PLS-SEM-power: A Shiny App and R package for Computing Required Sample Size and Minimum Detectable Effect Size in PLS-SEMs

TL;DR

This paper introduces PLS-SEM-power, a user-friendly Shiny app and R package that calculates required sample size and MDES for PLS-SEM studies, enhancing power analysis accessibility.

Contribution

It implements the inverse square root method for power analysis in PLS-SEM, providing an intuitive tool for researchers to perform a priori and sensitivity analyses.

Findings

Provides an accessible tool for power analysis in PLS-SEM

Enables reproducible sample size and effect size calculations

Facilitates better planning of PLS-SEM studies

Abstract

Despite its evanescent nature, statistical power is crucial for planning Partial Least Squares Structural Equation Modelling (PLS-SEM) studies. This brief paper introduces PLS-SEM-power, a Shiny Application and R package that implements the inverse square root method by Kock and Hadaya (2018) to calculate both the minimum required sample size (a priori analysis) and the Minimum Detectable Effect Size (MDES, sensitivity analysis), given a chosen significance level (alpha level) at 80% power (1 - beta). The application provides an intuitive user interface, facilitating reproducible and easily accessible analyses in diverse research contexts.