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On Measuring and Reducing Selection Bias With a Quasi‐Doubly Randomized Preference Trial

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  • Ted Joyce
  • Dahlia K. Remler
  • David A. Jaeger
  • Onur Altindag
  • Stephen D. O'Connell
  • Sean Crockett

Abstract

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.

Suggested Citation

  • Ted Joyce & Dahlia K. Remler & David A. Jaeger & Onur Altindag & Stephen D. O'Connell & Sean Crockett, 2017. "On Measuring and Reducing Selection Bias With a Quasi‐Doubly Randomized Preference Trial," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 36(2), pages 438-459, March.
  • Handle: RePEc:wly:jpamgt:v:36:y:2017:i:2:p:438-459
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    File URL: http://hdl.handle.net/10.1002/pam.21976
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    Cited by:

    1. Strobl, Renate & Wunsch, Conny, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.

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