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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
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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
Gagnon-Bartsch, J. A.; Sales, A. C.; Wu, E.; Botelho, A. F.; Erickson, J. A.; Miratrix, L. W.; Heffernan, N. T. – Grantee Submission, 2019
Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…
Descriptors: Randomized Controlled Trials, Educational Research, Prediction, Algorithms
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Taylor, Joseph; Kowalski, Susan; Stuhlsatz, Molly; Wilson, Christopher; Spybrook, Jessaca – Society for Research on Educational Effectiveness, 2013
The purpose of this paper is to use both conceptual and statistical approaches to explore publication bias in recent causal effects studies in science education, and to draw from this exploration implications for researchers, journal reviewers, and journal editors. This paper fills a void in the "science education" literature as no…
Descriptors: Science Education, Influences, Bias, Statistical Analysis
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Riegg, Stephanie K. – Review of Higher Education, 2008
This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…
Descriptors: Research Methodology, Causal Models, Inferences, Test Bias