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 |
| 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. |
|---|---|
| ISSN: | 0146-6216 |
| DOI: | 10.1177/0146621613488819 |
