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A Markov chain Monte Carlo approach ...

Edwards, Michael C.

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A Markov chain Monte Carlo approach to confirmatory item factor analysis.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Markov chain Monte Carlo approach to confirmatory item factor analysis./
作者:	Edwards, Michael C.
面頁冊數:	139 p.
附註:	Adviser: David Thissen.
Contained By:	Dissertation Abstracts International67-01B.
標題:	Psychology, Psychometrics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200762
ISBN:	9780542484728

A Markov chain Monte Carlo approach to confirmatory item factor analysis.
Edwards, Michael C.

A Markov chain Monte Carlo approach to confirmatory item factor analysis. - 139 p.

Adviser: David Thissen.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2005.

Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. While great strides have been made in the past three decades in parameter estimation for these types of models, significant limitations remain. The goal of the present research is to examine the feasibility of using Markov chain Monte Carlo (MCMC) estimation methods to estimate parameters of a wide variety of Confirmatory item factor analysis models. After providing an overview of item factor analysis and MCMC, I provide a detailed description of the MCMC estimation routine implemented for this research. Following this I review the accuracy of MCMC estimation for 5 simulated examples. These results suggest that MCMC is capable of providing reasonable parameter estimates for a wide array of Confirmatory item factor analysis models.

ISBN: 9780542484728Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

A Markov chain Monte Carlo approach to confirmatory item factor analysis.
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502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2005.
520 $a Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. While great strides have been made in the past three decades in parameter estimation for these types of models, significant limitations remain. The goal of the present research is to examine the feasibility of using Markov chain Monte Carlo (MCMC) estimation methods to estimate parameters of a wide variety of Confirmatory item factor analysis models. After providing an overview of item factor analysis and MCMC, I provide a detailed description of the MCMC estimation routine implemented for this research. Following this I review the accuracy of MCMC estimation for 5 simulated examples. These results suggest that MCMC is capable of providing reasonable parameter estimates for a wide array of Confirmatory item factor analysis models.
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