In this response, we first show that Simpson's proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.

A recent paper uses Bayes factors to argue a large minority of rigorous, large-scale education RCTs are "uninformative." The definition of "uninformative" depends on the authors' hypothesis choices for calculating Bayes factors. These arguably overadjust for effect size inflation and involve a fixed prior distribution,…

The time, material, and staff-consuming nature of anatomy's traditional pen-and-paper assessment system, the increase in the number of students enrolling in medical schools and the ever-escalating workload of academic staff have made the use of computer-based assessment (CBA) an attractive proposition. To understand the impact of such shift in the…

Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation not explained by observed covariates. We propose a model-free approach for testing for the presence of such unexplained variation. To use this randomization-based approach, we must address the fact that the…

In this paper we present a mobile application for self-assessment. The work describes the main features of the application and focuses on its acceptance by students and the increase on their learning, through its usage in real testing settings. The application supports the retrieval of questions based on a number of criteria and it was evaluated…