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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
Uwimpuhwe, Germaine; Singh, Akansha; Higgins, Steve; Coux, Mickael; Xiao, ZhiMin; Shkedy, Ziv; Kasim, Adetayo – Journal of Experimental Education, 2022
Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in education have used the standardised mean difference as a measure of the impact of interventions. This…
Descriptors: Program Effectiveness, Intervention, Multivariate Analysis, Bayesian Statistics
Harrison, Sean; Jones, Hayley E.; Martin, Richard M.; Lewis, Sarah J.; Higgins, Julian P. T. – Research Synthesis Methods, 2017
Meta-analyses combine the results of multiple studies of a common question. Approaches based on effect size estimates from each study are generally regarded as the most informative. However, these methods can only be used if comparable effect sizes can be computed from each study, and this may not be the case due to variation in how the studies…
Descriptors: Meta Analysis, Sample Size, Effect Size, Comparative Analysis
Joyce, Ted; Remler, Dahlia K.; Jaeger, David A.; Altindag, Onur; O'Connell, Stephen D.; Crockett, Sean – Journal of Policy Analysis and Management, 2017
Randomized experiments provide unbiased estimates of treatment effects, but are costly and time consuming. We demonstrate how a randomized experiment can be leveraged to measure selection bias by conducting a subsequent observational study that is identical in every way except that subjects choose their treatment--a quasi-doubly randomized…
Descriptors: Randomized Controlled Trials, Quasiexperimental Design, Selection Criteria, Selection Tools
Klopfer, Kristina M.; Scott, Katreena; Jenkins, Jennifer; Ducharme, Joe – Teacher Education and Special Education, 2019
Childhood emotional and behavioral problems are prevalent in elementary classroom settings, making it imperative that high-quality, efficacious training be available to support teachers in managing disruptive and distressed child behaviors. Our study used a randomized control design to examine the impact of 36 hours of preservice education…
Descriptors: Preservice Teacher Education, Classroom Techniques, Pretests Posttests, Emotional Disturbances
Hegedus, Stephen; Tapper, John; Dalton, Sara; Sloane, Finbarr – Research in Mathematics Education, 2013
We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variables. We provide a detailed description of such…
Descriptors: Randomized Controlled Trials, Student Diversity, Hierarchical Linear Modeling, Algebra