Reflections on Partial Least Squares Path Modeling
Point/Counterpoint on Partial Least Squares Organizational Research Methods 2014, Vol. 17(2) 210-251 ª The Author(s) 2014 Reflections on Partial Least Reprints and permission: sagepub.com/journalsPermissions.nav Squares Path Modeling DOI: 10.1177/1094428114529165 orm.sagepub.com Cameron N. McIntosh1, Jeffrey R. Edwards2, and John Antonakis3 Abstract ThepurposeofthepresentarticleistotakestockofarecentexchangeinOrganizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (a) can be truly characterized as a technique for structural equation modeling (SEM), (b) is able to correct for measurement error, (c) can be used to validate measurement models, (d) accommodates small sample sizes, (e) is able to provide null hypothesis tests for path coefficients, and (f) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature to offer additional thoughts concerning the utility of PLS-PM and ways in which the tech- nique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models. Keywords structural equation modeling, partial least squares, factor analysis, reliability and validity, instrumental variables, bootstrapping, model fit Partial least squares path modeling (PLS-PM) has begun to achieve widespread usage among applied researchers. Starting with the initial work by H. Wold (1966, 1973, 1975), the application of PLS-PM has been stimulated by comprehensive expositions and computer implementations by Lohmo¨ller (1984, 1988, 1989), Chin (1998, 2003), and others (for detailed historical reviews of the development of PLS-PM, see Mateos-Aparicio, 2011; Trujillo, 2009).
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