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

Author

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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)

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.

Suggested Citation

  • Youmi Suk & Peter M. Steiner & Jee-Seon Kim & Hyunseung Kang, 2022. "Regression Discontinuity Designs With an Ordinal Running Variable: Evaluating the Effects of Extended Time Accommodations for English-Language Learners," Journal of Educational and Behavioral Statistics, , vol. 47(4), pages 459-484, August.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:4:p:459-484
    DOI: 10.3102/10769986221090275
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