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Canonical Analysis of Longitudinal and Repeated Measures Data with Stationary Weights

Published online by Cambridge University Press:  01 January 2025

William Meredith  and
John Tisak
William Meredith*
Affiliation:
University of California, Berkeley
John Tisak
Affiliation:
University of California, Berkeley
*
Requests for reprints should be sent to William Meredith, Department of Psychology, University of California, Berkeley, California 94720.
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Abstract

When measuring the same variables on different “occasions”, two procedures for canonical analysis with stationary compositing weights are developed. The first, SUMCOV, maximizes the sum of the covariances of the canonical variates subject to norming constraints. The second, COLLIN, maximizes the largest root of the covariances of the canonical variates subject to norming constraints. A characterization theorem establishes a model building approach. Both methods are extended to allow for Cohort Sequential Designs. Finally a numerical illustration utilizing Nesselroade and Baltes data is presented.

Keywords

canonical correlationstationary weightslongitudinal data analysis
Type
Original Paper
Information
Psychometrika , Volume 47 , Issue 1 , March 1982 , pp. 47 - 67
Copyright
Copyright © 1982 The Psychometric Society

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Footnotes

The authors wish to thank John Nesselroade for permitting us to use the data whose analysis we present.

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