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Can Propensity Score Analysis Approximate Randomized Experiments Using Pretest and Demographic Information in Pre-K
11 Department of Educational, School, & Counseling Psychology, College of Education, University of Missouri, Columbia, MO, USA.
Evaluation Review
|January 3, 2018
Summary
Propensity score analysis (PSA) using pretest and demographic data approximates randomized experiment results for pre-Kindergarten (pre-K) treatment effects on achievement. While effect sizes varied, PSA generally matched benchmark findings on key equivalence criteria.
Area of Science:
- Educational Research
- Statistical Analysis
- Early Childhood Education
Background:
- Uncertainty exists regarding propensity score analysis (PSA) meeting ignorability assumptions for replicating randomized experiments.
- Assessing PSA's validity in educational settings is crucial for causal inference.
Purpose of the Study:
- To evaluate if PSA, using pretest and demographic covariates, can replicate pre-Kindergarten (pre-K) treatment effects found in randomized experiments.
- To assess the approximation of pre-K achievement outcome effects estimated by PSA compared to randomized trials.
Main Methods:
- Utilized data from four pre-K studies with math achievement measures, pretests, and demographic variables.
- Employed within-study comparisons, substituting PSA for randomized controls to reestimate effects.
- Evaluated correspondence between PSA and benchmark estimates using multiple criteria.
Main Results:
- PSA effect estimates showed the same direction and similar statistical significance as benchmark estimates.
- Magnitudes of effect sizes differed, with some bias noted, but formal tests for equivalence were not definitive due to power limitations.
- PSA estimates did not statistically differ from randomized experiment benchmarks.
Conclusions:
- Treatment effect estimates from PSA using pretest and demographic covariates generally correspond to randomized experiment findings.
- PSA offers a viable approach for approximating causal effects in educational research when randomization is not feasible.
- Further research with larger sample sizes may strengthen conclusions on effect size equivalence.
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