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Efficient Exploration Of Many Variables And Interactions Using Regularized Regression, Tyson S. Barrett, Ginger Lockhart

Psychology Faculty Publications

The prevention sciences often face several situations that can compromise the statistical power and validity of a study. Among these, research can (1) have data with many variables, sometimes with low sample sizes, (2) have highly correlated predictors, (3) have unclear theory or empirical evidence related to the research questions, and/or (4) have difficulty selecting the proper covariates in observational studies. Modeling in these situations is difficult—and at times impossible—with conventional methods. Fortunately, regularized regression—a machine learning technique—can aid in exploring datasets that are otherwise difficult to analyze, allowing researchers to draw insights from these data. Although many of these …