On measuring and reducing selection bias with a quasi-doubly randomized preference trial

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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    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. (C) 2017 by the Association for Public Policy Analysis and Management.

    Original languageEnglish
    Pages (from-to)438-459
    Number of pages27
    JournalJournal of Policy Analysis and Management
    Volume36
    Issue number2
    Early online date7 Feb 2017
    DOIs
    Publication statusPublished - Mar 2017

    Keywords

    • Experiment comparing random
    • Yield accurate answers
    • Intervention trials
    • Training-programs

    Fingerprint

    Dive into the research topics of 'On measuring and reducing selection bias with a quasi-doubly randomized preference trial'. Together they form a unique fingerprint.

    Cite this