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Estimating the Q-matrix for Cognitiv...
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Chung, Meng-ta.
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Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework./
作者:
Chung, Meng-ta.
面頁冊數:
79 p.
附註:
Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:
Dissertation Abstracts International75-10B(E).
標題:
Quantitative psychology. -
電子資源:
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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Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
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Adviser: Matthew S. Johnson.
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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.
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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.
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