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

Joseph F. Hair (), G. Tomas M. Hult (), Christian Ringle, Marko Sarstedt () and Kai Oliver Thiele ()
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Joseph F. Hair: University of South Alabama
G. Tomas M. Hult: Michigan State University
Marko Sarstedt: The University of Newcastle
Kai Oliver Thiele: Hamburg University of Technology (TUHH)

Journal of the Academy of Marketing Science, 2017, vol. 45, issue 5, No 3, 616-632

Abstract: 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.

Keywords: Composite; Generalized structured component analysis; GSCA; Partial least squares; PLS; SEM; Simulation; Structural equation modeling; Sum scores regression (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (247)

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DOI: 10.1007/s11747-017-0517-x

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