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Estimating the DINA model parameters using the No-U-Turn Sampler.
Biom J. 2018 03; 60(2):352-368.BJ

Abstract

The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is accurate for all simulated scenarios. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process.

Authors+Show Affiliations

Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Av. Trabalhador São Carlense, 400., 13566-590, São Carlos, SP, Brasil. Departamento de Estatística, Universidade Federal de São Carlos, Rod. Washington Luiz, km 235., 13565-905, São Carlos, SP, Brasil.Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Av. Trabalhador São Carlense, 400., 13566-590, São Carlos, SP, Brasil. Departamento de Estatística, Universidade Federal de São Carlos, Rod. Washington Luiz, km 235., 13565-905, São Carlos, SP, Brasil.ACTnext, by ACT, Inc., Iowa City, IA, USA.Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Av. Trabalhador São Carlense, 400., 13566-590, São Carlos, SP, Brasil.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29194715

Citation

da Silva, Marcelo A., et al. "Estimating the DINA Model Parameters Using the No-U-Turn Sampler." Biometrical Journal. Biometrische Zeitschrift, vol. 60, no. 2, 2018, pp. 352-368.
da Silva MA, de Oliveira ESB, von Davier AA, et al. Estimating the DINA model parameters using the No-U-Turn Sampler. Biom J. 2018;60(2):352-368.
da Silva, M. A., de Oliveira, E. S. B., von Davier, A. A., & Bazán, J. L. (2018). Estimating the DINA model parameters using the No-U-Turn Sampler. Biometrical Journal. Biometrische Zeitschrift, 60(2), 352-368. https://doi.org/10.1002/bimj.201600225
da Silva MA, et al. Estimating the DINA Model Parameters Using the No-U-Turn Sampler. Biom J. 2018;60(2):352-368. PubMed PMID: 29194715.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating the DINA model parameters using the No-U-Turn Sampler. AU - da Silva,Marcelo A, AU - de Oliveira,Eduardo S B, AU - von Davier,Alina A, AU - Bazán,Jorge L, Y1 - 2017/12/01/ PY - 2016/10/31/received PY - 2017/08/18/revised PY - 2017/08/21/accepted PY - 2017/12/2/pubmed PY - 2019/3/21/medline PY - 2017/12/2/entrez KW - Beck Depression Inventory KW - DINA model KW - No-U-Turn Hamiltonian Monte Carlo KW - cognitive diagnosis SP - 352 EP - 368 JF - Biometrical journal. Biometrische Zeitschrift JO - Biom J VL - 60 IS - 2 N2 - The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. The results indicated that NUTS algorithm employed in the DINA model properly recovers all parameters and is accurate for all simulated scenarios. We apply this methodology in the mental health area in order to develop a new method of classification for respondents to the Beck Depression Inventory. The implementation of this method for the DINA model applied to other psychological tests has the potential to improve the medical diagnostic process. SN - 1521-4036 UR - https://www.unboundmedicine.com/medline/citation/29194715/Estimating_the_DINA_model_parameters_using_the_No_U_Turn_Sampler_ L2 - https://doi.org/10.1002/bimj.201600225 DB - PRIME DP - Unbound Medicine ER -