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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
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
Xu Qin – Grantee Submission, 2023
When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a…
Descriptors: Sample Size, Statistical Analysis, Causal Models, Mediation Theory
Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
Bellara, Aarti P. – ProQuest LLC, 2013
Propensity score analysis has been used to minimize the selection bias in observational studies to identify causal relationships. A propensity score is an estimate of an individual's probability of being placed in a treatment group given a set of covariates. Propensity score analysis aims to use the estimate to create balanced groups, akin to a…
Descriptors: Scores, Probability, Monte Carlo Methods, Statistical Analysis
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
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Ferraro, Kenneth F.; Nuriddin, Tariqah A. – Journal of Health and Social Behavior, 2006
Does psychological distress increase mortality risk? If it does, are women more vulnerable than men to the effect of distress on mortality? Drawing from cumulative disadvantage theory, these questions are addressed with data from a 20-year follow-up of a national sample of adults ages 25-74. Event history analyses were performed to examine…
Descriptors: Heart Disorders, Death, Adjustment (to Environment), Anxiety