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

@article{Johnson03, abstract = {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. 10.3102/10769986028003195}, author = {Johnson, Matthew S. and Junker, Brian W.}, citeulike-article-id = {5219595}, citeulike-linkout-0 = {http://dx.doi.org/10.3102/10769986028003195}, citeulike-linkout-1 = {http://jeb.sagepub.com/cgi/content/abstract/28/3/195}, day = {1}, doi = {10.3102/10769986028003195}, journal = {JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS}, keywords = {model, proximity}, month = {January}, number = {3}, pages = {195--230}, posted-at = {2009-07-21 16:50:33}, priority = {5}, title = {Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models}, url = {http://dx.doi.org/10.3102/10769986028003195}, volume = {28}, year = {2003} }

TY - JOUR ID - Johnson03 L3 - citeulike-article-id:5219595 N2 - 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. 10.3102/10769986028003195 IS - 3 JF - JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS EP - 230 TI - Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models VL - 28 SP - 195 KW - model KW - proximity AU - Johnson, Matthew AU - Junker, Brian PY - 2003/01/01/ UR - http://dx.doi.org/10.3102/10769986028003195 ER -