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

Author

Listed:
  • Joseph F. Hair

    (University of South Alabama)

  • G. Tomas M. Hult

    (Michigan State University)

  • Christian M. Ringle

    (Hamburg University of Technology (TUHH)
    The University of Newcastle)

  • Marko Sarstedt

    (The University of Newcastle
    Otto-von-Guericke-University Magdeburg)

  • Kai Oliver Thiele

    (Hamburg University of Technology (TUHH))

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.

Suggested Citation

  • Joseph F. Hair & G. Tomas M. Hult & Christian M. Ringle & Marko Sarstedt & Kai Oliver Thiele, 2017. "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 616-632, September.
    • Handle: RePEc:spr:joamsc:v:45:y:2017:i:5:d:10.1007_s11747-017-0517-x
    DOI: 10.1007/s11747-017-0517-x
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    References listed on IDEAS

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