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Propensity Score Analysis in Non‐Randomized Experimental Designs: An Overview and a Tutorial Using R Software
Author(s) -
Kim Hanjoe
Publication year - 2019
Publication title -
new directions for child and adolescent development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.628
H-Index - 59
eISSN - 1534-8687
pISSN - 1520-3247
DOI - 10.1002/cad.20309
Subject(s) - propensity score matching , analysis of covariance , covariate , equating , statistics , causal inference , randomization , randomized experiment , randomized controlled trial , computer science , econometrics , psychology , mathematics , medicine , rasch model , surgery
Abstract Propensity score analysis is a statistical method that balances pre‐existing differences across treatment conditions achieving a similar condition as randomization and thus, allowing the estimation of causal effects in non‐randomized experimental designs. The four stages in propensity score analysis are (1) propensity score estimation, (2) equating or balancing procedures, (3) balance checking, and (4) outcome analysis. Each stage is explained followed by a step‐by‐step tutorial of applying propensity score analysis to an empirical dataset using R software. Project Achieve concerns grade retention data where the retained and promoted groups were balanced based on 64 baseline covariates. In discussion, some caveats of the propensity score analysis applied to the dataset are discussed with suggestions. A comparison between propensity score analysis and analysis of covariance (ANCOVA) is made and the advantage of using propensity score analysis over ANCOVA is explained. At last, some considerations utilizing propensity score methods in developmental research is discussed.