Hostname: page-component-6587cd75c8-rlsgg Total loading time: 0 Render date: 2025-04-23T18:07:02.587Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical Analysis of Sets of Congeneric Tests

Published online by Cambridge University Press:  01 January 2025

K. G. Jöreskog
K. G. Jöreskog*
Affiliation:
Educational Testing Service
Get access Rights & Permissions [Opens in a new window]

Abstract

Various models for sets of congeneric tests are considered, including models appropriate for the analysis of multitrait-multimethod data. All models are illustrated with real data. The special cases when two or more tests within a set are tau-equivalent or parallel are also considered. All data analyses are done within the framework of a general model by Jöreskog [1970].

Keywords

Data AnalysisGeneral ModelPublic PolicyReal DataStatistical Theory
Type
Original Paper
Information
Psychometrika , Volume 36 , Issue 2 , June 1971 , pp. 109 - 133
Copyright
Copyright © 1971 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

Research reported in this paper has been supported by Grant NSF-GB-12959 from National Science Foundation. The writer wishes to thank Gunnar Gruvaeus and Marielle van Thillo, who wrote the computer program and assisted in all aspects of the computational work.

†

Now at Statistics Department, University of Uppsala, Sweden

References

Campbell, D. T., & Fiske, D. W.. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 1959, 56, 81–105.CrossRefGoogle ScholarPubMed
Frederiksen, N., & Jensen, O., Beaton, A. E.. Organizational climates and administrative performance, 1968, Princeton, N. J.: Educational Testing Service.CrossRefGoogle Scholar
Jöreskog, K. G. Some contributions to maximum likelihood factor analysis. Psychometrika, 1967, 32, 443–482.CrossRefGoogle Scholar
Jöreskog, K. G. A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 1969, 34, 183–202.CrossRefGoogle Scholar
Jöreskog, K. G. A general method for analysis of covariance structures. Biometrika, 1970, 57, 239–251.CrossRefGoogle Scholar
Jöreskog, K. G., Gruvaeus, G. T., & van Thillo, M.. ACOVS—A general computer program for analysis of covariance structures, 1970, Princeton, N. J.: Educational Testing Service.CrossRefGoogle Scholar
Kelley, E. L., & Fiske, D. W.. The prediction of performance in clinical psychology, 1951, Ann Arbor: University of Michigan Press.CrossRefGoogle Scholar
Kristof, W. Statistical inferences about the error variance. Psychometrika, 1963, 28, 129–143.CrossRefGoogle Scholar
Kristof, W. Estimation of true score and error variance for tests under various equivalence assumptions. Psychometrika, 1969, 34, 489–507.CrossRefGoogle Scholar
Lord, F. M. A study of speed factors in tests and academic grades. Psychometrika, 1956, 21, 31–50.CrossRefGoogle Scholar
Lord, F. M. A significance test for the hypothesis that two variables measure the same trait except for errors of measurement. Psychometrika, 1957, 22, 207–220.CrossRefGoogle Scholar
Lord, F. M., & Novick, M. R.. Statistical theories of mental test scores (with contributions by A. Birnbaum), 1968, Reading, Mass.: Addison-Wesley.Google Scholar
McNemar, Q. Attenuation and interaction. Psychometrika, 1958, 23, 259–265.CrossRefGoogle Scholar
Votaw, D. F. Jr. Testing compound symmetry in a normal multivariate distribution. Annals of Mathematical Statistics, 1948, 19, 447–473.CrossRefGoogle Scholar
Wilks, S. S. Sample criteria for testing equality of means, equality of variances, and equality of covariances in a normal multivariate distribution. Annals of Mathematical Statistics, 1946, 17, 257–281.CrossRefGoogle Scholar