A Comparison of PLS and ML Bootstrapping Techniques in SEM: A Monte Carlo Study

Abstract

Structural Equation Modeling (SEM) techniques have been extensively used in business and social science research to model complex relationships. The two most widely used estimation methods in SEM are the Maximum Likelihood (ML) and Partial Least Square (PLS). Both the estimation methods rely on Bootstrap re-sampling to a large extent. While PLS relies completely on Bootstrapping to obtain standard errors for hypothesis testing, ML relies on Bootstrapping under conditions in violation of the distributional assumptions. Even though Bootstrapping has several advantages, it may fail under certain conditions. In this Monte Carlo study, we compare the accuracy and efficiency of ML and PLS based Bootstrapping in SEM, while recovering the true estimates under various conditions of sample size and distributional assumptions. Our results suggest that researchers might benefit by using PLS based bootstrapping with smaller sample sizes. However, at larger sample sizes the use of ML based bootstrapping is recommended.