COMPARING REGRESSION, PLS, AND LISREL USING AMONTE CARLO SIMULATION

摘要

This study uses Monte Carlo simulation to compare three relatively popular techniques, with findings that runrncounter to extant suggestions in MIS literature. We compare multiple regression, Partial Least Squares andrnLISREL under varying sample sizes (N = 40, 90, 150, and 200) and varying effect sizes (large, medium, smallrnand no effect). The focus of the analysis was on determining how frequently Type I errors (non-existent effectrnerroneously detected) and Type II errors (true effect not detected) occur for each combination of technique,rnsample size, and effect size. In addition, we tested the efficacy of bootstrapping versus jackknifing for the testrnof statistical significance of parameter estimates using PLS. Initial results, using very simple models andrnnicely behaved and normally distributed data, suggest that: (1) PLS with jackknifing (to test statisticalrnsignificance) has unacceptably low power (too many Type II errors) except with a strong effect size; and (2)rnat small sample size and medium or small effect size, all techniques (including PLS, which is reputed to bernstrong in this realm) have unacceptably low power.