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Improving IRT parameter estimates wi...

Foley, Brett Patrick.

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Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique./
Author:	Foley, Brett Patrick.
Description:	154 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-09, Section: A, page: 3241.
Contained By:	Dissertation Abstracts International71-09A.
Subject:	Education, Tests and Measurements. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3412859
ISBN:	9781124124025

Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique.
Foley, Brett Patrick.

Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique. - 154 p.

Source: Dissertation Abstracts International, Volume: 71-09, Section: A, page: 3241.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2010.

The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different evaluative criteria.

ISBN: 9781124124025Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique.
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020 $a 9781124124025
035 $a (UMI)AAI3412859
035 $a AAI3412859
040 $a UMI $c UMI
100 1 $a Foley, Brett Patrick. $3 1683255
245 1 0 $a Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique.
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-09, Section: A, page: 3241.
500 $a Adviser: Rafael J. De Ayala.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2010.
520 $a The 3PL model is a flexible and widely used tool in assessment. However, it suffers from limitations due to its need for large sample sizes. This study introduces and evaluates the efficacy of a new sample size augmentation technique called Duplicate, Erase, and Replace (DupER) Augmentation through a simulation study. Data are augmented using several variations of DupER Augmentation (based on different imputation methodologies, deletion rates, and duplication rates), analyzed in BILOG-MG 3, and results are compared to those obtained from analyzing the raw data. Additional manipulated variables include test length and sample size. Estimates are compared using seven different evaluative criteria.
520 $a Results are mixed and inconclusive. DupER augmented data tend to result in larger root mean squared errors (RMSEs) and lower correlations between estimates and parameters for both item and ability parameters. However, some DupER variations produce estimates that are much less biased than those obtained from the raw data alone. For one DupER variation, it was found that DupER produced better results for low-ability simulees and worse results for those with high abilities. Findings, limitations, and recommendations for future studies are discussed. Specific recommendations for future studies include the application of Duper Augmentation (1) to empirical data, (2) with additional IRT models, and (3) the analysis of the efficacy of the procedure for different item and ability parameter distributions.
590 $a School code: 0138.
650 4 $a Education, Tests and Measurements. $3 1017589
650 4 $a Education, Educational Psychology. $3 1017560
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0288
690 $a 0525
690 $a 0632
710 2 $a The University of Nebraska - Lincoln. $b Educational Psychology. $3 1022579
773 0 $t Dissertation Abstracts International $g 71-09A.
790 1 0 $a De Ayala, Rafael J., $e advisor
790 1 0 $a Ansorge, Charles J. $e committee member
790 1 0 $a Bovaird, James $e committee member
790 1 0 $a Bryant, Miles T. $e committee member
790 1 0 $a Geisinger, Kurt F. $e committee member
790 $a 0138
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3412859