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Multilevel Higher-Order Item Response Theory Models

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Title: Multilevel Higher-Order Item Response Theory Models
Language: English
Authors: Huang, Hung-Yu, Wang, Wen-Chung
Source: Educational and Psychological Measurement. Jun 2014 74(3):495-515.
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: 21
Publication Date: 2014
Document Type: Journal Articles
Reports - Research
Education Level: Grade 4
Intermediate Grades
Elementary Education
Higher Education
Postsecondary Education
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Computation, Test Reliability, Bayesian Statistics, Models, Markov Processes, Monte Carlo Methods, Goodness of Fit, Computer Software, Mathematics Tests, Achievement Tests, Grade 4, Elementary School Students, Student Evaluation of Teacher Performance, College Students, College Faculty, Correlation, Statistical Analysis, Foreign Countries
Geographic Terms: Taiwan
Assessment and Survey Identifiers: Students Evaluation of Educational Quality, Trends in International Mathematics and Science Study
DOI: 10.1177/0013164413509628
ISSN: 0013-1644
Abstract: In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The freeware WinBUGS was used for parameter estimation. A series of simulations were conducted to evaluate the parameter recovery and the consequence of ignoring the multilevel structure. The results indicated that the parameters were recovered fairly well; ignoring multilevel structures led to poor parameter estimation, overestimation of test reliability for the second-order latent trait, and underestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples that involve an ability test and a teaching effectiveness assessment are provided.
Abstractor: As Provided
Number of References: 37
Entry Date: 2014
Accession Number: EJ1026114
Database: ERIC
FullText Text:
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  Data: In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The freeware WinBUGS was used for parameter estimation. A series of simulations were conducted to evaluate the parameter recovery and the consequence of ignoring the multilevel structure. The results indicated that the parameters were recovered fairly well; ignoring multilevel structures led to poor parameter estimation, overestimation of test reliability for the second-order latent trait, and underestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples that involve an ability test and a teaching effectiveness assessment are provided.
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  Data: EJ1026114
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