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Catherine Mata; Katharine Meyer; Lindsay Page – Annenberg Institute for School Reform at Brown University, 2024
This article examines the risk of crossover contamination in individual-level randomization, a common concern in experimental research, in the context of a large-enrollment college course. While individual-level randomization is more efficient for assessing program effectiveness, it also increases the potential for control group students to cross…
Descriptors: Chemistry, Science Instruction, Undergraduate Students, Large Group Instruction
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Beth Chance; Karen McGaughey; Sophia Chung; Alex Goodman; Soma Roy; Nathan Tintle – Journal of Statistics and Data Science Education, 2025
"Simulation-based inference" is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this article, we highlight for teachers…
Descriptors: Simulation, Sampling, Randomized Controlled Trials, Hypothesis Testing
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James Soland – Journal of Research on Educational Effectiveness, 2024
When randomized control trials are not possible, quasi-experimental methods often represent the gold standard. One quasi-experimental method is difference-in-difference (DiD), which compares changes in outcomes before and after treatment across groups to estimate a causal effect. DiD researchers often use fairly exhaustive robustness checks to…
Descriptors: Item Response Theory, Testing, Test Validity, Intervention
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Maite Alguacil; Noemí Herranz-Zarzoso; José C. Pernías; Gerardo Sabater-Grande – Journal of Computing in Higher Education, 2024
Cheating in online exams without face-to-face proctoring has been a general concern for academic instructors during the crisis caused by COVID-19. The main goal of this work is to evaluate the cost of these dishonest practices by comparing the academic performance of webcam-proctored students and their unproctored peers in an online gradable test.…
Descriptors: Cheating, Computer Assisted Testing, Randomized Controlled Trials, Supervision
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Andrew P. Jaciw – American Journal of Evaluation, 2025
By design, randomized experiments (XPs) rule out bias from confounded selection of participants into conditions. Quasi-experiments (QEs) are often considered second-best because they do not share this benefit. However, when results from XPs are used to generalize causal impacts, the benefit from unconfounded selection into conditions may be offset…
Descriptors: Elementary School Students, Elementary School Teachers, Generalization, Test Bias
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Shahid A. Choudhry; Timothy J. Muckle; Christopher J. Gill; Rajat Chadha; Magnus Urosev; Matt Ferris; John C. Preston – Practical Assessment, Research & Evaluation, 2024
The National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA) conducted a one-year research study comparing performance on the traditional continued professional certification assessment, administered at a test center or online with remote proctoring, to a longitudinal assessment that required answering quarterly…
Descriptors: Nurses, Certification, Licensing Examinations (Professions), Computer Assisted Testing
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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
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Held, Leonhard; Matthews, Robert; Ott, Manuela; Pawel, Samuel – Research Synthesis Methods, 2022
It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…
Descriptors: Bayesian Statistics, Statistical Inference, Hypothesis Testing, Credibility
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Akansha Singh; Germaine Uwimpuhwe; Dimitrios Vallis; Nasima Akhter; Tahani Coolen-Maturi; Steve Higgins; Jochen Einbeck; Martin Culliney; Sean Demack – Education Endowment Foundation, 2023
The aim of this study was to investigate and empirically derive parameters commonly used for statistical power and sample size calculations to better inform future trial design. Towards achieving this aim, the research project leveraged the richness of the National Pupil Database (NPD) and the Education Endowment Foundation (EEF) Archive to: (1)…
Descriptors: Foreign Countries, Statistical Analysis, Sample Size, Educational Research
Timothy Lycurgus; Ben B. Hansen; Mark White – Grantee Submission, 2022
We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Intervention studies using longitudinal data often include multiple observations on individuals, some of which may be more likely to manifest a treatment effect…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Quasiexperimental Design, Intervention
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Kelcey, Ben; Spybrook, Jessaca; Dong, Nianbo; Bai, Fangxing – Journal of Research on Educational Effectiveness, 2020
Professional development for teachers is regarded as one of the principal pathways through which we can understand and cultivate effective teaching and improve student outcomes. A critical component of studies that seek to improve teaching through professional development is the detailed assessment of the intermediate teacher development processes…
Descriptors: Faculty Development, Educational Research, Randomized Controlled Trials, Research Design
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Lortie-Forgues, Hugues; Inglis, Matthew – Educational Researcher, 2019
In this response, we first show that Simpson's proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.
Descriptors: Randomized Controlled Trials, Bayesian Statistics, Educational Research, Program Evaluation
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Cohen, Dale; Tracy, Ryan; Cohen, Jon – Applied Measurement in Education, 2017
This study examined the effectiveness and influence on validity of a computer-based pop-up English glossary accommodation for English learners (ELs) in grades 3 and 7. In a randomized controlled trial, we administered pop-up English glossaries with audio to students taking a statewide accountability English language arts (ELA) and mathematics…
Descriptors: English (Second Language), Glossaries, Testing Accommodations, Measurement
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Simpson, Adrian – Educational Researcher, 2019
A recent paper uses Bayes factors to argue a large minority of rigorous, large-scale education RCTs are "uninformative." The definition of "uninformative" depends on the authors' hypothesis choices for calculating Bayes factors. These arguably overadjust for effect size inflation and involve a fixed prior distribution,…
Descriptors: Randomized Controlled Trials, Bayesian Statistics, Educational Research, Program Evaluation
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Loeb, Katharine L.; Radnitz, Cynthia; Keller, Kathleen L.; Schwartz, Marlene B.; Zucker, Nancy; Marcus, Sue; Pierson, Richard N.; Shannon, Michael; DeLaurentis, Danielle – Journal of School Health, 2018
Background: In this study, we applied behavioral economics to optimize elementary school lunch choices via parent-driven decisions. Specifically, this experiment tested an optimal defaults paradigm, examining whether strategically manipulating the health value of a default menu could be co-opted to improve school-based lunch selections. Methods:…
Descriptors: Elementary School Students, Eating Habits, Food, Decision Making
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