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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.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Markov chain Monte Carlo approach to confirmatory item factor analysis./
Author:	Edwards, Michael C.
Description:	139 p.
Notes:	Adviser: David Thissen.
Contained By:	Dissertation Abstracts International67-01B.
Subject:	Psychology, Psychometrics. -
Online resource:	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.
LDR:01737nam 2200265 a 45 001 969892
005 20110920
008 110921s2005 eng d
020 $a 9780542484728
035 $a (UnM)AAI3200762
035 $a AAI3200762
040 $a UnM $c UnM
100 1 $a Edwards, Michael C. $3 1293948
245 1 2 $a A Markov chain Monte Carlo approach to confirmatory item factor analysis.
300 $a 139 p.
500 $a Adviser: David Thissen.
500 $a Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0595.
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.
590 $a School code: 0153.
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0632
710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 67-01B.
790 $a 0153
790 1 0 $a Thissen, David, $e advisor
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3200762