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Direct linkZhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Journal of Educational Measurement, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Download full textZhang, Xue; Tao, Jian; Wang, Chun; Shi, Ning-Zhong – Grantee Submission, 2019
Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item…
Descriptors: Bayesian Statistics, Item Response Theory, Measurement, Models
Wang, Chun; Nydick, Steven W. – Journal of Educational and Behavioral Statistics, 2020
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and…
Descriptors: Item Response Theory, Longitudinal Studies, Comparative Analysis, Models
Wang, Chun; Nydick, Steven W. – Grantee Submission, 2019
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve (LGC) model (e.g., McArdle, 1988) and extended the assessment of growth to multidimensional IRT models (e.g., Hsieh, von Eye, & Maier, 2010; Huang, 2013) and higher-order IRT models…
Descriptors: Longitudinal Studies, Item Response Theory, Comparative Analysis, Models
