Affiliation:
1. University of North Carolina - Chapel Hill
2. ADIYAMAN UNIVERSITY
Abstract
Multilevel regression discontinuity designs have been increasingly used in education research to evaluate the effectiveness of policy and programs. It is common to ignore a level of nesting in a three-level data structure (students nested in classrooms/teachers nested in schools), whether unwittingly during data analysis or due to resource constraints during the planning phase. This study investigates the consequences of ignoring intermediate or top level in blocked three-level regression discontinuity designs (BIRD3; treatment is at level 1) during data analysis and planning. Monte Carlo simulation results indicated that ignoring a level during analysis did not affect the accuracy of treatment effect estimates; however, it affected the precision (standard errors, power, and Type I error rates). Ignoring the intermediate level did not cause a significant problem. Power rates were slightly underestimated, whereas Type I error rates were stable. In contrast, ignoring a top-level resulted in overestimated power rates; however, severe inflation in Type I error deemed this strategy ineffective. As for the design phase, when the intermediate level was ignored, it is viable to use parameters from a two-level blocked regression discontinuity model (BIRD2) to plan a BIRD3 design. However, level 2 parameters from the BIRD2 model should be substituted for level 3 parameters in the BIRD3 design. When the top level was ignored, using parameters from the BIRD2 model to plan a BIRD3 design should be avoided.
Reference40 articles.
1. Balu, R., Zhu, P., Doolittle, F., Schiller, E., Jenkins, J., & Gersten, R. (2015). Evaluation of response to intervention practices for elementary school reading (NCEE 2016-4000). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. https://files.eric.ed.gov/fulltext/ED560820.pdf
2. Bickel, R. (2007). Multilevel analysis for applied research: It's just regression! Guilford Press.
3. Bulus, M. (2022). Minimum detectable effect size computations for cluster-level regression discontinuity: Specifications beyond the linear functional form. Journal of Research on Education Effectiveness, 15(1), 151-177. https://doi.org/10.1080/19345747.2021.1947425
4. Bulus, M., & Dong, N. (2021a). Bound constrained optimization of sample sizes subject to monetary restrictions in planning of multilevel randomized trials and regression discontinuity studies. The Journal of Experimental Education, 89(2), 379-401. https://doi.org/10.1080/00220973.2019.1636197
5. Bulus, M., & Dong, N. (2021b). cosa: Bound constrained optimal sample size allocation. R package version 2.1.0. https://CRAN.R-project.org/package=cosa