Estimating General Multidimensional Item-Response Models Using Markov Chain Monte Carlo Estimation Methods: Simulation Study and Empirical Example

2011 ◽  
Author(s):  
Jinsong Chen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Item Response ◽  
Simulation Study ◽  
Estimation Methods ◽  
Multidimensional Item Response ◽  
Response Models ◽  
Item Response Models ◽  
Monte Carlo Estimation

Markov Chain Monte Carlo for Item Response Models

2017 ◽  
pp. 271-312
Author(s):  
Brian W. Junker ◽  
Richard J. Patz ◽  
Nathan M. VanHoudnos
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Item Response ◽  
Response Models ◽  
Item Response Models

Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models

2003 ◽  
Vol 28 (3) ◽  
pp. 195-230 ◽  
Author(s):  
Matthew S. Johnson ◽  
Brian W. Junker
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Item Response ◽  
Data Augmentation ◽  
Estimation Procedure ◽  
Model Parameters ◽  
Response Model ◽  
Response Models ◽  
Mcmc Estimation

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 model using a latent response model (LRM; Maris, 1995 ). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.

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