Limitless Regression Discontinuity
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| Title: | Limitless Regression Discontinuity |
|---|---|
| Language: | English |
| Authors: | Sales, Adam C., Hansen, Ben B. |
| Source: | Journal of Educational and Behavioral Statistics. Apr 2020 45(2):143-174. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 32 |
| Publication Date: | 2020 |
| Sponsoring Agency: | Institute of Education Sciences (ED) National Science Foundation (NSF) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (NIH) |
| Contract Number: | R305B100012 SES0753164 R24HD041028 |
| Document Type: | Journal Articles Reports - Descriptive |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Regression (Statistics), Computation, Statistical Inference, Robustness (Statistics), Randomized Controlled Trials, Public Health, Educational Research, Simulation, Statistical Analysis, Grade Point Average, Foreign Countries, College Students |
| Geographic Terms: | Puerto Rico, Canada |
| DOI: | 10.3102/1076998619884904 |
| ISSN: | 1076-9986 |
| Abstract: | Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment, I[R |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2020 |
| Accession Number: | EJ1247335 |
| Database: | ERIC |
| Abstract: | Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment, I[R<c], is ignorable vis-à-vis potential outcomes. Instead, the method presented in this article assumes "residual ignorability," ignorability of treatment assignment vis-à-vis detrended potential outcomes. Detrending is effected not with ordinary least squares but with MM estimation, following a distinct phase of sample decontamination. The method's inferences acknowledge uncertainty in both of these adjustments, despite its applicability whether "R" is discrete or continuous; it is uniquely robust to leading validity threats facing regression discontinuity designs. |
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| ISSN: | 1076-9986 |
| DOI: | 10.3102/1076998619884904 |
