Why You Shouldn't Use PLS: Four Reasons to Be Uneasy about Using PLS in Analyzing Path Models

Abstract

When it was originally introduced, Partial Least Squares (PLS) was designed primarily for exploratory studies focusing on prediction (rather than hypothesis testing). Over time, however, PLS has become a very popular statistical analysis technique for testing hypothesized relationships (confirmatory studies) within MIS research. In this paper we note some challenges that have been raised relative to PLS, and then focus on four assertions concerning its use that we believe are problematic. We show that these frequently stated assertions of PLS strength actually suggest important weaknesses. In particular, we show evidence that PLS seems to capitalize on chance correlations among indicators at sample sizes typically used in MIS research. Our belief is that many PLS users are unaware of the trade-offs involved in PLS use, and we recommend a much reduced (or possibly non-existent) role for PLS in confirmatory work.