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ERIC Number: EJ1026114
Record Type: Journal
Publication Date: 2014-Jun
Pages: 21
Abstractor: As Provided
ISSN: ISSN-0013-1644
Multilevel Higher-Order Item Response Theory Models
Huang, Hung-Yu; Wang, Wen-Chung
Educational and Psychological Measurement, v74 n3 p495-515 Jun 2014
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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Publication Type: Journal Articles; Reports - Research
Education Level: Grade 4; Intermediate Grades; Elementary Education; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Taiwan
Identifiers - Assessments and Surveys: Students Evaluation of Educational Quality; Trends in International Mathematics and Science Study
Grant or Contract Numbers: N/A