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ERIC Number: EJ782480
Record Type: Journal
Publication Date: 2003
Pages: 36
Abstractor: Author
Reference Count: 37
ISBN: N/A
ISSN: ISSN-1076-9986
Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models
Johnson, Matthew S.; Junker, Brian W.
Journal of Educational and Behavioral Statistics, v28 n3 p195-230 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 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. (Contains 9 figures and 7 tables.)
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A