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Huibin Zhang; Zuchao Shen; Walter L. Leite – Journal of Experimental Education, 2025
Cluster-randomized trials have been widely used to evaluate the treatment effects of interventions on student outcomes. When interventions are implemented by teachers, researchers need to account for the nested structure in schools (i.e., students are nested within teachers nested within schools). Schools usually have a very limited number of…
Descriptors: Sample Size, Multivariate Analysis, Randomized Controlled Trials, Correlation
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
Kristin Porter; Luke Miratrix; Kristen Hunter – Society for Research on Educational Effectiveness, 2021
Background: Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs)…
Descriptors: Statistical Analysis, Hypothesis Testing, Computer Software, Randomized Controlled Trials
Sales, Adam C.; Hansen, Ben B. – Journal of Educational and Behavioral Statistics, 2020
Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment,…
Descriptors: Regression (Statistics), Computation, Statistical Inference, Robustness (Statistics)
Miratrix, Luke; Furey, Jane; Feller, Avi; Grindal, Todd; Page, Lindsay C. – Journal of Research on Educational Effectiveness, 2018
Estimating treatment effects for subgroups defined by posttreatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challenging and heavily reliant on strong assumptions. We investigate an alternative path: using bounds to identify ranges of possible effects that are consistent with the data.…
Descriptors: College School Cooperation, Program Effectiveness, Attribution Theory, Prediction
Chaney, Bradford – American Journal of Evaluation, 2016
The primary technique that many researchers use to analyze data from randomized control trials (RCTs)--detecting the average treatment effect (ATE)--imposes assumptions upon the data that often are not correct. Both theory and past research suggest that treatments may have significant impacts on subgroups even when showing no overall effect.…
Descriptors: Randomized Controlled Trials, Data Analysis, Outcomes of Treatment, Simulation
Giacalone, James – ProQuest LLC, 2017
Simulation technology in healthcare education is continuously evolving but little is known about the effects of high fidelity simulation on student knowledge and skill retention or motivation. The purpose of this quantitative study was to use quasi-experimental design in a randomized controlled trial that compared the retention of knowledge and…
Descriptors: Simulation, Fidelity, Allied Health Occupations Education, Statistical Analysis
Miratrix, Luke; Feller, Avi; Pillai, Natesh; Pati, Debdeep – Society for Research on Educational Effectiveness, 2016
Modeling the distribution of site level effects is an important problem, but it is also an incredibly difficult one. Current methods rely on distributional assumptions in multilevel models for estimation. There it is hoped that the partial pooling of site level estimates with overall estimates, designed to take into account individual variation as…
Descriptors: Probability, Models, Statistical Distributions, Bayesian Statistics
Westlund, Erik; Stuart, Elizabeth A. – American Journal of Evaluation, 2017
This article discusses the nonuse, misuse, and proper use of pilot studies in experimental evaluation research. The authors first show that there is little theoretical, practical, or empirical guidance available to researchers who seek to incorporate pilot studies into experimental evaluation research designs. The authors then discuss how pilot…
Descriptors: Use Studies, Pilot Projects, Evaluation Research, Experiments
Koka, Andre – European Physical Education Review, 2017
This study examined the effectiveness of a brief theory-based intervention on muscular strength among adolescents in a physical education setting. The intervention adopted a process-based mental simulation technique. The self-reported frequency of practising for and actual levels of abdominal muscular strength/endurance as one component of…
Descriptors: Intervention, Simulation, Muscular Strength, Physical Education
Leppink, Jimmie; van Merriënboer, Jeroen J. G. – Educational Technology & Society, 2015
An increasing part of cognitive load research in technology-based learning includes a component of repeated measurements, that is: participants are measured two or more times on the same performance, mental effort or other variable of interest. In many cases, researchers aggregate scores obtained from repeated measurements to one single sum or…
Descriptors: Cognitive Processes, Difficulty Level, Measures (Individuals), Statistical Analysis
Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
Bloom, Howard S.; Porter, Kristin E.; Weiss, Michael J.; Raudenbush, Stephen – Society for Research on Educational Effectiveness, 2013
To date, evaluation research and policy analysis have focused mainly on average program impacts and paid little systematic attention to their variation. Recently, the growing number of multi-site randomized trials that are being planned and conducted make it increasingly feasible to study "cross-site" variation in impacts. Important…
Descriptors: Research Methodology, Policy, Evaluation Research, Randomized Controlled Trials
Buchanan, Taylor L.; Lohse, Keith R. – Measurement in Physical Education and Exercise Science, 2016
We surveyed researchers in the health and exercise sciences to explore different areas and magnitudes of bias in researchers' decision making. Participants were presented with scenarios (testing a central hypothesis with p = 0.06 or p = 0.04) in a random order and surveyed about what they would do in each scenario. Participants showed significant…
Descriptors: Researchers, Attitudes, Statistical Significance, Bias