Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of…

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing…

Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature…

Individual participant data (IPD) meta-analysis projects obtain, harmonise, and synthesise original data from multiple studies. Many IPD meta-analyses of randomised trials are initiated to identify treatment effect modifiers at the individual level, thus requiring statistical modelling of interactions between treatment effect and participant-level…

There is a considerable gap between many of the findings from educational psychology research and educational practice. This gap is especially notable in the field of science education. In this article, the implications of three categories of research and their findings for science educational policy in the USA and other jurisdictions were…

In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…

In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…

Randomised controlled trials of cancer treatments typically report progression free survival (PFS) and overall survival (OS) outcomes. Existing methods to synthesise evidence on PFS and OS either rely on the proportional hazards assumption or make parametric assumptions which may not capture the diverse survival curve shapes across studies and…

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also…

Background: Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs)…

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.…

Analyzing heterogeneous treatment effects (HTE) 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 pre-intervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

Purpose: Key considerations in designing multilevel experimental studies are to efficiently use resources and to determine the sample size allocation such that designs have adequate statistical power. The utility of optimal sampling and power analysis results depends on accurate information about design parameters, such as intraclass correlations…