Prior research has suggested that clustered regression discontinuity designs are a formidable alternative to cluster randomized designs because they provide targeted treatment assignment while maintaining a high-quality basis for inferences on local treatment effects. However, methods for the design and analysis of clustered regression…

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…

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental…

Multisite trials that randomize individuals (e.g., students) within sites (e.g., schools) or clusters (e.g., teachers/classrooms) within sites (e.g., schools) are commonly used for program evaluation because they provide opportunities to learn about treatment effects as well as their heterogeneity across sites and subgroups (defined by moderating…

Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…

Assessing student achievement over multiple years is complicated by students' annual matriculation through different classrooms. The process of matriculation, or annual classroom change, threatens the validity of statistical inferences because it violates the independence of observations necessary in a regression context. The current study…

The article focuses on estimating effects in nonrandomized studies with two outcome measurement occasions and one predictor variable. Given such a design, the analysis approach can be to include the measurement at the previous time point as a predictor in the regression model (ANCOVA), or to predict the change-score of the outcome variable…

This paper's objective was to teach the Equivalence Testing applied to Educational Research to emphasize recommendations and to increase quality of research. Equivalence Testing is a technique used to compare effect sizes or means of two different studies to ascertain if they would be statistically equivalent. For making accessible Equivalence…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

Evidence-based education aims to support policy makers choosing between potential interventions. This rarely involves considering each in isolation; instead, sets of evidence regarding many potential policy interventions are considered. Filtering a set on any quantity measured with error risks the "winner's curse": conditional on…

Published research in scientific journals are critical resources for researchers as primary sources about: what is important in the field, the direction the field is headed, how the field relates to other sciences, and as a historical record for each of these. In this exploratory study, we analyzed the articles of five behavior analytic journals…

Despite growing calls to develop data science students' ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science…

Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the…

Propensity and prognostic score methods are two statistical techniques used to correct for the selection bias in nonexperimental studies. Recently, the joint use of propensity and prognostic scores (i.e., two-score methods) has been proposed to improve the performance of adjustments using propensity or prognostic scores alone for bias reduction.…

A priori power analyses allow researchers to estimate the number of participants needed to detect the effects of an intervention. However, power analyses are only as valid as the parameter estimates used. One such parameter, the expected effect size, can vary greatly depending on several study characteristics, including the nature of the…