[案例分析]Item Response Theory using WinBUGS

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关键词：response winbugs Theory WINBUG Using 案例分析

Authors:	S. McKay Curtis
Title:	[download] (13008)BUGS Code for Item Response Theory
Reference:	Vol. 36, Code Snippet 1, Aug 2010Submitted 2010-03-30, Accepted 2010-07-07
Type:	Code Snippet
Paper:	[download] (13008)BUGS Code for Item Response Theory (application/pdf, 1.4 MB)
Supplements:	[download] (2904)v36c01-code.zip: Data and code (BUGS, JAGS, R) for examples (application/zip, 20.7 KB)
Resources:	BibTeX | OAI

Authors:	Dimitris Rizopoulos
Title:	[download] (19883)ltm: An R Package for Latent Variable Modeling and Item Response Analysis
Reference:	Vol. 17, Issue 5, Nov 2006Submitted 2006-05-08, Accepted 2006-11-20
Type:	Article
Abstract:	The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.
Paper:	[download] (19883)ltm: An R Package for Latent Variable Modeling and Item Response Analysis (application/pdf, 447.3 KB)
Supplements:	[download] (2150)ltm_0.7-0.tar.gz: R source package (application/x-gzip, 73.5 KB)
	[download] (2290)v17i05.R: R example code from the paper (application/x-zip-compressed, 1.7 KB)
Resources:	BibTeX | OAI

Authors:	Jean-Paul Fox
Title:	[download] (9063)Multilevel IRT Modeling in Practice with the Package mlirt
Reference:	Vol. 20, Issue 5, Feb 2007Submitted 2006-10-01, Accepted 2007-02-22
Type:	Article
Abstract:	Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals' outcomes differ substantially when taking the measurement error in the dependent variable of the model into account. The multilevel model can be extended to handle measurement error using an item response theory (IRT) model, leading to a multilevel IRT model. This extended multilevel model is in particular suitable for the analysis of educational response data where students are nested in schools and schools are nested within cities/countries.
Paper:	[download] (9063)Multilevel IRT Modeling in Practice with the Package mlirt (application/pdf, 309.5 KB)
Supplements:	[download] (2487)Data.zip: Data sets in SPSS format (application/zip, 2 MB)
	[download] (2559)mlirt_1.0.tar.gz: R source package (application/x-gzip, 682.5 KB)
	[download] (2422)v20i05.R: R example code from the paper (application/zip, 2 KB)
Resources:	BibTeX | OAI