Volume 75, Issue 2 p. 220-251
Original Article

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Gyeongcheol Cho

Gyeongcheol Cho

McGill University, Montreal, Quebec, Canada

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Marko Sarstedt

Marko Sarstedt

Ludwig-Maximilians-University Munich, Germany

Babeş?-Bolyai University, Cluj-Napoca, Romania

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Heungsun Hwang

Corresponding Author

Heungsun Hwang

McGill University, Montreal, Quebec, Canada

Correspondence should be addressed to Heungsun Hwang, Department of Psychology, McGill University, 2001 McGill College Avenue, Montreal, QC, Canada H3A 1G1 (email: [email protected]).

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First published: 18 October 2021
Citations: 11

Correction added on 25 October 2021, after online publication: The affiliation Babe-Bolyai University has been updated as Babeş-Bolyai University.

Abstract

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.

Conflicts of interest

All authors declare no conflict of interest.