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Generalized Structured Component Analysis with Latent Interactions

Published online by Cambridge University Press:  01 January 2025

Heungsun Hwang ,
Moon-Ho Ringo Ho  and
Jonathan Lee
Heungsun Hwang*
Affiliation:
McGill University
Moon-Ho Ringo Ho
Affiliation:
Nanyang Technological University
Jonathan Lee
Affiliation:
California State University
*
heungsun.hwang@mcgill.ca
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Abstract

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA is proposed to effectively deal with various types of interactions among latent variables. In the proposed method, a latent interaction is defined as a product of interacting latent variables. As a result, this method does not require the construction of additional indicators for latent interactions. Moreover, it can easily accommodate both exogenous and endogenous latent interactions. An alternating least-squares algorithm is developed to minimize a single optimization criterion for parameter estimation. A Monte Carlo simulation study is conducted to investigate the parameter recovery capability of the proposed method. An application is also presented to demonstrate the empirical usefulness of the proposed method.

Keywords

generalized structured component analysislatent interactionsalternating least squares
Type
Original Paper
Information
Psychometrika , Volume 75 , Issue 2 , June 2010 , pp. 228 - 242
Copyright
Copyright © 2010 The Psychometric Society

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