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

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.

Keywords: Selection bias; Experiments; Education; Online learning.