Identifying Baseline Covariates for Use in Propensity Scores: A Novel Approach Illustrated for a Nonrandomized Study of Recovery High Schools

Abstract

There are many situations where random assignment of participants to treatment and comparison conditions may be unethical or impractical. This article provides an overview of propensity score techniques that can be used for estimating treatment effects in nonrandomized quasi-experimental studies. After reviewing the logic of propensity score methods, we call attention to the importance of the strong ignorability assumption and its implications. We then discuss the importance of identifying and measuring a sufficient set of baseline covariates upon which to base the propensity scores and illustrate approaches to that task in the design of a study of recovery high schools for adolescents treated for substance abuse. One novel approach for identifying important covariates that we suggest and demonstrate is to draw on the predictor-outcome correlations compiled in meta-analyses of prospective longitudinal correlations.

Additional information

Notes on contributors

Emily E. Tanner-Smith

Emily E. Tanner-Smith is a Research Assistant Professor at the Peabody Research Institute and Department of Human and Organizational Development at Vanderbilt University. Her broad areas of expertise include the social epidemiology, prevention, and treatment of adolescent substance use. Her recent research appears in the Oxford Handbook of Criminological Theory, Journal of Substance Abuse Treatment, Prevention Science, and Research Synthesis Methods.

Mark W. Lipsey

Mark W. Lipsey is the Director of the Peabody Research Institute and a Research Professor at Vanderbilt University. He specializes in program evaluation with a focus on programs for at-risk children and youth.