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Timo Gnambs; Ulrich Schroeders – Research Synthesis Methods, 2024
Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing…
Descriptors: Accuracy, Meta Analysis, Randomized Controlled Trials, Effect Size
Experimental Estimates of College Coaching on Postsecondary Re-Enrollment. EdWorkingPaper No. 23-746
Lesley J. Turner; Oded Gurantz – Annenberg Institute for School Reform at Brown University, 2024
College attendance has increased significantly over the last few decades, but dropout rates remain high, with fewer than half of all adults ultimately obtaining a postsecondary credential. This project investigates whether one-on-one college coaching improves college attendance and completion outcomes for former low- and middle-income income state…
Descriptors: College Students, Coaching (Performance), Attendance, Alumni
Foster, Colin – International Journal of Research & Method in Education, 2023
This paper introduces a simple, quotient effect size, termed (for 'quotient'), suitable for reporting on the effectiveness of educational interventions. The quotient effect size for a pre-test-post-test design is defined as the gain score (i.e. post-test minus pre-test) for the intervention group, divided by the gain score for the control group.…
Descriptors: Effect Size, Intervention, Bias, Randomized Controlled Trials
Ishita Ahmed; Masha Bertling; Lijin Zhang; Andrew Ho; Prashant Loyalka; Scott Rozelle; Ben Domingue – Society for Research on Educational Effectiveness, 2023
Background: Evidence from education randomized controlled trials (RCTs) in low- and middle-income countries (LMICs) demonstrates how interventions can improve children's educational achievement [1, 2, 3, 4]. RCTs assess the impact of an intervention by comparing outcomes--aggregate test scores--between treatment and control groups. A review of…
Descriptors: Randomized Controlled Trials, Educational Research, Outcome Measures, Research Design
Ishita Ahmed; Masha Bertling; Lijin Zhang; Andrew D. Ho; Prashant Loyalka; Hao Xue; Scott Rozelle; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2023
Researchers use test outcomes to evaluate the effectiveness of education interventions across numerous randomized controlled trials (RCTs). Aggregate test data--for example, simple measures like the sum of correct responses--are compared across treatment and control groups to determine whether an intervention has had a positive impact on student…
Descriptors: Randomized Controlled Trials, Educational Research, Outcome Measures, Research Design
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
Justin Boutilier; Jonas Jonasson; Hannah Li; Erez Yoeli – Society for Research on Educational Effectiveness, 2024
Background: Randomized controlled trials (RCTs), or experiments, are the gold standard for intervention evaluation. However, the main appeal of RCTs--the clean identification of causal effects--can be compromised by interference, when one subject's actions can influence another subject's behavior or outcomes. In this paper, we formalize and study…
Descriptors: Randomized Controlled Trials, Intervention, Mathematical Models, Interference (Learning)
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Journal of Experimental Education, 2022
In two-level designs, the total sample is a function of both the number of Level 2 clusters and the average number of Level 1 units per cluster. Traditional multilevel power calculations rely on either the arithmetic average or the harmonic mean when estimating the average number of Level 1 units across clusters of unbalanced size. The current…
Descriptors: Multivariate Analysis, Randomized Controlled Trials, Monte Carlo Methods, Sample Size
Sims, Sam; Anders, Jake; Inglis, Matthew; Lortie-Forgues, Hugues – Journal of Research on Educational Effectiveness, 2023
Randomized controlled trials have proliferated in education, in part because they provide an unbiased estimator for the causal impact of interventions. It is increasingly recognized that many such trials in education have low power to detect an effect if indeed there is one. However, it is less well known that low powered trials tend to…
Descriptors: Randomized Controlled Trials, Educational Research, Effect Size, Intervention
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
E. C. Hedberg – American Journal of Evaluation, 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
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
Kraft, Matthew A. – Educational Researcher, 2023
It is a healthy exercise to debate the merits of using effect-size benchmarks to interpret research findings. However, these debates obscure a more central insight that emerges from empirical distributions of effect-size estimates in the literature: Efforts to improve education often fail to move the needle. I find that 36% of effect sizes from…
Descriptors: Effect Size, Benchmarking, Educational Research, Educational Policy
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Power in multilevel models remains an area of interest to both methodologists and substantive researchers. In two-level designs, the total sample is a function of both the number of level-2 (e.g., schools) clusters and the average number of level-1 (e.g., classrooms) units per cluster. Traditional multilevel power calculations rely on either the…
Descriptors: Multivariate Analysis, Randomized Controlled Trials, Monte Carlo Methods, Sample Size
Singh, Akansha; Uwimpuhwe, Germaine; Li, Mengchu; Einbeck, Jochen; Higgins, Steve; Kasim, Adetayo – International Journal of Research & Method in Education, 2022
In education, multisite trials involve randomization of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This study investigates the impact of statistical models on the uncertainty associated with an effect size…
Descriptors: Randomized Controlled Trials, Effect Size, Hierarchical Linear Modeling, Least Squares Statistics