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Estimating the Q-matrix for Cognitiv...

Chung, Meng-ta.

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Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework./
Author:	Chung, Meng-ta.
Description:	79 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
Subject:	Quantitative psychology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3622690
ISBN:	9781303945779

Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework.
Chung, Meng-ta.

Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework. - 79 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2014.

This research aims to develop an MCMC algorithm for estimating the Q-matrix in a Bayesian framework. A saturated multinomial model was used to estimate correlated attributes in the DINA model and rRUM. Closed-forms of posteriors for guess and slip parameters were derived for the DINA model. The random walk Metropolis-Hastings algorithm was applied to parameter estimation in the rRUM. An algorithm for reducing potential label switching was incorporated into the estimation procedure. A method for simulating data with correlated attributes for the DINA model and rRUM was offered.

ISBN: 9781303945779Subjects--Topical Terms:

2144748
Quantitative psychology.

Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework.
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020 $a 9781303945779
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100 1 $a Chung, Meng-ta. $3 3180000
245 1 0 $a Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework.
300 $a 79 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Adviser: Matthew S. Johnson.
502 $a Thesis (Ph.D.)--Columbia University, 2014.
520 $a This research aims to develop an MCMC algorithm for estimating the Q-matrix in a Bayesian framework. A saturated multinomial model was used to estimate correlated attributes in the DINA model and rRUM. Closed-forms of posteriors for guess and slip parameters were derived for the DINA model. The random walk Metropolis-Hastings algorithm was applied to parameter estimation in the rRUM. An algorithm for reducing potential label switching was incorporated into the estimation procedure. A method for simulating data with correlated attributes for the DINA model and rRUM was offered.
520 $a Three simulation studies were conducted to evaluate the algorithm for Bayesian estimation. Twenty simulated data sets for simulation study 1.were generated from independent attributes for the DINA model and rRUM. A hundred data sets from correlated attributes were generated for the DINA and rRUM with guess and slip parameters set to 0.2 in simulation study 2. Simulation study 3 analyzed data sets simulated from the DINA model with guess and slip parameters generated from Uniform (0.1, 0.4). Results from simulation studies showed that the Q-matrix recovery rate was satisfactory. Using the fraction-subtraction data, an empirical study was conducted for the DINA model and rRUM. The estimated Q-matrices from the two models were compared with the expert-designed Q-matrix.
590 $a School code: 0054.
650 4 $a Quantitative psychology. $3 2144748
650 4 $a Statistics. $3 517247
650 4 $a Educational tests & measurements. $3 3168483
690 $a 0632
690 $a 0463
690 $a 0288
710 2 $a Columbia University. $b Measurement and Evaluation. $3 3180001
773 0 $t Dissertation Abstracts International $g 75-10B(E).
790 $a 0054
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3622690