Abstract
Propensity score matching and weighting methods are often used in observational effectiveness studies to reduce imbalance between treated and untreated groups on a set of potential confounders. However, much of the prior methodological literature on matching and weighting has yet to examine performance for scenarios with a majority of treated units, as is often encountered with programs and interventions that have been widely disseminated or “scaled-up.” Using a series of Monte Carlo simulations, we compare the performance of k:1 matching with replacement and weighting methods with respect to covariate balance, bias, and mean squared error. Results indicate that the accuracy of all methods declined as treatment prevalence increased. While weighting produced the largest reduction in covariate imbalance, 1:1 matching with replacement provided the most unbiased treatment effect estimates. An applied example using empirical school-level data is provided to further illustrate the application and interpretation of these methods to a real-world scale-up effort. We conclude by considering the implications of propensity score methods for observational effectiveness studies with a particular focus on educational research.
Open Research Statements
Study and Analysis Plan Registration
There is no registration associated with the study reported in this manuscript.
Data, Code, and Materials Transparency
The code that support the findings of this study are openly available at: https://github.com/jmk7cj/Covariate-Balance.
Design and Analysis Reporting Guidelines
Not applicable.
Transparency Declaration
The lead author (the manuscript's guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Replication Statement
This manuscript reports an original study.
Acknowledgments
We thank the Maryland PBIS Management Team, which includes the Maryland State Department of Education, Sheppard Pratt Health System, and the 24 local school districts. We would also like to give special thanks to Drs. Ji Hoon Ryoo and Elizabeth Stuart for providing feedback on the article and for their methodological consultation.