Publication Date
In 2025 | 2 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 4 |
Descriptor
Causal Models | 4 |
Randomized Controlled Trials | 4 |
Computation | 2 |
Intervention | 2 |
Simulation | 2 |
Statistical Bias | 2 |
Statistical Inference | 2 |
Athletes | 1 |
Control Groups | 1 |
Correlation | 1 |
Difficulty Level | 1 |
More ▼ |
Source
Journal of Educational and… | 4 |
Author
Publication Type
Journal Articles | 4 |
Reports - Research | 4 |
Education Level
Early Childhood Education | 1 |
Elementary Education | 1 |
Grade 2 | 1 |
High Schools | 1 |
Primary Education | 1 |
Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peter Z. Schochet – Journal of Educational and Behavioral Statistics, 2025
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…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
Joshua B. Gilbert; Luke W. Miratrix; Mridul Joshi; Benjamin W. Domingue – Journal of Educational and Behavioral Statistics, 2025
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.…
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics
Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Hasegawa, Raiden B.; Deshpande, Sameer K.; Small, Dylan S.; Rosenbaum, Paul R. – Journal of Educational and Behavioral Statistics, 2020
Causal effects are commonly defined as comparisons of the potential outcomes under treatment and control, but this definition is threatened by the possibility that either the treatment or the control condition is not well defined, existing instead in more than one version. This is often a real possibility in nonexperimental or observational…
Descriptors: Causal Models, Inferences, Randomized Controlled Trials, Experimental Groups