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

  • Original Empirical Research
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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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Notes

  1. 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.

  2. 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.

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

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

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

  6. 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.

  7. 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.

  8. 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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Acknowledgements

Earlier versions of the manuscript have been presented at the 2015 Academy of Marketing Science Annual Conference held in Denver, Colorado, and the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users held in Seville, 2015. The authors would like to thank Jan-Michael Becker, University of Cologne, Jörg Henseler, University of Twente, and Rainer Schlittgen, University of Hamburg, for their support and helpful comments when developing the simulation study and its data generation in order to improve earlier versions of the manuscript. Even though this research does not explicitly refer to the use of the statistical software SmartPLS (http://www.smartpls.com), Ringle acknowledges a financial interest in SmartPLS.

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  1. University of South Alabama, Mobile, AL, USA

    Joseph F. Hair

  2. Michigan State University, East Lansing, MI, USA

    G. Tomas M. Hult

  3. Hamburg University of Technology (TUHH), Hamburg, Germany

    Christian M. Ringle & Kai Oliver Thiele

  4. The University of Newcastle, Callaghan, NSW, Australia

    Christian M. Ringle & Marko Sarstedt

  5. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Marko Sarstedt

Authors
  1. Joseph F. Hair
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  2. G. Tomas M. Hult
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  3. Christian M. Ringle
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  4. Marko Sarstedt
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  5. Kai Oliver Thiele
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Corresponding author

Correspondence to G. Tomas M. Hult.

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John Hulland served as Area Editor for this article.

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Hair, J.F., Hult, G.T.M., Ringle, C.M. et al. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. of the Acad. Mark. Sci. 45, 616–632 (2017). https://doi.org/10.1007/s11747-017-0517-x

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  • DOI: https://doi.org/10.1007/s11747-017-0517-x

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