Minimum sample size estimation in PLS-SEM: The inverse ...

1 Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods Ned Kock Pierre Hadaya Full reference: Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS SEM: The inverse square root and gamma exponential methods. Information Systems Journal, 28(1), 227 261. Abstract Partial least squares-based structural equation modeling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are employed. One of the most fundamental issues in PLS-S E M is that of Minimum sample size estimation . The 10-times rule has been a favorite due to 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.

thus being a more conservative effect size estimate. By convention, effect sizes of 0.02, 0.15, and 0.35 are respectively termed small, medium, and large (Cohen, 1992; Kock, 2014b). The minimum sample size at which a PLS-SEM test achieves an acceptable level of power

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