Comparison of Lasso and Stepwise Regression in Psychological Data

Authors

  • Di Jody Zhou Orcid
    Department of Human Ecology, University of California, Davis, CA, USA
  • Rajpreet Chahal Orcid
    Department of Psychology, Stanford University, Stanford, CA, USA
  • Ian H. Gotlib Orcid
    Department of Psychology, Stanford University, Stanford, CA, USA
  • Siwei Liu Orcid
    Department of Human Ecology, University of California, Davis, CA, USA

Abstract

Identifying significant predictors of behavioral outcomes is of great interest in many psychological studies. Lasso regression, as an alternative to stepwise regression for variable selection, has started gaining traction among psychologists. Yet, further investigation is valuable to fully understand its performance across various psychological data conditions. Using a Monte Carlo simulation and an empirical demonstration, we compared Lasso regression to stepwise regression in typical psychological datasets varying in sample size, predictor size, sparsity, and signal-to-noise ratio. We found that: (1) Lasso regression was more accurate in within-sample selection and yielded more consistent out-of-sample prediction accuracy than stepwise regression; (2) Lasso with a harsher shrinkage parameter was more accurate, parsimonious, and robust to sampling variability than the prediction-optimizing Lasso. Finally, we concluded with cautious notes and recommendations in practice on the application of Lasso regression.