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A multilevel item response theory mo...

Nelson, Lauren Moore.

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A multilevel item response theory model for time structured data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A multilevel item response theory model for time structured data./
Author:	Nelson, Lauren Moore.
Description:	86 p.
Notes:	Director: David Thissen.
Contained By:	Dissertation Abstracts International66-04B.
Subject:	Psychology, Psychometrics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3170515
ISBN:	9780542067778

A multilevel item response theory model for time structured data.
Nelson, Lauren Moore.

A multilevel item response theory model for time structured data. - 86 p.

Director: David Thissen.

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

The feasibility of a Markov Chain Monte Carlo (MCMC) method to estimate a multi-level Item Response Theory (IRT) model for the repeated assessments on individuals was examined. The IRT portion of the model was limited to uni-dimensional constructs having polytomous responses. This approach was applied to simulated data to demonstrate its ability to recover known parameter estimates, and on data obtained from the National Center for early Development and Learning Multi-State Study of Pre-kindergarten (NCEDL) study to determine experimental parameters. The MCMC techniques used Gibbs sampling in conjunction with a data augmentation procedure. Results were compared to estimates obtained under Maximum Likelihood methods. For the simulated data, the parameter recovery was successful for the IRT slope and location parameters, showing strong associations for fixed and random effects. Recovery proved less successful for the IRT offset and structural parameters with the expected weaker associations. From results of the real-life NCEDL study, it was recognized that small-sized clusters lack sufficient information for these complex models. The different methods gave similar estimates although the conclusions differed for the NCEDL application. Implications for future research are discussed including the effects of MCMC adjustments, missing data, and model extension.

ISBN: 9780542067778Subjects--Topical Terms:

1017742
Psychology, Psychometrics.

A multilevel item response theory model for time structured data.
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020 $a 9780542067778
035 $a (UnM)AAI3170515
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100 1 $a Nelson, Lauren Moore. $3 1293847
245 1 2 $a A multilevel item response theory model for time structured data.
300 $a 86 p.
500 $a Director: David Thissen.
500 $a Source: Dissertation Abstracts International, Volume: 66-04, Section: B, page: 2336.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2005.
520 $a The feasibility of a Markov Chain Monte Carlo (MCMC) method to estimate a multi-level Item Response Theory (IRT) model for the repeated assessments on individuals was examined. The IRT portion of the model was limited to uni-dimensional constructs having polytomous responses. This approach was applied to simulated data to demonstrate its ability to recover known parameter estimates, and on data obtained from the National Center for early Development and Learning Multi-State Study of Pre-kindergarten (NCEDL) study to determine experimental parameters. The MCMC techniques used Gibbs sampling in conjunction with a data augmentation procedure. Results were compared to estimates obtained under Maximum Likelihood methods. For the simulated data, the parameter recovery was successful for the IRT slope and location parameters, showing strong associations for fixed and random effects. Recovery proved less successful for the IRT offset and structural parameters with the expected weaker associations. From results of the real-life NCEDL study, it was recognized that small-sized clusters lack sufficient information for these complex models. The different methods gave similar estimates although the conclusions differed for the NCEDL application. Implications for future research are discussed including the effects of MCMC adjustments, missing data, and model extension.
590 $a School code: 0153.
650 4 $a Psychology, Psychometrics. $3 1017742
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710 2 0 $a The University of North Carolina at Chapel Hill. $3 1017449
773 0 $t Dissertation Abstracts International $g 66-04B.
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=3170515