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Zhan, Peida; Jiao, Hong; Man, Kaiwen; Wang, Lijun – Journal of Educational and Behavioral Statistics, 2019
In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy "and" gate model; the deterministic inputs, noisy "or" gate model; the linear logistic model; the reduced reparameterized unified…
Descriptors: Bayesian Statistics, Computer Software, Models, Test Items
Sweet, Tracy M. – Journal of Educational and Behavioral Statistics, 2015
Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…
Descriptors: Social Networks, Statistical Analysis, Probability, Models
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Koskinen, Johan; Stenberg, Sten-Ake – Journal of Educational and Behavioral Statistics, 2012
When studying educational aspirations of adolescents, it is unrealistic to assume that the aspirations of pupils are independent of those of their friends. Considerable attention has also been given to the study of peer influence in the educational and behavioral literature. Typically, in empirical studies, the friendship networks have either been…
Descriptors: Foreign Countries, Bayesian Statistics, Models, Friendship
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2011
An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, such as students clustered in classes. For each subject, a latent process is used to represent the characteristic of interest (e.g., ability) conditional on the effect of the cluster to…
Descriptors: Markov Processes, Data Analysis, Maximum Likelihood Statistics, Computation
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Browne, William; Goldstein, Harvey – Journal of Educational and Behavioral Statistics, 2010
In this article, we discuss the effect of removing the independence assumptions between the residuals in two-level random effect models. We first consider removing the independence between the Level 2 residuals and instead assume that the vector of all residuals at the cluster level follows a general multivariate normal distribution. We…
Descriptors: Computation, Sampling, Markov Processes, Monte Carlo Methods
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Seltzer, Michael; Novak, John; Choi, Kilchan; Lim, Nelson – Journal of Educational and Behavioral Statistics, 2002
Examines the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in hierarchical models (HMs). Also outlines and illustrates the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under "t" level-1 assumptions, including algorithms for settings in which the degrees of…
Descriptors: Algorithms, Estimation (Mathematics), Markov Processes, Monte Carlo Methods
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Patz, Richard J.; Junker, Brian W. – Journal of Educational and Behavioral Statistics, 1999
Demonstrates Markov chain Monte Carlo (MCMC) techniques that are well-suited to complex models with Item Response Theory (IRT) assumptions. Develops an MCMC methodology that can be routinely implemented to fit normal IRT models, and compares the approach to approaches based on Gibbs sampling. Contains 64 references. (SLD)
Descriptors: Item Response Theory, Markov Processes, Models, Monte Carlo Methods