Two new methods for estimating structural equation models: An illustration and a comparison with two established methods

J. of the Acad. Mark. Sci.

Ringle

Sarstedt

2014

18,986

405

12,498

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the FornellLarcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

Section: Introductionmentioning

confidence: 99%

A new criterion for assessing discriminant validity in variance-based structural equation modeling

J. of the Acad. Mark. Sci.

Ringle

Sarstedt

2014

18,986

405

12,498

“…This topic has been passionately debated in recent years (e.g., Marcoulides & Saunders, 2006) and has been empirically examined in various simulation studies (e.g., Areskoug, 1982;Goodhue et al, 2012;Hulland et al, 2010;Lu et al, 2011;Reinartz et al, 2009;Vilares & Coelho, 2013). As also emphasized in previous studies (e.g., Hair et al, 2013;Hair, Sarstedt, Pieper, et al, 2012;, we agree with R&E's criticism that many authors seem to believe that sample size considerations do not play a role in the application of PLS by scholars.…”

Section: Critique 4: Can Pls Be Used For Null Hypothesis Significancementioning

confidence: 99%

Common Beliefs and Reality About PLS

Organizational Research Methods

Dijkstra

Sarstedt

et al. 2014

2,325

769

This article addresses Rö nkkö and Evermann's criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann's study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rö nkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.

“…Instead, they should resort to the ample support provided by a plethora of conceptual, empirical, and simulation-based comparisons of structural equation modeling techniques (e.g., Dijkstra 1983;Fornell and Bookstein 1982;Lu et al 2011;Reinartz et al 2009) to make deliberate choices among their options.…”

Section: Further Researchmentioning

confidence: 99%

“…Overall then, GSCA cannot be universally recommended for use in marketing research, regardless of whether a correct model specification has been achieved. Instead, researchers should make deliberate choices based on conceptual, empirical, and simulation-based comparisons of extant structural equation modeling techniques (e.g., Dijkstra 1983;Fornell and Bookstein 1982;Lu et al 2011;Reinartz et al 2009). …”

mentioning

confidence: 99%

Why generalized structured component analysis is not universally preferable to structural equation modeling

J. of the Acad. Mark. Sci.

2011

119

Generalized structured component analysis has emerged in marketing and psychometric literature as an alternative to structural equation modeling. A recent simulation study recommends that, in most cases, this analysis is preferable to structural equation modeling because it outperforms the latter when the model is misspecified. This article examines the characteristics of generalized structured component analysis and reveals that the surprising previous findings are attributable to an incomplete experimental design and an error incurred during the software implementation of generalized structured component analysis. Simulated data show that generalized structured component analysis provides inconsistent estimates. In some instances, model misspecification can nearly neutralize this inconsistency, but in others it will reinforce the inconsistency. Moreover, generalized structured component analysis is hardly suitable for mediation analysis because it substantially overestimates the direct effect. Thus, generalized structured component analysis cannot be recommended universally over structural equation modeling.