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Wendy Chan; Larry Vernon Hedges – Journal of Educational and Behavioral Statistics, 2022
Multisite field experiments using the (generalized) randomized block design that assign treatments to individuals within sites are common in education and the social sciences. Under this design, there are two possible estimands of interest and they differ based on whether sites or blocks have fixed or random effects. When the average treatment…
Descriptors: Research Design, Educational Research, Statistical Analysis, Statistical Inference
Adam C. Sales; Ethan Prihar; Johann Gagnon-Bartsch; Ashish Gurung; Neil T. Heffernan – Grantee Submission, 2022
Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…
Descriptors: Research Methodology, Educational Experiments, Causal Models, Computation
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Fergusson, Anna; Pfannkuch, Maxine – Mathematical Thinking and Learning: An International Journal, 2022
The advent of data science has led to statistics education researchers re-thinking and expanding their ideas about tools for teaching statistical modeling, such as the use of code-driven tools at the secondary school level. Methods for statistical inference, such as the randomization test, are typically taught within secondary school classrooms…
Descriptors: Foreign Countries, Data Science, Statistics Education, Mathematical Models
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Motz, Benjamin A.; Carvalho, Paulo F.; de Leeuw, Joshua R.; Goldstone, Robert L. – Journal of Learning Analytics, 2018
To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real…
Descriptors: Causal Models, Statistical Inference, Inferences, Educational Experiments
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Jo, Booil; Stuart, Elizabeth A. – Journal of Research on Educational Effectiveness, 2012
The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…
Descriptors: Bayesian Statistics, Educational Experiments, Educational Research, Observation
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Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2013
This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for…
Descriptors: Computation, Causal Models, Statistical Inference, Nonparametric Statistics