Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods

Ned Kock · Pierre Hadaya

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Psychometric Methodologies and Testing Statistical Methods and Applications Advanced Statistical Methods and Models

Abstract

Abstract Partial least squares‐based structural equation modelling (PLS‐SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS‐SEM is that of minimum sample size estimation. The ‘10‐times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum sample size estimation in PLS‐SEM: the inverse square root method, and the gamma‐exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd

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References

1992A power primer.1998Principles and Practice of Structural Equation Modeling 2011PLS-SEM: Indeed a Silver Bullet 2003A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study 1998Issues and opinion on structural equation modeling 2012Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations 2013A Primer on Partial Least Squares Structural Equation Modeling 2015Consistent Partial Least Squares Path Modeling1

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