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• W2971330264 abstract "Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items." @default.
• W2971330264 created "2019-09-05" @default.
• W2971330264 creator A5067741681 @default.
• W2971330264 creator A5071957884 @default.
• W2971330264 date "2019-08-27" @default.
• W2971330264 modified "2023-09-29" @default.
• W2971330264 title "A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats" @default.
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• W2971330264 doi "https://doi.org/10.1177/0013164419871659" @default.
• W2971330264 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7221495" @default.
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