Propensity Score Matching for Education Data: Worked Examples
Abstract
Randomized controlled trials are not always feasible in educational research, so researchers must use alternative methods to study treatment effects. Propensity score matching is one such method for observational studies that has shown considerable growth in popularity since it was first introduced in the early 1980s. This paper outlines the concept of propensity scores by explaining their theoretical principles and providing two examples of their usefulness within the realm of educational research. Through worked examples, we highlight the effectiveness of propensity scores as a method for reducing bias and increasing the balance between treatment and comparison groups. To aid in the understanding and future use of propensity scores, we provide R syntax for all our analyses.
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Matched sampling analyses can be improved by using covariates not affected by bridge participation to build propensity scores that reduce bias from confounding variables.
“Matched sampling analyses can be improved by using covariates that are not affected by a student participating in the bridge program (e.g., students’ demographic backgrounds, high school preparation) to build propensity scores, which attempt to match students on these covariates and reduce bias produced by confounding variables (Powell et al., 2020).”
From A Meta-analysis of University STEM Summer Bridge Program Effectiveness (2021) · cites this paper as Powell et al., 2020
Cites
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A Meta-analysis of University STEM Summer Bridge Program Effectiveness
Bradford, B. C., Beier, M. E., & Oswald, F. L. (2021) · CBE—Life Sciences Education · doi:10.1187/cbe.20-03-0046 · Cited by 73 · see citing works
Wladis, C., Sencindiver, B., Hachey, A. C., Myszkowski, N., Offenholley, K., Samuels, J., & Aly, G. (2026) · AERA Open · doi:10.1177/23328584261419497 · Cited by 1 · see citing works
- Authors
- Powell, M. G., Hull, D. M., & Beaujean, A. A.
- Affiliations
- George Mason University (US)
- University of North Texas (US)
- Baylor University (US)
- Corresponding author
- Powell, Marvin G.
- Venue
- The Journal of Experimental Education
- Publisher
- Informa UK Limited
- Published
- 2019-01-07
- Volume
- 88
- Issue
- 1
- Pages
- 145-164
- ISSN
- 0022-0973 (ISSN-L)
- 1940-0683
- OpenAlex
- W2910169595
- Cited by
- 40
- References
- 8
- Citations by year
- 2026: 11 · 2025: 8 · 2024: 5 · 2023: 5 · 2022: 3 · 2021: 6 · 2020: 2
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Experimental Education (publishedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Education › School Choice and Performance
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- propensity score matching, propensity score, treatment effect estimation, observational studies, selection bias, educational intervention, R code
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