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On Methods in the Analysis of Profile Data

Published online by Cambridge University Press:  01 January 2025

Samuel W. Greenhouse
Affiliation:
National Institute of Mental Health
Seymour Geisser
Affiliation:
National Institute of Mental Health

Abstract

This paper is concerned with methods for analyzing quantitative, non-categorical profile data, e.g., a battery of tests given to individuals in one or more groups. It is assumed that the variables have a multinormal distribution with an arbitrary variance-covariance matrix. Approximate procedures based on classical analysis of variance are presented, including an adjustment to the degrees of freedom resulting in conservative F tests. These can be applied to the case where the variance-covariance matrices differ from group to group. In addition, exact generalized multivariate analysis methods are discussed. Examples are given illustrating both techniques.

Type
Original Paper
Copyright
Copyright © 1959 The Psychometric Society

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Footnotes

*

We are indebted to Mrs. Norma French for performing all the calculations appearing in this paper.

References

Anderson, T. W.. Introduction to multivariate statistical analysis, New York: Wiley, 1958.Google Scholar
Block, J., Levine, L., andMcNemar, Q. Testing for the existence of psychometric patterns. J. abnorm. soc. Psychol., 1951, 46, 356359.CrossRefGoogle ScholarPubMed
Box, G. E. P. A general distribution theory for a class of likelihood criteria. Biometrika, 1949, 36, 317346.CrossRefGoogle ScholarPubMed
Box, G. E. P. Problems in the analysis of growth and wear curves. Biometrics, 1950, 6, 362389.CrossRefGoogle ScholarPubMed
Box, G. E. P. Some theorems on quadratic forms applied in the study of analysis of variance problems: I. Effect of inequality of variance in the one-way classification. Ann. math. Statist., 1954, 25, 290302.CrossRefGoogle Scholar
Box, G. E. P. Some theorems on quadratic forms applied in the study of analysis of variance problems: II. Effects of inequality of variance and of correlation between errors in the two-way classification. Ann. math. Statist., 1954, 25, 484498.CrossRefGoogle Scholar
Eisenhart, C. The assumptions underlying the analysis of variance. Biometrics, 1947, 3, 121.CrossRefGoogle ScholarPubMed
Geisser, S. and Greenhouse, S. W. An extension of Box's results on the use of theF distribution in multivariate analysis. Ann. math. Statist., 1958, 29, 885891.CrossRefGoogle Scholar
Heck, D. L. Some uses of the distribution of the largest root in multivariate analysis. Inst. Statist. Univ. North Carolina, Mimeo. Ser. No. 194, 1958.Google Scholar
Hotelling, H. A generalizedT test and measure of multivariate dispersion. Berkeley: Univ. Calif. Press, 1951, 2342.Google Scholar
Kullback, S. An application of information theory to multivariate analysis, II. Ann. math. Statist., 1956, 27, 122146.CrossRefGoogle Scholar
Rao, C. R. Advanced statistical methods in biometric research, New York: Wiley, 1952.Google Scholar
Roy, S. N. On a heuristic method of test construction and its use in multivariate analysis. Ann. math. Statist., 1953, 24, 220238.CrossRefGoogle Scholar
Scheffé, H. A “mixed model” for the analysis of variance. Ann. math. Statist., 1956, 27, 2336.CrossRefGoogle Scholar
Welch, B. L. Note on Mrs. Aspin's Tables and on certain approximations to the tabled functions. Biometrika, 1949, 36, 293296.Google Scholar
Wilk, M. B. and Kempthorne, O. Fixed, mixed, and random models. J. Amer. statist. Ass., 1955, 50, 11441167.Google Scholar
Wilks, S. S. Certain generalizations in the analysis of variance. Biometrika, 1932, 24, 471494.CrossRefGoogle Scholar