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.

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.

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