scholarly journals Cognitive Diagnostic Models for Rater Effects

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaomin Li ◽  
Wen-Chung Wang ◽  
Qin Xie
Keyword(s):  
Rater Effects ◽  
Diagnostic Models ◽  
Cognitive Diagnostic Models

Assessing Fit of Cognitive Diagnostic Models A Case Study

2007 ◽  
Vol 67 (2) ◽  
pp. 239-257 ◽  
Author(s):  
Sandip Sinharay ◽  
Russell G. Almond
Keyword(s):  
Diagnostic Models ◽  
Cognitive Diagnostic Models

scholarly journals Examining the Impact of Differential Item Functioning on Classification Accuracy in Cognitive Diagnostic Models

2019 ◽  
Vol 44 (4) ◽  
pp. 267-281 ◽  
Author(s):  
Justin Paulsen ◽  
Dubravka Svetina ◽  
Yanan Feng ◽  
Montserrat Valdivia
Keyword(s):  
Differential Item Functioning ◽  
Classification Accuracy ◽  
Simple Structure ◽  
Future Directions ◽  
Content Areas ◽  
Item Functioning ◽  
Diagnostic Models ◽  
Cognitive Diagnostic Models ◽  
The Impact ◽  
Item Types

Cognitive diagnostic models (CDMs) are of growing interest in educational research because of the models’ ability to provide diagnostic information regarding examinees’ strengths and weaknesses suited to a variety of content areas. An important step to ensure appropriate uses and interpretations from CDMs is to understand the impact of differential item functioning (DIF). While methods of detecting DIF in CDMs have been identified, there is a limited understanding of the extent to which DIF affects classification accuracy. This simulation study provides a reference to practitioners to understand how different magnitudes and types of DIF interact with CDM item types and group distributions and sample sizes to influence attribute- and profile-level classification accuracy. The results suggest that attribute-level classification accuracy is robust to DIF of large magnitudes in most conditions, while profile-level classification accuracy is negatively influenced by the inclusion of DIF. Conditions of unequal group distributions and DIF located on simple structure items had the greatest effect in decreasing classification accuracy. The article closes by considering implications of the results and future directions.

A Simulation Study to Compare Five Cognitive Diagnostic Models

2013 ◽  
Vol 45 (11) ◽  
pp. 1295-1304 ◽  
Author(s):  
Yan CAI ◽  
Dongbo TU ◽  
Shuliang DING
Keyword(s):  
Simulation Study ◽  
Diagnostic Models ◽  
Cognitive Diagnostic Models

scholarly journals Cognitive Diagnostic Models With Attribute Hierarchies: Model Estimation With a Restricted Q-Matrix Design

2018 ◽  
Vol 43 (4) ◽  
pp. 255-271 ◽  
Author(s):  
Dongbo Tu ◽  
Shiyu Wang ◽  
Yan Cai ◽  
Jeff Douglas ◽  
Hua-Hua Chang
Keyword(s):  
Classification Accuracy ◽  
Latent Class ◽  
Estimation Procedure ◽  
Syllogistic Reasoning ◽  
Estimation Methods ◽  
Diagnostic Models ◽  
Cognitive Diagnostic Models ◽  
Attribute Hierarchy ◽  
Q Matrix ◽  
Matrix Design

Attribute hierarchy is a common assumption in the educational context, where the mastery of one attribute is assumed to be a prerequisite to the mastery of another one. The attribute hierarchy can be incorporated through a restricted Q matrix that implies the specified structure. The latent class–based cognitive diagnostic models (CDMs) usually do not assume a hierarchical structure among attributes, which means all profiles of attributes are possible in a population of interest. This study investigates different estimation methods to the classification accuracy for a family of CDMs when they are combined with a restricted Q-matrix design. A simulation study is used to explain the misclassification caused by an unrestricted estimation procedure. The advantages of the restricted estimation procedure utilizing attribute hierarchies for increased classification accuracy are also further illustrated through a real data analysis on a syllogistic reasoning diagnostic assessment. This research can provide guidelines for educational and psychological researchers and practitioners when they use CDMs to analyze the data with a restricted Q-matrix design and make them be aware of the potentially contaminated classification results if ignoring attribute hierarchies.

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