Sequential Blocked Randomization for Internet-Based Survey Experiments

Aaron Kaufman and Matthew Kim

Abstract: It has been long-established that completely randomized experiments are generally less efficient than block randomized experiments, in that the latter can produce lower standard errors and narrower confidence intervals. However, in many practical settings, the application of block randomization has been infeasible. In medical trials, for example, patients often need to be assigned to a treatment condition the moment they enter the hospital. We introduce a platform to design and proliferate block randomized online survey experiments in which respondents arrive sequentially. Written in \texttt{R Shiny}, the platform offers a powerful and flexible framework for blocking on an arbitrary set of covariates and for implementing sophisticated modes of control flow, substantially improving researchers’ abilities to answer complex behavioral questions while minimizing respondent needs.