Publication Date
In 2025 | 0 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 6 |
Since 2006 (last 20 years) | 7 |
Descriptor
Source
Grantee Submission | 2 |
Educational Research and… | 1 |
Educational Researcher | 1 |
Journal of Statistics… | 1 |
Open Review of Educational… | 1 |
Society for Research on… | 1 |
Author
Balsai, Michael | 2 |
Cromley, Jennifer | 2 |
Dai, Ting | 2 |
Kaplan, Avi | 2 |
Mara, Kyle | 2 |
Perez, Tony | 2 |
Adams, Bryan | 1 |
Clark, Nicholas | 1 |
Cope, Bill | 1 |
Cummiskey, Kevin | 1 |
Joyce, Kathryn E. | 1 |
More ▼ |
Publication Type
Reports - Evaluative | 7 |
Journal Articles | 4 |
Opinion Papers | 2 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peter Schochet – Society for Research on Educational Effectiveness, 2024
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…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design

Kenneth A. Frank; Qinyun Lin; Spiro J. Maroulis – Grantee Submission, 2024
In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…
Descriptors: Causal Models, Statistical Inference, Observation, Educational Policy
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Educational Researcher, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Grantee Submission, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
Cummiskey, Kevin; Adams, Bryan; Pleuss, James; Turner, Dusty; Clark, Nicholas; Watts, Krista – Journal of Statistics Education, 2020
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable…
Descriptors: Statistics, Teaching Methods, Inferences, Guidelines
Joyce, Kathryn E. – Educational Research and Evaluation, 2019
Within evidence-based education, results from randomised controlled trials (RCTs), and meta-analyses of them, are taken as reliable evidence for effectiveness -- they speak to "what works". Extending RCT results requires establishing that study samples and settings are representative of the intended target. Although widely recognised as…
Descriptors: Evidence Based Practice, Educational Research, Instructional Effectiveness, Randomized Controlled Trials
Cope, Bill; Kalantzis, Mary – Open Review of Educational Research, 2015
In this article, we argue that big data can offer new opportunities and roles for educational researchers. In the traditional model of evidence-gathering and interpretation in education, researchers are independent observers, who pre-emptively create instruments of measurement, and insert these into the educational process in specialized times and…
Descriptors: Data Collection, Data Interpretation, Evidence, Educational Research