Minimum Detectable Effect Size Computations for Cluster-Level Regression Discontinuity Studies: Specifications beyond the Linear Functional Form

Autor/in	Bulus, Metin
Titel	Minimum Detectable Effect Size Computations for Cluster-Level Regression Discontinuity Studies: Specifications beyond the Linear Functional Form
Quelle	In: Journal of Research on Educational Effectiveness, 15 (2022) 1, S.151-177 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Bulus, Metin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2021.1947425
Schlagwörter	Effect Size; Computation; Regression (Statistics); Statistical Analysis; Sample Size + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Statistische Analyse
Abstract	Although Cattaneo et al. (2019) provided a data-driven framework for power computations for Regression Discontinuity Designs in line with rdrobust Stata and R commands, which allows higher-order functional forms for the score variable when using the non-parametric local polynomial estimation, analogous advancements in their parametric estimation have been lagging. This study extends power formulas for Cluster-level Regression Discontinuity (CRD) designs beyond the linear form specification proposed by Schochet (2009). Results reveal that, with symmetric truncation intervals and symmetric distributions, a linear form, linear form with interactions, or quadratic form all require the same sample size. However, quadratic form with interactions requires larger sample sizes to reach the same level of precision as the other functional forms. In comparison, with asymmetric truncation intervals or asymmetric distributions, even a slight change in the functional form can alter sample size requirements. CRD design effects are usually inflated when interactions are introduced, but they hardly vary as a function of treatment group sampling rate, especially with the truncated normal or uniform distributions. Formulas and the extended empirical framework are implemented in the cosa R package and the companion web application. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01