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

Youmi Suk, Peter M. Steiner, Jee-Seon Kim and Hyunseung Kang
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Youmi Suk: School of Data Science, University of Virginia, Charlottesville, VA, USA
Peter M. Steiner: Department of Human Development and Quantitative Methodology, University of Maryland–College Park, MD, USA
Jee-Seon Kim: Department of Educational Psychology, University of Wisconsin–Madison, WI, USA
Hyunseung Kang: Department of Statistics, University of Wisconsin–Madison, WI, USA

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 4, 459-484

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.

Keywords: regression discontinuity designs; causal inference; testing accommodations; NAEP; observational studies (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:4:p:459-484

DOI: 10.3102/10769986221090275

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