scholarly journals Cognitively Diagnostic Analysis Using the G-DINA Model in R

Psych ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 812-835
Author(s):  
Qingzhou Shi ◽  
Wenchao Ma ◽  
Alexander Robitzsch ◽  
Miguel A. Sorrel ◽  
Kaiwen Man
Keyword(s):  
Cognitive Diagnosis ◽  
Model Fit ◽  
Calibration Model ◽  
Cognitive Diagnosis Models ◽  
Dina Model ◽  
Hands On ◽  
R Packages ◽  
Q Matrix ◽  
Cognitively Diagnostic ◽  
Classification Reliability

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed.

On Permissible Attribute Classes in Noncompensatory Cognitive Diagnosis Models

Methodology ◽  
2014 ◽  
Vol 10 (3) ◽  
pp. 100-107 ◽  
Author(s):  
Jürgen Groß ◽  
Ann Cathrice George
Keyword(s):  
Diagnostic Information ◽  
Cognitive Diagnosis ◽  
Latent Classes ◽  
Psychometric Test ◽  
Cognitive Diagnosis Models ◽  
Dina Model

When a psychometric test has been completed by a number of examinees, an afterward analysis of required skills or attributes may improve the extraction of diagnostic information. Relying upon the retrospectively specified item-by-attribute matrix, such an investigation may be carried out by classifying examinees into latent classes, consisting of subsets of required attributes. Specifically, various cognitive diagnosis models may be applied to serve this purpose. In this article it is shown that the permission of all possible attribute combinations as latent classes can have an undesired effect in the classification process, and it is demonstrated how an appropriate elimination of specific classes may improve the classification results. As an easy example, the popular deterministic input, noisy “and” gate (DINA) model is applied to Tatsuoka’s famous fraction subtraction data, and results are compared to current discussions in the literature.

scholarly journals Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints

2020 ◽  
pp. 014662162097768
Author(s):  
Wenchao Ma ◽  
Zhehan Jiang
Keyword(s):  
Real Data ◽  
Cognitive Diagnosis ◽  
Item Parameter ◽  
Small Samples ◽  
Parameter Estimates ◽  
Cognitive Diagnosis Models ◽  
Dina Model ◽  
Item Parameter Estimates ◽  
Bayes Modal ◽  
Item Parameter Estimation

Despite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy “and” gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.

Development and Application of an Exploratory Reduced Reparameterized Unified Model

2018 ◽  
Vol 44 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Steven Andrew Culpepper ◽  
Yinghan Chen
Keyword(s):  
Bayesian Methods ◽  
Unified Model ◽  
Cognitive Diagnosis ◽  
Simulation Studies ◽  
Binary Matrix ◽  
Data Set ◽  
Cognitive Diagnosis Models ◽  
Item Parameters ◽  
Attribute Hierarchy ◽  
Q Matrix

Exploratory cognitive diagnosis models (CDMs) estimate the Q matrix, which is a binary matrix that indicates the attributes needed for affirmative responses to each item. Estimation of Q is an important next step for improving classifications and broadening application of CDMs. Prior research primarily focused on an exploratory version of the restrictive deterministic-input, noisy-and-gate model, and research is needed to develop exploratory methods for more flexible CDMs. We consider Bayesian methods for estimating an exploratory version of the more flexible reduced reparameterized unified model (rRUM). We show that estimating the rRUM Q matrix is complicated by a confound between elements of Q and the rRUM item parameters. A Bayesian framework is presented that accurately recovers Q using a spike–slab prior for item parameters to select the required attributes for each item. We present Monte Carlo simulation studies, demonstrating the developed algorithm improves upon prior Bayesian methods for estimating the rRUM Q matrix. We apply the developed method to the Examination for the Certificate of Proficiency in English data set. The results provide evidence of five attributes with a partially ordered attribute hierarchy.

scholarly journals A Deterministic Learning Algorithm Estimating the Q-Matrix for Cognitive Diagnosis Models

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3062
Author(s):  
Meng-Ta Chung ◽  
Shui-Lien Chen
Keyword(s):  
Learning Algorithm ◽  
Practical Importance ◽  
Cognitive Diagnosis ◽  
Diagnostic Assessment ◽  
Deterministic Learning ◽  
Domain Experts ◽  
The Core ◽  
Cognitive Diagnosis Models ◽  
Automated Method ◽  
Q Matrix

The goal of an exam in cognitive diagnostic assessment is to uncover whether an examinee has mastered certain attributes. Different cognitive diagnosis models (CDMs) have been developed for this purpose. The core of these CDMs is the Q-matrix, which is an item-to-attribute mapping, traditionally designed by domain experts. An expert designed Q-matrix is not without issues. For example, domain experts might neglect some attributes or have different opinions about the inclusion of some entries in the Q-matrix. It is therefore of practical importance to develop an automated method to estimate the Q-matrix. This research proposes a deterministic learning algorithm for estimating the Q-matrix. To obtain a sensible binary Q-matrix, a dichotomizing method is also devised. Results from the simulation study shows that the proposed method for estimating the Q-matrix is useful. The empirical study analyzes the ECPE data. The estimated Q-matrix is compared with the expert-designed one. All analyses in this research are carried out in R.

Evaluation of Model Fit in Cognitive Diagnosis Models

2016 ◽  
Vol 16 (2) ◽  
pp. 119-141 ◽  
Author(s):  
Jinxiang Hu ◽  
M. David Miller ◽  
Anne Corinne Huggins-Manley ◽  
Yi-Hsin Chen
Keyword(s):  
Cognitive Diagnosis ◽  
Model Fit ◽  
Cognitive Diagnosis Models

A New Method of Q-matrix Validation Based on DINA Model

2013 ◽  
Vol 44 (4) ◽  
pp. 558-568 ◽  
Author(s):  
Dong-Bo TU ◽  
Yan CAI ◽  
Hai-Qi DAI
Keyword(s):  
New Method ◽  
Dina Model ◽  
Q Matrix
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