Investigating the accuracy of three estimation methods for regression discontinuity design
Regression discontinuity design is an alternative to randomized experiments to make causal inference when random assignment is not possible. This article first presents the formal identification and estimation of regression discontinuity treatment effects in the framework of Rubin's causal model, followed by a thorough literature review of three major methods for estimating regression discontinuity effects. The authors conducted a Monte Carlo simulation to compare the accuracy of 3 estimation methods and evaluate the effects of sample sizes, cutoff score locations, and distribution assumptions on the accuracy of parameter estimates. Although all 3 methods can produce reasonably accurate parameter estimates under various manipulated data conditions, extreme cutoff scores tend to introduce large parameter estimate biases when the variance of the outcome variable differs across groups. Implications and directions for further research are discussed. © 2013 Taylor and Francis Group, LLC.
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Related Subject Headings
- Education
- 5201 Applied and developmental psychology
- 3904 Specialist studies in education
- 3903 Education systems
- 1303 Specialist Studies in Education
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Education
- 5201 Applied and developmental psychology
- 3904 Specialist studies in education
- 3903 Education systems
- 1303 Specialist Studies in Education