応用統計学

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Local Dependence Model in Latent Rank Theory

Kojiro Shojima

英文要旨

In test data analysis, it is often important to examine the inter-item dependency structure underlying the test data. However, this structure often varies according to the academic ability level of the examinees. In this paper, a latent rank theory (LRT) model incorporating a Bayesian network model is proposed. Although the conventional LRT model is formulated under the assumption of local independence among test items, the proposed LRT model supposes local dependence among test items. The proposed model can be used to efficiently analyze the data of math and science tests in which inter-item dependency relationships among items are not negligible.


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