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J Educ Eval Health Prof > Epub ahead of print
J Educ Eval Health Prof. 2021; 18: 15.
Published online July 5, 2021.
DOI: https://doi.org/10.3352/jeehp.2021.18.15
[Epub ahead of print]
The accuracy and consistency of mastery for each content domain using the Rasch and deterministic inputs, noisy “and” gate diagnostic classification models: a simulation study and a real-world analysis using data from the Korean Medical Licensing Examination
Dong Gi Seo1,2  , Jae Kum Kim3 
1Department of Psychology, College of Social Science, Hallym University, Chuncheon, Korea
2Hallym Applied Psychology Institute, College of Social Science, Hallym University, Chuncheon, Korea
3Korea International University in Ferghana, Ferghana, Uzbekistan
Correspondence  Dong Gi Seo ,Email: wmotive@hallym.ac.kr
Editor:  Sun Huh, Hallym University, Korea
Submitted: June 3, 2021  Accepted after revision: June 22, 2021
Abstract
Purpose
Diagnostic classification models (DCMs) were developed to identify the mastery or non-mastery of the attributes required for solving test items, but their application has been limited to very low-level attributes, and the accuracy and consistency of high-level attributes using DCMs have rarely been reported compared with classical test theory (CTT) and item response theory models. This paper compared the accuracy of high-level attribute mastery between deterministic inputs, noisy “and” gate (DINA) and Rasch models, along with sub-scores based on CTT.
Methods
First, a simulation study explored the effects of attribute length (number of items per attribute) and the correlations among attributes with respect to the accuracy of mastery. Second, a real-data study examined model and item fit and investigated the consistency of mastery for each attribute among the 3 models using the 2017 Korean Medical Licensing Examination with 360 items.
Results
Accuracy of mastery increased with a higher number of items measuring each attribute across all conditions. The DINA model was more accurate than the CTT and Rasch models for attributes with high correlations (>0.5) and few items. In the real-data analysis, the DINA and Rasch models generally showed better item fits and appropriate model fit. The consistency of mastery between the Rasch and DINA models ranged from 0.541 to 0.633 and the correlations of person attribute scores between the Rasch and DINA models ranged from 0.579 to 0.786.
Conclusion
Although all 3 models provide a mastery decision for each examinee, the individual mastery profile using the DINA model provides more accurate decisions for attributes with high correlations than the CTT and Rasch models. The DINA model can also be directly applied to tests with complex structures, unlike the CTT and Rasch models, and it provides different diagnostic information from the CTT and Rasch models.
Keywords: Data collection; Data analysis; Psychometrics; Republic of Korea; Statistical models
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