Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences

Abstract

Ordinary least squares and stepwise selection are widespread in behavioral science research; however, these methods are well known to encounter overfitting problems such that R² and regression coefficients may be inflated while standard errors and p values may be deflated, ultimately reducing both the parsimony of the model and the generalizability of conclusions. More optimal methods for selecting predictors and estimating regression coefficients such as regularization methods (e.g., Lasso) have existed for decades, are widely implemented in other disciplines, and are available in mainstream software, yet, these methods are essentially invisible in the behavioral science literature while the use of sub optimal methods continues to proliferate. This paper discusses potential issues with standard statistical models, provides an introduction to regularization with specific details on both Lasso and its related predecessor ridge regression, provides an example analysis and code for running a Lasso analysis in R and SAS, and discusses limitations and related methods.