On Measuring and Reducing Selection Bias With a Quasi-Doubly Randomized Preference Trial
| Autor: | Sean Crockett, David A. Jaeger, Dahlia K. Remler, Onur Altindag, Stephen D. O'Connell, Ted Joyce |
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| Rok vydání: | 2017 |
| Předmět: |
Selection bias
Public Administration Sociology and Political Science Computer science Randomized experiment media_common.quotation_subject 05 social sciences Confounding Psychological intervention 01 natural sciences General Business Management and Accounting Preference law.invention 010104 statistics & probability Randomized controlled trial law 0502 economics and business Statistics Public university Econometrics Observational study 050207 economics 0101 mathematics media_common |
| Zdroj: | Journal of Policy Analysis and Management. 36:438-459 |
| ISSN: | 0276-8739 |
| DOI: | 10.1002/pam.21976 |
| Popis: | Randomized experiments provide unbiased estimates of treatment effects, but are costly and time consuming. We demonstrate how a randomized experiment can be leveraged to measure selection bias by conducting a subsequent observational study that is identical in every way except that subjects choose their treatment—a quasi-doubly randomized preference trial (quasi-DRPT). Researchers first strive to think of and measure all possible confounders and then determine how well these confounders as controls can reduce or eliminate selection bias. We use a quasi-DRPT to study the effect of class time on student performance in an undergraduate introductory microeconomics course at a large public university, illustrating its required design elements: experimental and choice arms conducted in the same setting with identical interventions and measurements, and all confounders measured prospectively to treatment assignment or choice. Quasi-DRPTs augment randomized experiments in real-world settings where participants choose their treatments. |
| Databáze: | OpenAIRE |
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