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- W2259168301 abstract "This research focuses on developing item-level fit checking procedures in the context of diagnostic classification models (DCMs), and more specifically for the “Deterministic Input; Noisy ‘And’ gate” (DINA) model. Although there is a growing body of literature discussing model fit checking methods for DCM, the item-level fit analysis is not adequately discussed in literature. This study intends to take an initiative to fill in this gap. Two approaches are proposed, one stems from classical goodness-of-fit test statistics coupled with the Expectation-Maximization algorithm for model estimation, and the other is the posterior predictive model checking (PPMC) method coupled with the Markov chain Monte Carlo estimation. For both approaches, the chi-square statistic and a power-divergence index are considered, along with Stone’s method for considering uncertainty in latent attribute estimation. A simulation study with varying manipulated factors is carried out. Results show that both approaches are promising if Stone’s method is imposed, but the classical goodness-of-fit approach has a much higher detection rate (i.e., proportion of misfit items that are correctly detected) than the PPMC method." @default.
- W2259168301 created "2016-06-24" @default.
- W2259168301 creator A5007286116 @default.
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- W2259168301 date "2015-05-05" @default.
- W2259168301 modified "2023-10-03" @default.
- W2259168301 title "Assessing Item-Level Fit for the DINA Model" @default.
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- W2259168301 doi "https://doi.org/10.1177/0146621615583050" @default.
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- W2259168301 hasPublicationYear "2015" @default.
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