Growing literature documents the promise of active learning instruction in engaging students in college classrooms. Accordingly, faculty professional development (PD) programs on active learning have become increasingly popular in postsecondary institutions; yet, quantitative evidence on the effectiveness of these programs is limited. Using…

Randomized evaluations of educational technology produce log data as a bi-product: highly granular data on student and teacher usage. These datasets could shed light on causal mechanisms, effect heterogeneity, or optimal use. However, there are methodological challenges: implementation is not randomized and is only defined for the treatment group,…

The arc of quantitative educational research should not be etched in stone but should adapt and change over time. In this article, I argue that it is time for a reshaping by offering my personal view of the past, present and future of our field. Educational research--and research in the social and life sciences--is at a crossroads. There are many…

Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative…

The authors reflect on findings from three studies of different approaches to reading intervention (Al Otaiba et al., Denton et al., and Miller et al., all found in this issue). It is argued that the science of interventions for reading disorders is advanced and that these and other related studies provide a strong evidence base for guiding…

A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the…

In this commentary, the author focuses on the use of design elements for increasing the severity of causal mediation tests. The estimation of causal mediation effects from observational data rests on rather stringent assumptions. In introducing and exemplifying ratio-of-mediator-probability weighting (RMPW), Hong and Nomi (henceforth HN) make…

The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…

Experimental evaluations are increasingly common in the U.S. educational policy-research context. Often, in investigations of multifaceted interventions, researchers and policymakers alike are interested in not only "whether" a given intervention impacted an outcome but also "why". What "features" of the intervention…

Some form of a short interrupted time series (ITS) is often used to evaluate state and national programs. An ITS design with a single treatment group assumes that the pretest functional form can be validly estimated and extrapolated into the postintervention period where it provides a valid counterfactual. This assumption is problematic. Ambiguous…

Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…