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Huang, Hung-Yu – Educational and Psychological Measurement, 2017
Mixture item response theory (IRT) models have been suggested as an efficient method of detecting the different response patterns derived from latent classes when developing a test. In testing situations, multiple latent traits measured by a battery of tests can exhibit a higher-order structure, and mixtures of latent classes may occur on…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Computation
Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 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…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Computation, Test Reliability
Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2013
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…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Computation
Wu, Chia-Huei; Chen, Lung Hung – Educational and Psychological Measurement, 2010
In 2001, Elliot and McGregor proposed a 2 x 2 (mastery-performance x approach- avoidance) achievement goal frameworks and developed a questionnaire to measure four goals (mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance goals). This study examines the dual meanings of items in 2 x 2 achievement goal…
Descriptors: Questionnaires, Achievement, Objectives, Factor Analysis