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Journal of the Academy of Marketing Science

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16-02-2017 | Original Empirical Research

Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

Authors: Joseph F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Kai Oliver Thiele

Published in: Journal of the Academy of Marketing Science | Issue 5/2017

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Abstract

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.

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Note that researchers frequently distinguish between latent variables/constructs and composites. We use the term latent variable/construct to refer to the entities that represent conceptual variables in a structural equation model.

Our comparison does not consider consistent PLS (PLSc; Dijkstra 2014; Dijkstra and Henseler 2015) that corrects the PLS estimates for attenuation to mimic common factor models. As our objective is to compare composite-based SEM techniques on the basis of composite model data, PLSc is not relevant to our study.

Note that constructs in factor-based SEM are also proxies for the conceptual variables under investigation (Rigdon 2012).

Table A1 in the Online Appendix shows the indicator weights for different numbers of indicators.

For further details about the non-normal data, see the additional information on the data generation in the Online Appendix.

As the analyses show only marginal differences between normal and non-normal data, the following results presentations use the joint outcomes of the different data distribution types considered in this simulation study.

For example, for the condition with 500 observations, two indicators with equal weights of 0.625, PLS yields a MAE value of 0.05814 in the measurement models, which translates into an MARE value of 0.093. On the contrary, a very similar MAE value of 0.06029 for the condition with 500 observations, eight indicators with equal weights of 0.25 translates into a MARE value of 0.241.

Note that the MARE is not defined for the two null paths γ ₄ and γ ₅ (Fig. 2). Hence, we did not include these two paths in the MARE computations.

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Title: Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
Authors: Joseph F. Hair
G. Tomas M. Hult
Christian M. Ringle
Marko Sarstedt
Kai Oliver Thiele
Publication date: 16-02-2017
Publisher: Springer US
Published in: Journal of the Academy of Marketing Science / Issue 5/2017
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI: https://doi.org/10.1007/s11747-017-0517-x

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