PLS, Small Sample Size, and Statistical Power in MIS Research

By Dale Goodhue, William Lewis, Ronald L. Thompson. Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), 2006

352 citations recorded by the source corpus.

Subfields: Food Science, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/913250111bb1264cd4242c1473beb350952843c5

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Citations in the corpus

A Comparison of PLS and ML Bootstrapping Techniques in SEM: A Monte Carlo Study (2013, background): “While there are a few studies comparing CBSEM and PLS under various sets of design factors [1, 6, 18], none of them have focused on bootstrapping behaviors of the estimation methods used.”
Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review (2020, background): “According to their conclusions they mentioned “for simple SEM models with normally distributed data and relatively reliable measures, none of the techniques have adequate power to detect small or medium effects at small sample size” (Goodhue et al., 2006).”
Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly (2012, background): “This issue has been passionately debated over the last years (e.g., Goodhue et al. 2006; Marcoulides and Saunders 2006) with Gefen et al. (2011, p. iii) noting that there is an “apparent misuse of perceived leniencies such as assumptions about minimum sample sizes.””
Structural Equation Modeling in Information Systems Research Using Partial Least Squares (2010, background): “This entails a number of voices calling for a more differentiated view of sample size requirements (Goodhue et al. 2006; Marcoulides et al. 2009; Marcoulides and Saunders 2006).”
An assessment of the use of partial least squares structural equation modeling in marketing research (2011, background): “The sample size argument in particular has been the subject of much debate (e.g., Goodhue et al. 2006; Marcoulides and Saunders 2006).”
SMEs' adoption of enterprise applications: A technology-organisation-environment model (2013, methodology): “Partial least squares (PLS) is a popular Structural Equation Modelling (SEM) technique to conduct data analysis (Goodhue et al., 2006).”
Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research (2012, background): “In other words, applying the “10 times” rule of thumb without performing a formal power analysis may lead to hypothesis tests with low power (Goodhue et al., 2006; Marcoulides and Saunders, 2006). Based on the results of a simulation study, Goodhue et al. (2006) argue that the “10 times” rule of thu”
ASSESSING REFLECTIVE MODELS IN MARKETING RESEARCH: A COMPARISON BETWEEN PLS AND PLSc ESTIMATES (2018, result): “…information maximum likelihood in CB-SEM but it can more or less produce identical results of loading, path coefficients and explanatory power, thereby offering an opportunity to fill the gap between factor models and composite models (Goodhue et al., 2006; Lu et al., 2011; Reinartz et al., 2009).”
The Use of Partial Least Squares Path Modeling in International Marketing (2009, result): “’’ Goodhue et al. (2006) stress that even though PLS path modeling seems to have no special abilities at small sample size, its performance, in terms of statistical power, is equal to other techniques for normally distributed data.”