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Peer reviewedKenneth A. Frank – Grantee Submission, 2025
Most randomized field experiments experience some attrition. Moreover, the extent of attrition may differ by treatment condition in systematic, non-random ways, biasing estimates of treatment effects and contributing to invalid inferences. We address concerns about non-random attrition by quantifying the conditions necessary in the attritted data…
Descriptors: Attrition (Research Studies), Randomized Controlled Trials, Inferences, Correlation
Nianbo Dong; Benjamin Kelcey; Jessaca Spybrook; Yanli Xie; Dung Pham; Peilin Qiu; Ning Sui – Grantee Submission, 2024
Multisite trials that randomize individuals (e.g., students) within sites (e.g., schools) or clusters (e.g., teachers/classrooms) within sites (e.g., schools) are commonly used for program evaluation because they provide opportunities to learn about treatment effects as well as their heterogeneity across sites and subgroups (defined by moderating…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Educational Research, Effect Size
Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
Clarissa Victoria Velez; Mileini Campez-Pardo; Jennifer Mariam Canovas; Paloma Maria Pedronzo; Yeojin Amy Ahn; Chelsea Faye Dale; Sannisha K. Dale; Lisa Gwynn; Amanda Jensen-Doss; Elizabeth R. Pulgaron; Sara Mijares St. George; Jill Ehrenreich-May – Grantee Submission, 2025
Background: Despite many adolescents experiencing mental health concerns, a substantial portion lack access to evidence-based treatments (EBTs) for psychopathology; this issue is magnified for adolescents belonging to communities considered marginalized. One way to ameliorate this is by adapting existent EBTs--typically delivered in research…
Descriptors: Prevention, High School Students, Evidence Based Practice, Therapy
Regan Mozer; Luke Miratrix – Grantee Submission, 2024
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by trained human raters. This process, the current standard, is both time-consuming and limiting: even the largest human coding efforts are typically constrained to…
Descriptors: Artificial Intelligence, Coding, Efficiency, Statistical Inference
Jill Locke; Nathaniel J. Williams; Aksheya Sridhar; Mark G. Ehrhart; Alex Dopp; Marissa Thirion; Christine Espeland; Brandon Riddle; Kelcey Schmitz; Kurt Hatch; Lindsey Buehler; Aaron R. Lyon – Grantee Submission, 2025
Background: Schools need to implement universal student supports that prevent social, emotional, and behavioral difficulties; minimize associated risks; and promote social, emotional, and behavioral competencies. The purpose of this study is to examine the efficacy of the Helping Educational Leaders Mobilize Evidence (HELM) implementation strategy…
Descriptors: Positive Behavior Supports, Elementary Schools, Program Implementation, Program Effectiveness
Eric C. Hedberg – Grantee Submission, 2023
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…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
Empirical Benchmarks for Planning and Interpreting Causal Effects of Community College Interventions
Michael J. Weiss; Marie-Andrée Somers; Colin Hill – Grantee Submission, 2023
Randomized controlled trials (RCTs) are an increasingly common research design for evaluating the effectiveness of community college (CC) interventions. However, when planning an RCT evaluation of a CC intervention, there is limited empirical information about what sized effects an intervention might reasonably achieve, which can lead to under- or…
Descriptors: Community Colleges, Response to Intervention, Randomized Controlled Trials, College Enrollment
Sandra Jo Wilson; Brian Freeman; E. C. Hedberg – Grantee Submission, 2024
As reporting of effect sizes in evaluation studies has proliferated, researchers and consumers of research need tools for interpreting or benchmarking the magnitude of those effect sizes that are relevant to the intervention, target population, and outcome measure being considered. Similarly, researchers planning education studies with social and…
Descriptors: Benchmarking, Effect Size, Meta Analysis, Statistical Analysis
Marie-Andrée Somers; Michael J. Weiss; Colin Hill – Grantee Submission, 2022
The last two decades have seen a dramatic increase in randomized controlled trials (RCTs) conducted in community colleges. Yet, there is limited empirical information on the design parameters necessary to plan the sample size for RCTs in this context. We provide empirical estimates of key design parameters, discussing lessons based on the pattern…
Descriptors: Randomized Controlled Trials, Research Design, Sample Size, Statistical Analysis
Xinxin Sun – Grantee Submission, 2023
Noncompliance to treatment assignment is widespread in randomized trials and presents challenges in causal inference. In the presence of noncompliance, the most commonly estimated effect of treatment assignment, also known as the intent-to-treat (ITT) effect, is biased. Of interest in this setting is the complier average causal effect (CACE), the…
Descriptors: Compliance (Psychology), Randomized Controlled Trials, Maximum Likelihood Statistics, Computation
Avery H. Closser; Adam Sales; Anthony F. Botelho – Grantee Submission, 2024
Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data on study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is…
Descriptors: Data Analysis, Educational Research, Randomized Controlled Trials, Sampling
Jordan Rickles; Margaret Clements; Iliana Brodziak de los Reyes; Mark Lachowicz; Shuqiong Lin; Jessica Heppen – Grantee Submission, 2023
Online credit recovery will likely expand in the coming years as school districts try to address increased course failure rates brought on by the coronavirus pandemic. Some researchers and policymakers, however, raise concerns over how much students learn in online courses, and there is limited evidence about the effectiveness of online credit…
Descriptors: Online Courses, Electronic Learning, Repetition, Required Courses
Alexa C. Budavari; Heather L. McDaniel; Antonio A. Morgan-López; Rashelle J. Musci; Jason T. Downer; Nicholas S. Ialongo; Catherine P. Bradshaw – Grantee Submission, 2025
Retention of early career teachers is a critical issue in education, with burnout and self-efficacy serving as important precursors to teachers leaving the field. An integration of the PAX Good Behavior Game (GBG; Barrish et al., 1969) and MyTeachingPartner (MTP; Allen et al., 2015) was tested in a randomized controlled trial (RCT) to investigate…
Descriptors: Randomized Controlled Trials, Followup Studies, COVID-19, Pandemics
Peer reviewedKenneth 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

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