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• W2332233792 abstract "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 different orders and influence the item responses of examinees from different classes. This study aims to develop a new class of higher-order mixture IRT models by integrating mixture IRT models and higher-order IRT models to address these practical concerns. The proposed higher-order mixture IRT models can accommodate both linear and nonlinear models for latent traits and incorporate diverse item response functions. The Rasch model was selected as the item response function, metric invariance was assumed in the first simulation study, and multiparameter IRT models without an assumption of metric invariance were used in the second simulation study. The results show that the parameters can be recovered fairly well using WinBUGS with Bayesian estimation. A larger sample size resulted in a better estimate of the model parameters, and a longer test length yielded better individual ability recovery and latent class membership recovery. The linear approach outperformed the nonlinear approach in the estimation of first-order latent traits, whereas the opposite was true for the estimation of the second-order latent trait. Additionally, imposing identical factor loadings between the second- and first-order latent traits by fitting the mixture bifactor model resulted in biased estimates of the first-order latent traits and item parameters. Finally, two empirical analyses are provided as an example to illustrate the applications and implications of the new models." @default.
• W2332233792 created "2016-06-24" @default.
• W2332233792 creator A5054766062 @default.
• W2332233792 date "2016-07-11" @default.
• W2332233792 modified "2023-09-25" @default.
• W2332233792 title "Mixture IRT Model With a Higher-Order Structure for Latent Traits" @default.
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• W2332233792 doi "https://doi.org/10.1177/0013164416640327" @default.
• W2332233792 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5965539" @default.
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• W2332233792 hasPublicationYear "2016" @default.
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