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Higher-Order Item Response Models for Hierarchical Latent Traits

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Title: Higher-Order Item Response Models for Hierarchical Latent Traits
Language: English
Authors: Huang, Hung-Yu, Wang, Wen-Chung, Chen, Po-Hsi, Su, Chi-Ming
Source: Applied Psychological Measurement. Nov 2013 37(8):619-637.
Availability: 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
Peer Reviewed: Y
Page Count: 19
Publication Date: 2013
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Higher Education
Postsecondary Education
Descriptors: Item Response Theory, Models, Vertical Organization, Bayesian Statistics, Markov Processes, Monte Carlo Methods, Computer Software, Computation, Achievement Tests, Personality Assessment, Junior High School Students, Internet, Addictive Behavior, Psychological Patterns, College Students, Factor Analysis, Foreign Countries
Geographic Terms: Taiwan
DOI: 10.1177/0146621613488819
ISSN: 0146-6216
Abstract: Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.
Abstractor: As Provided
Number of References: 30
Entry Date: 2014
Accession Number: EJ1019141
Database: ERIC
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  Data: 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
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  Data: Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.
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  Label: Entry Date
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  Data: 2014
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      – SubjectFull: Models
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      – SubjectFull: Vertical Organization
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      – SubjectFull: Bayesian Statistics
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