimators of parameters and partitions of chi-square goodness-of-fit statistics are explicitly presented; the independence of certain models is noted, and an example is discussed.

Type
Original Paper
Copyright
Copyright © 1978 The Psychometric Society

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Footnotes

The author wishes to thank the editor and referees for helpful suggestions in preparing the final version of this paper.

References

Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. Discrete multivariate analysis, 1975, Cambridge: MIT Press.Google Scholar
Bowker, A. H. A test for symmetry in contingency tables. Journal of the American Statistical Association, 1948, 43, 572574.CrossRefGoogle ScholarPubMed
Caussinus, H. Contribution à l'analyse statistique des tableaux de correlation. Annales du Faculté des Sciences, Université de Toulouse, 1966, 29, 77182.Google Scholar
Darroch, J. N., & Silvey, S. D. On testing more than one hypothesis. Annals of Mathematical Statistics, 1963, 34, 555567.CrossRefGoogle Scholar
Goodman, L. A. On partitioning chi-square and detecting partial association in three-way contingency tables. Journal of the Royal Statistical Society, Series B, 1969, 31, 486498.CrossRefGoogle Scholar
Goodman, L. A. The multivariate analysis of qualitative data: Interactions among multiple classifications. Journal of the American Statistical Association, 1970, 65, 226256.CrossRefGoogle Scholar
Hamdan, M. A., Pirie, W. R. & Arnold, J. C. Simultaneous testing of McNemar's problem for several populations. Psychometrika, 1975, 40, 153161.CrossRefGoogle Scholar
Hogg, R. V. On the resolution of statistical hypotheses. Journal of the American Statistical Association, 1961, 56, 978989.CrossRefGoogle Scholar
Ireland, C. T., Ku, H. H. & Kullback, S. Symmetry and marginal homogeneity of an r ×r contingency table. Journal of the American Statistical Association, 1969, 64, 13231341.Google Scholar
Koch, G. G. & Reinfurt, D. W. The analysis of categorical data from mixed models. Biometrics, 1971, 27, 157173.CrossRefGoogle Scholar
Kullback, S. Information theory, 1959, New York: J. Wiley.Google Scholar
McKinlay, S. M. The design and analysis of the observational study—a review. Journal of the American Statistical Association, 1975, 70, 503520.Google Scholar
McNemar, Q. Note on the sampling error of the differences between correlated proportions or percentages. Psychometrika, 1947, 12, 153157.CrossRefGoogle ScholarPubMed
Plackett, R. L. The analysis of categorical data, 1974, London: Griffin.Google Scholar
Read, C. B. The partitioning of chi-square: A teaching approach. Communications in Statistics, 1977, 6, 553562.CrossRefGoogle Scholar
Stuart, A. The estimation and comparison of strengths of association in contingency tables. Biometrika, 1953, 40, 105110.CrossRefGoogle Scholar