Social science researchers need to use modeling to understand complex real-life phenomena. But how does a researcher decide which of the available models is most appropriate? In this video, MARCO SARSTEDT analyzes the metrics employed by researchers in assessing PLS (Partial Least Squares) models, outlining how such assessments can be optimized. Running a Monte Carlo simulation study, Sarstedt explains the inadequacies (for PLS researchers) of commonly used metrics like R² and the Goodness of Fit index by comparison with information criteria like BIC and GM. Offering suggestions as to how these metrics should ideally be implemented, Sarstedt notes that further work is required to assess whether their advantages extend to studies which employ more complex modeling.