Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

There are some educational interventions aimed at changing the ways in which individuals interact, and social networks are particularly useful for quantifying these changes. For many of these interventions, the ultimate goal is to change some outcome of interest such as teacher quality or student achievement, and social networks act as a natural…

In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a period of substantive interest relate to differences in subsequent change. In this article, the authors present a fully Bayesian approach to estimating three-level Hierarchical Models in which latent…

An analytical approach was employed to compare sensitivity of causal effect estimates with different assumptions on treatment noncompliance and non-response behaviors. The core of this approach is to fully clarify bias mechanisms of considered models and to connect these models based on common parameters. Focusing on intention-to-treat analysis,…

Examined alternative ways of specifying models in the complier average causal effects (CACE) estimation method when the major interest is in estimating causal effects of treatments for compliers. Explored modeling possibilities of CACE estimation in a maximum likelihood-expectation maximization framework in the presence of covariate information.…

Notes that the article reviewed contributes to the expanding literature on noncompliance by explicating the assumptions involving covariates that can be used to identify maximum likelihood estimates in place of exclusion restrictions. Notes points that require further discussion. (SLD)

A family of finite mixture distribution models is presented that allows specification of basically different developmental processes in distinct latent subpopulations. These models are introduced within the framework of mixed latent Markov chains with multiple indicators per occasion, and they are illustrated with empirical data on therapeutic…

Intervention studies often rely on microcoded data of social interactions to provide evidence of change due to development or treatment. Traditionally these data have been collapsed into small contingency tables. Such an approach can introduce spurious findings. Instead of treating each unit's contingency table independently, or collapsing the…

In group-allocation studies for comparing behavioral, social, or educational interventions, subjects in the same group necessarily receive the same treatment, whereby a group and/or group-dynamic effect can confound the treatment effect. General counterfactual outcomes that depend on group characteristics, group membership, and treatment are…