scholarly journals Implementation of Cognitive Diagnosis Modeling using the GDINA R Package

2019 ◽  
Vol 19 (80) ◽  
pp. 1-22
Author(s):  
Jimmy de la TORRE ◽  
Lokman AKBAY
Keyword(s):  
R Package ◽  
Cognitive Diagnosis ◽  
Cognitive Diagnosis Modeling

Adjusting Person Fit Index for Skewness in Cognitive Diagnosis Modeling

2019 ◽  
Vol 37 (2) ◽  
pp. 399-420
Author(s):  
Kevin Carl P. Santos ◽  
Jimmy de la Torre ◽  
Matthias von Davier
Keyword(s):  
Cognitive Diagnosis ◽  
Person Fit ◽  
Person Fit Index ◽  
Fit Index ◽  
Cognitive Diagnosis Modeling

Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling

2013 ◽  
Vol 50 (2) ◽  
pp. 123-140 ◽  
Author(s):  
Jinsong Chen ◽  
Jimmy de la Torre ◽  
Zao Zhang
Keyword(s):  
Cognitive Diagnosis ◽  
Cognitive Diagnosis Modeling

scholarly journals The R Package CDM for Cognitive Diagnosis Models

2016 ◽  
Vol 74 (2) ◽  
Author(s):  
Ann Cathrice George ◽  
Alexander Robitzsch ◽  
Thomas Kiefer ◽  
Jürgen Groß ◽  
Ali Ünlü
Keyword(s):  
R Package ◽  
Cognitive Diagnosis ◽  
Cognitive Diagnosis Models

scholarly journals Assessing Change in Latent Skills Across Time With Longitudinal Cognitive Diagnosis Modeling: An Evaluation of Model Performance

2016 ◽  
Vol 77 (3) ◽  
pp. 369-388 ◽  
Author(s):  
Yasemin Kaya ◽  
Walter L. Leite
Keyword(s):  
Latent Variables ◽  
Model Performance ◽  
Repeated Measurements ◽  
Cognitive Diagnosis ◽  
Longitudinal Models ◽  
Monte Carlo Simulation Study ◽  
Transition Analysis ◽  
Cognitive Diagnosis Models ◽  
Study Results ◽  
Cognitive Diagnosis Modeling

Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor attribute stability of individuals and to account for respondent dependence. Models based on combining latent transition analysis modeling and the DINA and DINO cognitive diagnosis models were developed and then evaluated through a Monte Carlo simulation study. The study results indicate that the proposed models provide adequate convergence and correct classification rates.

scholarly journals Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism

2020 ◽  
Vol 11 ◽  
Author(s):  
Na Shan ◽  
Xiaofei Wang
Keyword(s):  
Missing Data ◽  
International Student ◽  
Student Assessment ◽  
Cognitive Diagnosis ◽  
Parameter Recovery ◽  
International Student Assessment ◽  
Item Level ◽  
Item Responses ◽  
Computer Based ◽  
Cognitive Diagnosis Modeling

The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur. Even if the primary interest is to provide diagnostic classification of respondents, misspecification of missing data mechanism may lead to biased conclusions. This paper proposes a joint cognitive diagnosis modeling of item responses and item-level missing data mechanism. A Bayesian Markov chain Monte Carlo (MCMC) method is developed for model parameter estimation. Our simulation studies examine the parameter recovery under different missing data mechanisms. The parameters could be recovered well with correct use of missing data mechanism for model fit, and missing that is not at random is less sensitive to incorrect use. The Program for International Student Assessment (PISA) 2015 computer-based mathematics data are applied to demonstrate the practical value of the proposed method.

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