東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A multilevel item response theory mo...

Nelson, Lauren Moore.

FindBook

Google Book

Amazon

博客來

A multilevel item response theory model for time structured data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A multilevel item response theory model for time structured data./
作者:	Nelson, Lauren Moore.
面頁冊數:	86 p.
附註:	Director: David Thissen.
Contained By:	Dissertation Abstracts International66-04B.
標題:	Psychology, Psychometrics. -
電子資源:	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.
LDR:02246nam 2200265 a 45 001 969790
005 20110920
008 110921s2005 eng d
020 $a 9780542067778
035 $a (UnM)AAI3170515
035 $a AAI3170515
040 $a UnM $c UnM
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
690 $a 0632
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