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Kylie E. Hunter; Mason Aberoumand; Sol Libesman; James X. Sotiropoulos; Jonathan G. Williams; Wentao Li; Jannik Aagerup; Ben W. Mol; Rui Wang; Angie Barba; Nipun Shrestha; Angela C. Webster; Anna Lene Seidler – Research Synthesis Methods, 2024
Increasing integrity concerns in medical research have prompted the development of tools to detect untrustworthy studies. Existing tools primarily assess published aggregate data (AD), though scrutiny of individual participant data (IPD) is often required to detect trustworthiness issues. Thus, we developed the IPD Integrity Tool for detecting…
Descriptors: Integrity, Randomized Controlled Trials, Data Use, Individual Characteristics
Reagan Mozer; Luke Miratrix – Society for Research on Educational Effectiveness, 2023
Background: For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require each document first be manually coded for constructs of interest by trained human raters. These hand-coded scores are then used as a measured outcome for an impact analysis, with the average scores of the treatment group…
Descriptors: Artificial Intelligence, Coding, Randomized Controlled Trials, Research Methodology
Adam Sales; Ethan Prihar; Johann Gagnon-Bartsch; Neil Heffernan – Society for Research on Educational Effectiveness, 2023
Background: Randomized controlled trials (RCTs) give unbiased estimates of average effects. However, positive effects for the majority of students may mask harmful effects for smaller subgroups, and RCTs often have too small a sample to estimate these subgroup effects. In many RCTs, covariate and outcome data are drawn from a larger database. For…
Descriptors: Learning Analytics, Randomized Controlled Trials, Data Use, Accuracy
Sales, Adam C.; Prihar, Ethan B.; Gagnon-Bartsch, Johann A.; Heffernan, Neil T. – Journal of Educational Data Mining, 2023
Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered,…
Descriptors: Data Use, Research Methodology, Randomized Controlled Trials, Educational Technology
Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
The gold-standard for evaluating the effect of an educational intervention on student outcomes is running a randomized controlled trial (RCT). However, RCTs may often be small due to logistical considerations, and resulting treatment effect estimates may lack precision. Recent methods improve experimental precision by incorporating information…
Descriptors: Intervention, Outcomes of Education, Randomized Controlled Trials, Data Use
Hallberg, Kelly; Williams, Ryan; Swanlund, Andrew – Journal of Research on Educational Effectiveness, 2020
More aggregate data on school performance is available than ever before, opening up new possibilities for applied researchers interested in assessing the effectiveness of school-level interventions quickly and at a relatively low cost by implementing comparative interrupted times series (CITS) designs. We examine the extent to which effect…
Descriptors: Data Use, Research Methodology, Program Effectiveness, Design
Bloom, Howard; Bell, Andrew; Reiman, Kayla – Journal of Research on Educational Effectiveness, 2020
This article assesses the likely generalizability of educational treatment-effect estimates from regression discontinuity designs (RDDs) when treatment assignment is based on academic pretest scores. Our assessment uses data on outcome and pretest measures from six educational experiments, ranging from preschool through high school, to estimate…
Descriptors: Data Use, Randomized Controlled Trials, Research Design, Regression (Statistics)
Bloom, Howard; Bell, Andrew; Reiman, Kayla – Grantee Submission, 2020
This article assesses the likely generalizability of educational treatment-effect estimates from regression discontinuity designs (RDDs) when treatment assignment is based on academic pretest scores. Our assessment uses data on outcome and pretest measures from six educational experiments, ranging from preschool through high school, to estimate…
Descriptors: Data Use, Randomized Controlled Trials, Research Design, Regression (Statistics)
Society for Research on Educational Effectiveness, 2017
Bayesian statistical methods have become more feasible to implement with advances in computing but are not commonly used in educational research. In contrast to frequentist approaches that take hypotheses (and the associated parameters) as fixed, Bayesian methods take data as fixed and hypotheses as random. This difference means that Bayesian…
Descriptors: Bayesian Statistics, Educational Research, Statistical Analysis, Decision Making
McGrath, Scott – ProQuest LLC, 2019
The concept of precision medicine aims to provide additional context to patient data for healthcare providers. Precision medicine overlays three additional layers of patient data on top of standard patient information: environmental exposure, personal lifestyle and behavior patterns, and information gleaned from their genome. While precision…
Descriptors: Medicine, Genetics, Primary Health Care, Physicians