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Some Developments in Multivariate Generalizability

Published online by Cambridge University Press:  01 January 2025

George W. Joe  and
J. Arthur Woodward
George W. Joe*
Affiliation:
Texas Christian University
J. Arthur Woodward
Affiliation:
University of Texas Medical Branch, Galveston
*
Requests for reprints should be sent to George W. Joe, Institute of Behavioral Research, Texas Christian University, Fort Worth, Texas 76129.
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Abstract

This article is concerned with estimation of components of maximum generalizability in multifacet experimental designs involving multiple dependent measures. Within a Type II multivariate analysis of variance framework, components of maximum generalizability are defined as those composites of the dependent measures that maximize universe score variance for persons relative to observed score variance. The coefficient of maximum generalizability, expressed as a function of variance component matrices, is shown to equal the squared canonical correlation between true and observed scores. Emphasis is placed on estimation of variance component matrices, on the distinction between generalizability- and decision-studies, and on extension to multifacet designs involving crossed and nested facets. An example of a two-facet partially nested design is provided.

Keywords

reliabilityvariance component matricesmaximally reliable composite

Information

Type
Original Paper
Information
Psychometrika , Volume 41 , Issue 2 , June 1976 , pp. 205 - 217
Copyright
Copyright © 1976 The Psychometric Society

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Footnotes

Appreciation is expressed to the Office of Research in Medical Education, University of Texas Medical Branch, for permitting use of their data.

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