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Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English-Language Learners

Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of... Regression discontinuity (RD) designs are commonly used for program evaluation with continuous treatment assignment variables. But in practice, treatment assignment is frequently based on ordinal variables. In this study, we propose an RD design with an ordinal running variable to assess the effects of extended time accommodations (ETA) for English-language learners (ELLs). ETA eligibility is determined by ordinal ELL English-proficiency categories of National Assessment of Educational Progress data. We discuss the identification and estimation of the average treatment effect (ATE), intent-to-treat effect, and the local ATE at the cutoff. We also propose a series of sensitivity analyses to probe the effect estimates’ robustness to the choices of scaling functions and cutoff scores and remaining confounding. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Educational and Behavioral Statistics SAGE

Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English-Language Learners

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References (41)

Publisher
SAGE
Copyright
© 2022 AERA
ISSN
1076-9986
eISSN
1935-1054
DOI
10.3102/10769986221090275
Publisher site
See Article on Publisher Site

Abstract

Regression discontinuity (RD) designs are commonly used for program evaluation with continuous treatment assignment variables. But in practice, treatment assignment is frequently based on ordinal variables. In this study, we propose an RD design with an ordinal running variable to assess the effects of extended time accommodations (ETA) for English-language learners (ELLs). ETA eligibility is determined by ordinal ELL English-proficiency categories of National Assessment of Educational Progress data. We discuss the identification and estimation of the average treatment effect (ATE), intent-to-treat effect, and the local ATE at the cutoff. We also propose a series of sensitivity analyses to probe the effect estimates’ robustness to the choices of scaling functions and cutoff scores and remaining confounding.

Journal

Journal of Educational and Behavioral StatisticsSAGE

Published: Aug 1, 2022

Keywords: regression discontinuity designs; causal inference; testing accommodations; NAEP; observational studies

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