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Bayesian model checking strategies f...

Toribio, Sherwin G.

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Bayesian model checking strategies for dichotomous item response theory models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian model checking strategies for dichotomous item response theory models./
Author:	Toribio, Sherwin G.
Description:	199 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2066.
Contained By:	Dissertation Abstracts International67-04B.
Subject:	Education, Tests and Measurements. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3216849
ISBN:	9780542668524

Bayesian model checking strategies for dichotomous item response theory models.
Toribio, Sherwin G.

Bayesian model checking strategies for dichotomous item response theory models. - 199 p.

Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2066.

Thesis (Ph.D.)--Bowling Green State University, 2006.

Item Response Theory (IRT) models are commonly used in educational and psychological testing. These models are mainly used to assess the latent abilities of examinees and the effectiveness of the test items in measuring this underlying trait. However, model checking in Item Response Theory is still an underdeveloped area. In this dissertation, various model checking strategies from a Bayesian perspective for different Item Response models are presented. In particular, three methods are employed to assess the goodness-of-fit of different IRT models. First, Bayesian residuals and different residual plots are introduced to serve as graphical procedures to check for model fit and to detect outlying items and examinees. Second, the idea of predictive distributions is used to construct reference distributions for different test quantities and discrepancy measures, including the standard deviation of point bi-serial correlations, Bock's Pearson-type chi-square index, Yen's Q1 index, Hosmer-Lemeshow Statistic, Mckinley and Mill's G 2 index, Orlando and Thissen's S-G2 and S-X2 indices, Wright and Stone's W-statistic, and the Log-likelihood statistic. The prior, posterior, and partial posterior predictive distributions are discussed and employed. Finally, Bayes factor are used to compare different IRT models in model selection and detection of outlying discrimination parameters. In this topic, different numerical procedures to estimate the Bayes factors for these models are discussed. All of these proposed methods are illustrated using simulated data and Mathematics placement exam data from BGSU.

ISBN: 9780542668524Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

Bayesian model checking strategies for dichotomous item response theory models.
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100 1 $a Toribio, Sherwin G. $3 1921391
245 1 0 $a Bayesian model checking strategies for dichotomous item response theory models.
300 $a 199 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-04, Section: B, page: 2066.
500 $a Adviser: James H. Albert.
502 $a Thesis (Ph.D.)--Bowling Green State University, 2006.
520 $a Item Response Theory (IRT) models are commonly used in educational and psychological testing. These models are mainly used to assess the latent abilities of examinees and the effectiveness of the test items in measuring this underlying trait. However, model checking in Item Response Theory is still an underdeveloped area. In this dissertation, various model checking strategies from a Bayesian perspective for different Item Response models are presented. In particular, three methods are employed to assess the goodness-of-fit of different IRT models. First, Bayesian residuals and different residual plots are introduced to serve as graphical procedures to check for model fit and to detect outlying items and examinees. Second, the idea of predictive distributions is used to construct reference distributions for different test quantities and discrepancy measures, including the standard deviation of point bi-serial correlations, Bock's Pearson-type chi-square index, Yen's Q1 index, Hosmer-Lemeshow Statistic, Mckinley and Mill's G 2 index, Orlando and Thissen's S-G2 and S-X2 indices, Wright and Stone's W-statistic, and the Log-likelihood statistic. The prior, posterior, and partial posterior predictive distributions are discussed and employed. Finally, Bayes factor are used to compare different IRT models in model selection and detection of outlying discrimination parameters. In this topic, different numerical procedures to estimate the Bayes factors for these models are discussed. All of these proposed methods are illustrated using simulated data and Mathematics placement exam data from BGSU.
590 $a School code: 0018.
650 4 $a Education, Tests and Measurements. $3 1017589
650 4 $a Statistics. $3 517247
650 4 $a Psychology, Psychometrics. $3 1017742
690 $a 0288
690 $a 0463
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710 2 0 $a Bowling Green State University. $3 676285
773 0 $t Dissertation Abstracts International $g 67-04B.
790 1 0 $a Albert, James H., $e advisor
790 $a 0018
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3216849