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 .

Power, effect size, and minimum sample size Statistical power (Cohen, 1988; 1992; Goodhue et al., 2012; Kock, 2016; Muthén & Muthén, 2002), often referred to simply as “power”, is a statistical test’s probability of avoiding type II errors, or false negatives. Power is often estimated for a particular coefficient of association and

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  Power, Statistical, Enoch, Statistical power

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