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Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items

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Title: Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items
Language: English
Authors: Huang, Hung-Yu, Wang, Wen-Chung
Source: Educational and Psychological Measurement. Jun 2013 73(3):491-511.
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: 2013
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Descriptors: Item Response Theory, Models, Bayesian Statistics, Computation, Simulation, Test Reliability, Goodness of Fit, Test Items, Monte Carlo Methods, Markov Processes, Test Bias, Junior High School Students, Minimum Competency Testing, Internet, Measures (Individuals), Foreign Countries
Geographic Terms: Taiwan
Assessment and Survey Identifiers: Graduate Record Examinations, Wechsler Adult Intelligence Scale
DOI: 10.1177/0013164412454431
ISSN: 0013-1644
Abstract: Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian approach implemented in the WinBUGS freeware for parameter estimation. A series of simulations were conducted to evaluate parameter recovery, consequences of model misspecification, and effectiveness of model-data fit statistics. Results show that the parameters of the new models can be recovered well. Ignoring the testlet effect led to a biased estimation of item parameters, underestimation of factor loadings, and overestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and the posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples of ability tests and nonability tests are given. (Contains 6 tables.)
Abstractor: As Provided
Number of References: 37
Entry Date: 2014
Accession Number: EJ1011210
Database: ERIC
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  Data: Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian approach implemented in the WinBUGS freeware for parameter estimation. A series of simulations were conducted to evaluate parameter recovery, consequences of model misspecification, and effectiveness of model-data fit statistics. Results show that the parameters of the new models can be recovered well. Ignoring the testlet effect led to a biased estimation of item parameters, underestimation of factor loadings, and overestimation of test reliability for the first-order latent traits. The Bayesian deviance information criterion and the posterior predictive model checking were helpful for model comparison and model-data fit assessment. Two empirical examples of ability tests and nonability tests are given. (Contains 6 tables.)
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      – SubjectFull: Item Response Theory
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      – SubjectFull: Models
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      – SubjectFull: Bayesian Statistics
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      – SubjectFull: Monte Carlo Methods
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      – TitleFull: Higher Order Testlet Response Models for Hierarchical Latent Traits and Testlet-Based Items
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