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Fuzzy Clusterwise Generalized Structured Component Analysis

Published online by Cambridge University Press:  01 January 2025

Heungsun Hwang ,
Wayne S. DeSarbo  and
Yoshio Takane
Heungsun Hwang*
Affiliation:
McGill University
Wayne S. DeSarbo
Affiliation:
Pennsylvania State University
Yoshio Takane
Affiliation:
McGill University
*
Requests for reprints should be sent to Heungsun Hwang, Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada. E-mail: heungsun.hwang@mcgill.ca
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Abstract

Generalized Structured Component Analysis (GSCA) was recently introduced by Hwang and Takane (2004) as a component-based approach to path analysis with latent variables. The parameters of GSCA are estimated by pooling data across respondents under the implicit assumption that they all come from a single, homogenous group. However, as has been empirically demonstrated by various researchers across a number of areas of inquiry, such aggregate analyses can often mask the true structure in data when respondent heterogeneity is present. In this paper, GSCA is generalized to a fuzzy clustering framework so as to account for potential group-level respondent heterogeneity. An alternating least-squares procedure is developed and technically described for parameter estimation. A small-scale Monte Carlo study involving synthetic data is carried out to compare the performance between the proposed method and an extant approach. In addition, an empirical application concerning alcohol use among adolescents from US northwestern urban areas is presented to illustrate the usefulness of the proposed method. Finally, a number of directions for future research are provided.

Keywords

generalized structured component analysisfuzzy clusteringrespondent heterogeneityalternating least squareslatent curve modelsalcohol use

Information

Type
Original Paper
Information
Psychometrika , Volume 72 , Issue 2 , June 2007 , pp. 181 - 198
Copyright
Copyright © 2007 The Psychometric Society

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Footnotes

The work reported in this paper was supported by Grant 290439 and Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the first and third authors, respectively. We wish to thank Terry Duncan for generously providing us with his alcohol use data. We also wish to thank the Editor, Associate Editor, and two anonymous reviewers for their constructive comments which helped improve the overall quality and readability of this manuscript.

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