TY - JOUR
T1 - Bayesian Estimation of the DINA Q matrix
AU - Chen, Yinghan
AU - Culpepper, Steven Andrew
AU - Chen, Yuguo
AU - Douglas, Jeffrey
N1 - Publisher Copyright:
© 2017, The Psychometric Society.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Cognitive diagnosis models are partially ordered latent class models and are used to classify students into skill mastery profiles. The deterministic inputs, noisy “and” gate model (DINA) is a popular psychometric model for cognitive diagnosis. Application of the DINA model requires content expert knowledge of a Q matrix, which maps the attributes or skills needed to master a collection of items. Misspecification of Q has been shown to yield biased diagnostic classifications. We propose a Bayesian framework for estimating the DINA Q matrix. The developed algorithm builds upon prior research (Chen, Liu, Xu, & Ying, in J Am Stat Assoc 110(510):850–866, 2015) and ensures the estimated Q matrix is identified. Monte Carlo evidence is presented to support the accuracy of parameter recovery. The developed methodology is applied to Tatsuoka’s fraction-subtraction dataset.
AB - Cognitive diagnosis models are partially ordered latent class models and are used to classify students into skill mastery profiles. The deterministic inputs, noisy “and” gate model (DINA) is a popular psychometric model for cognitive diagnosis. Application of the DINA model requires content expert knowledge of a Q matrix, which maps the attributes or skills needed to master a collection of items. Misspecification of Q has been shown to yield biased diagnostic classifications. We propose a Bayesian framework for estimating the DINA Q matrix. The developed algorithm builds upon prior research (Chen, Liu, Xu, & Ying, in J Am Stat Assoc 110(510):850–866, 2015) and ensures the estimated Q matrix is identified. Monte Carlo evidence is presented to support the accuracy of parameter recovery. The developed methodology is applied to Tatsuoka’s fraction-subtraction dataset.
KW - Bayesian statistics
KW - Q matrix
KW - cognitive diagnosis models
KW - deterministic inputs
KW - fraction-subtraction data
KW - noisy “and” gate (DINA) model
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U2 - 10.1007/s11336-017-9579-4
DO - 10.1007/s11336-017-9579-4
M3 - Article
C2 - 28861685
AN - SCOPUS:85028770102
SN - 0033-3123
VL - 83
SP - 89
EP - 108
JO - Psychometrika
JF - Psychometrika
IS - 1
ER -