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Peer reviewedRan Xu; Kenneth A. Frank; Qinyun Lin; Spiro J. Maroulis; Xuesen Cheng – Grantee Submission, 2025
One of the most important factors affecting the use of evidence for policy or practice is the uncertainty of study results. Furthermore, this uncertainty is compounded by our increasing awareness of heterogeneous treatment effects. Here we inform debate about the strength of study evidence by quantifying the conditions necessary to nullify an…
Descriptors: Literacy Education, Intervention, Statistical Inference, Vocabulary Development
Zhang, Dongbo; Koda, Keiko – Reading and Writing: An Interdisciplinary Journal, 2012
Within the Structural Equation Modeling framework, this study tested the direct and indirect effects of morphological awareness and lexical inferencing ability on L2 vocabulary knowledge and reading comprehension among advanced Chinese EFL readers in a university in China. Using both regular z-test and the bootstrapping (data-based resampling)…
Descriptors: Reading Comprehension, Structural Equation Models, Foreign Countries, Vocabulary Development
Zhao, Yuan – ProQuest LLC, 2010
Learning a phonetic category (or any linguistic category) requires integrating different sources of information. A crucial unsolved problem for phonetic learning is how this integration occurs: how can we update our previous knowledge about a phonetic category as we hear new exemplars of the category? One model of learning is Bayesian Inference,…
Descriptors: Evidence, Cues, Phonetics, Prior Learning
Peer reviewedSeltzer, Michael H. – Journal of Educational Statistics, 1993
A Bayesian approach to sensitivity of inferences to possible outliers involves recalculating marginal posterior distributions of parameters of interest under assumptions of heavy tails. This strategy is implemented in the hierarchical model setting through Gibbs sampling, a Monte Carlo technique, and illustrated through a reanalysis of data on…
Descriptors: Bayesian Statistics, Elementary Education, Equations (Mathematics), Mathematical Models

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