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A polytomous extension of the fusion...

Fu, Jianbin.

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A polytomous extension of the fusion model and its Bayesian parameter estimation.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A polytomous extension of the fusion model and its Bayesian parameter estimation./
作者:	Fu, Jianbin.
面頁冊數:	327 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1733.
Contained By:	Dissertation Abstracts International66-05A.
標題:	Education, Tests and Measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3175493
ISBN:	9780542140396

A polytomous extension of the fusion model and its Bayesian parameter estimation.
Fu, Jianbin.

A polytomous extension of the fusion model and its Bayesian parameter estimation. - 327 p.

Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1733.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2005.

Finally, in chapter 5 the findings from the study of the PFM-C are summarized. Various issues regarding the appealing features of the PFM-C, computing software, and future research are discussed.

ISBN: 9780542140396Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

A polytomous extension of the fusion model and its Bayesian parameter estimation.
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245 1 2 $a A polytomous extension of the fusion model and its Bayesian parameter estimation.
300 $a 327 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-05, Section: A, page: 1733.
500 $a Supervisor: Daniel M. Bolt.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2005.
520 $a Finally, in chapter 5 the findings from the study of the PFM-C are summarized. Various issues regarding the appealing features of the PFM-C, computing software, and future research are discussed.
520 $a The second part of this thesis (Part II) extends the fusion model to handle polytomously-scored items. In chapter 1, the fusion model and its predecessor, the unified model, are described in detail.
520 $a The first part of this thesis (Part I) provides a comprehensive review and synthesis of the existing cognitively diagnostic psychometric models (CDPMs), totaling 62 models and their relatives, focusing on the significant CDPMs as well as less frequently mentioned ones. These models are reviewed in a unified way and in the following three respects: (a) their mathematical formulations, (b) their identification conditions, estimation procedures, and model assessment techniques, and (c) their relationships to other CDPMs and their place within a taxonomy of CDPM models. This taxonomy is constructed based on some important model characteristics with regard to knowledge structure, item structure, and time component. At the end of the review, the cognitive limitations of CDPMs and interactive CDPMs are discussed, and the future development of new CDPMs is suggested.
520 $a In chapter 2, the polytomous extension of the fusion model using the cumulative score probability function (referred as to PFM-C) is proposed. A hierarchical Bayesian estimation procedure based on Markov chain Monte Carlo (MCMC) is developed. Simulation studies demonstrate the promising parameter recovery of the PFM-C for tests having high cognitive structure, especially under the highly-informative approach.
520 $a Chapter 3 evaluates ten item discrepancy statistics using posterior predictive checks and simulation data. Results suggest that second order statistics, especially the absolute item variance-covariance residuals, perform better than the first order statistics in the PFM-C in terms of false alarm rate and detecting rate.
520 $a In chapter 4 the Bayesian estimation of the PFM-C and the item fit statistics developed previously are applied to the 2000 4th grade NAEP math test. A stepwise model reduction rule, which drops the insignificant or inestimable parameters from the models, is proposed to improve model robustness and interpretability.
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791 $a Ph.D.
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