Estimation issues with PLS and CBSEM: Where the bias lies!

https://doi.org/10.1016/j.jbusres.2016.06.007Get rights and content
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Highlights

  • Aligns measurement and estimation views in structural equation modeling (SEM)

  • Clarifies misconceptions about measurement concepts and their role in SEM

  • Highlights biases that occur when using SEM methods on misspecified populations

  • Provides guidance regarding the choice of the appropriate SEM method

Abstract

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.

Keywords

Common factor models
Composite models
Reflective measurement
Formative measurement
Structural equation modeling
Partial least squares

Cited by (0)

The authors thank George R. Franke (University of Alabama), Edward E. Rigdon (Georgia State University), and the participants of the 2nd International Symposium on Partial Least Squares Path Modeling at the University of Seville for their constructive feedback. The authors also thank Jörg Henseler, University of Twente, The Netherlands, for his support with the replication of Reinartz, Haenlein, and Henseler's study (2009), and with the development of the composite model-based data generation approach. 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.