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A Multivariate Reduced-Rank Growth Curve Model with Unbalanced Data

Published online by Cambridge University Press:  01 January 2025

Heungsun Hwang and
Yoshio Takane
Heungsun Hwang*
Affiliation:
HEC Montreal
Yoshio Takane
Affiliation:
Mcgill University
*
Requests for reprints should be sent to Heungsun Hwang, HEC Montreal, Department of Marketing, 3000 Chemin de la Cote Ste-Catherine, Montreal, Quebec, H3T 2A7, CANADA. Email: heungsun.hwang@hec.ca
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Abstract

A multivariate reduced-rank growth curve model is proposed that extends the univariate reducedrank growth curve model to the multivariate case, in which several response variables are measured over multiple time points. The proposed model allows us to investigate the relationships among a number of response variables in a more parsimonious way than the traditional growth curve model. In addition, the method is more flexible than the traditional growth curve model. For example, response variables do not have to be measured at the same time points, nor the same number of time points. It is also possible to apply various kinds of basis function matrices with different ranks across response variables. It is not necessary to specify an entire set of basis functions in advance. Examples are given for illustration.

Keywords

Multivariate growth curve modelsreduced-rank restrictionlongitudinal data

Information

Type
Theory And Methods
Information
Psychometrika , Volume 69 , Issue 1 , March 2004, pp. 65 - 79
Copyright
Copyright © 2004 The Psychometric Society

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Footnotes

The work reported in this paper was supported by Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the second author. We thank Jennifer Stephan for her helpful comments on an earlier version of this paper. We also thank Patrick Curran and Terry Duncan for kindly letting us use the NLSY and substance use data, respectively. The substance use data were provided by Grant DA09548 from the National Institute on Drug Abuse.

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