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Prevention Science



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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 methods have existed for several decades, prevention researchers rarely use them. As a gentle introduction, we discuss the utility of regularized regression to the field of prevention science and apply the technique to a real dataset. The data (n = 7979) for the demonstration consisted of 76 variables (151 including the modeled interactions) from the Youth Risk-Behavior Surveillance System (YRBSS) from 2015. Overall, it is clear that regularized regression can be an important tool in analyzing and gaining insight from data in the prevention sciences.


This is a post-peer-review, pre-copyedit version of an article published in Prevention Science. The final authenticated version is available online at:

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