Jean-Paul Fox's Bayesian Item Response Modeling: Theory and Applications PDF

By Jean-Paul Fox

ISBN-10: 1441907416

ISBN-13: 9781441907417

This ebook provides a radical remedy and unified insurance of Bayesian merchandise reaction modeling with functions in quite a few disciplines, together with schooling, drugs, psychology, and sociology. Breakthroughs in computing expertise have made the Bayesian technique fairly beneficial for plenty of reaction modeling difficulties. loose from computational constraints, real looking and cutting-edge latent variable reaction types are thought of for complicated review and survey facts to resolve real-world difficulties. The Bayesian framework defined offers a unified process for modeling and inference, facing (nondata) earlier details and data throughout a number of info assets. The ebook discusses equipment for examining merchandise reaction information and the complicated relationships quite often linked to human reaction habit and lines • Self-contained creation to Bayesian merchandise reaction modeling and a insurance of extending typical types to deal with complicated review information • an intensive review of Bayesian estimation and trying out equipment for merchandise reaction types, the place MCMC equipment are emphasised • various examples that disguise quite a lot of software parts, together with schooling, drugs, psychology, and sociology • Datasets and software program (S+, R, and WinBUGS code) of the types and techniques offered within the ebook can be found on www.jean-paulfox.com Bayesian merchandise reaction Modeling is a superb booklet for examine execs, together with utilized statisticians, psychometricians, and social scientists who learn merchandise reaction information from a Bayesian standpoint. it's a advisor to the growing to be zone of Bayesian reaction modeling for researchers and graduate scholars, and also will serve them as a very good reference. Jean-Paul Fox is affiliate Professor of size and knowledge research, collage of Twente, The Netherlands. His major examine actions are in numerous components of Bayesian reaction modeling. Dr. Fox has released various articles within the parts of Bayesian merchandise reaction research, statistical equipment for reading multivariate express reaction info, and nonlinear combined results models.

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Additional info for Bayesian Item Response Modeling: Theory and Applications

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Estimated posterior densities of the difficulty parameters for different prior choices. enables detection of differential item functioning, among other uses. MULTILOG (Thissen, 1991) can be used specifically to perform a multiple-category IRT analysis for polytomous IRT models. PARSCALE (Muraki and Bock, 1997) is used for IRT scaling, item analysis, and scoring of rating scale data. A popular noncommercial (Dutch) program is OPLM (Verhelst, Glas and Verstralen, 1995); it can handle dichotomously or polychotomously scored items using different one-parameter models.

It follows that the prior’s variance parameter influences at least the location of the posterior mean. Specifying prior parameters is difficult when not much is known beyond the data. By defining a prior for the variance parameter, instead of fixing its value, the data are used to estimate the prior variance. This approach is advisable when no prior information is available to specify the variance. The location of each posterior mean is constructed by combining sample and prior information, where the level of uncertainty about the prior mean is estimated by the response data.

Therefore, assume that the item-level response data are independent conditional on the item-level parameters ξ k . This independence assumption leads to a withinitem and between-item structure and defines the second hierarchy of the response data. Analogous to the specification of the joint posterior of abilities, the joint posterior of the item parameters can be written as p(ξ 1 , . . , ξ K | y) ∝ p (yk | θ, ξk ) p (ξ1 , . . 2) k p (yk | θ, ξk ) p (ξk | ξP ) p (θ) dθ, ∝ k where the prior for the item parameters is structured by assuming independence conditional on the hyperparameters ξP .

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Bayesian Item Response Modeling: Theory and Applications by Jean-Paul Fox


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