Statistical Power for Regression Discontinuity Designs in Education Evaluations

This article examines theoretical and empirical issues related to the statistical power of impact estimates under clustered regression discontinuity (RD) designs. The theory is grounded in the causal inference and hierarchical linear modeling literature, and the empirical work focuses on common desi...

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Bibliographic Details
Published in:Journal of educational and behavioral statistics 2009-06, Vol.34 (2), p.238-266
Main Author: Schochet, Peter Z.
Format: Article
Language:English
Subjects:
Achievement Tests
Classroom design
Control Groups
Design
Design analysis
Design evaluation
Disadvantaged Schools
Educational Environment
Educational Research
Elementary School Students
Estimators
Evaluation
Gaussian distributions
Inference
Intervention
Mathematical discontinuity
Preschool Children
Regression (Statistics)
Research Design
Sample Size
Scores
Standard deviation
Standardized Tests
Statistical Analysis
Statistical variance
Statistics
Students
Studies
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Summary:This article examines theoretical and empirical issues related to the statistical power of impact estimates under clustered regression discontinuity (RD) designs. The theory is grounded in the causal inference and hierarchical linear modeling literature, and the empirical work focuses on common designs used in education research to test intervention effects on student test scores. The main conclusion is that three to four times larger samples are typically required under RD than experimental clustered designs to produce impacts with the same level of statistical precision. Thus, the viability of using RD designs for new impact evaluations of educational interventions may be limited and will depend on the point of treatment assignment, the availability of pretests, and key research questions.
ISSN:1076-9986
1935-1054
DOI:10.3102/1076998609332748