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Jean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
Vehtari, Aki; Gelman, Andrew; Sivula, Tuomas; Jylänki, Pasi; Tran, Dustin; Sahai, Swupnil; Blomstedt, Paul; Cunningham, John P.; Schiminovich, David; Robert, Christian P. – Grantee Submission, 2020
A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for…
Descriptors: Bayesian Statistics, Algorithms, Computation, Generalization
Bao, Lei; Koenig, Kathleen; Xiao, Yang; Fritchman, Joseph; Zhou, Shaona; Chen, Cheng – Physical Review Physics Education Research, 2022
Abilities in scientific thinking and reasoning have been emphasized as core areas of initiatives, such as the Next Generation Science Standards or the College Board Standards for College Success in Science, which focus on the skills the future will demand of today's students. Although there is rich literature on studies of how these abilities…
Descriptors: Physics, Science Instruction, Teaching Methods, Thinking Skills
Ross, Steven J.; Mackey, Beth – Language Learning, 2015
This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…
Descriptors: Bayesian Statistics, Hypothesis Testing, Meta Analysis, Inferences
Perfors, Amy; Tenenbaum, Joshua B.; Wonnacott, Elizabeth – Journal of Child Language, 2010
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object…
Descriptors: Verbs, Inferences, Language Acquisition, Bayesian Statistics
Chater, Nick; Brown, Gordon D. A. – Cognitive Science, 2008
The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision…
Descriptors: Sciences, Scientific Principles, Models, Memory
Gresham, Frank M.; McIntyre, Laura Lee; Olson-Tinker, Heidi; Dolstra, Lisa; McLaughlin, Veronica; Van, Mai – Research in Developmental Disabilities: A Multidisciplinary Journal, 2004
The Individuals With Disabilities Education Act (IDEA) Amendments of 1997 mandate the use of functional behavioral assessment (FBA) and positive behavioral supports and interventions for students with disabilities. Although much progress has been made in our understanding of functional analysis over the past 15 years, the extent to which these…
Descriptors: Attitudes toward Disabilities, Functional Behavioral Assessment, Developmental Disabilities, Intervention

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