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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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Culpepper, Steven Andrew – Journal of Educational and Behavioral Statistics, 2015
A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas,…
Descriptors: Bayesian Statistics, Models, Sampling, Computation
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Verkuilen, Jay; Smithson, Michael – Journal of Educational and Behavioral Statistics, 2012
Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…
Descriptors: Responses, Regression (Statistics), Statistical Analysis, Models
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Fox, J.-P.; Wyrick, Cheryl – Journal of Educational and Behavioral Statistics, 2008
The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…
Descriptors: Item Response Theory, Models, Markov Processes, Monte Carlo Methods
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Segall, Daniel O. – Journal of Educational and Behavioral Statistics, 2002
Developed an item response model for characterizing test-compromise that enables the estimation of item preview and score-gain distributions. In the approach, models parameters and posterior distributions are estimated by Markov Chain Monte Carlo procedures. Simulation study results suggest that when at least some test items are known to be…
Descriptors: Estimation (Mathematics), Item Response Theory, Markov Processes, Models
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Johnson, Matthew S.; Junker, Brian W. – Journal of Educational and Behavioral Statistics, 2003
Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993)and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT…
Descriptors: Markov Processes, Item Response Theory, Computation, Data Analysis