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

TLDR

Partial least squares‑based structural equation modelling (PLS‑SEM) is widely used in information systems and other fields, yet determining its minimum sample size remains a key challenge, with the commonly used 10‑times rule often yielding imprecise estimates. The study proposes two mathematical methods—the inverse square root and gamma‑exponential—to estimate the minimum sample size for PLS‑SEM. The authors derive closed‑form equations for the inverse square root and gamma‑exponential methods to calculate required sample sizes. Monte Carlo experiments show both methods produce accurate estimates, with the inverse square root method offering particularly simple application. © 2016 John Wiley & Sons Ltd.

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