Journal of Educational Evaluation for Health Professions

Research article

Usefulness of the DETECT program for assessing the internal structure of dimensionality in simulated data and results of the Korean nursing licensing examination: Dong Gi Seo, Younyoung Choi, Sun Huh; J Educ Eval Health Prof. 2017;14:32. Published online December 27, 2017; DOI: https://doi.org/10.3352/jeehp.2017.14.32

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Purpose
The dimensionality of examinations provides empirical evidence of the internal test structure underlying the responses to a set of items. In turn, the internal structure is an important piece of evidence of the validity of an examination. Thus, the aim of this study was to investigate the performance of the DETECT program and to use it to examine the internal structure of the Korean nursing licensing examination.
Methods
Non-parametric methods of dimensional testing, such as the DETECT program, have been proposed as ways of overcoming the limitations of traditional parametric methods. A non-parametric method (the DETECT program) was investigated using simulation data under several conditions and applied to the Korean nursing licensing examination.
Results
The DETECT program performed well in terms of determining the number of underlying dimensions under several different conditions in the simulated data. Further, the DETECT program correctly revealed the internal structure of the Korean nursing licensing examination, meaning that it detected the proper number of dimensions and appropriately clustered the items within each dimension.
Conclusion
The DETECT program performed well in detecting the number of dimensions and in assigning items for each dimension. This result implies that the DETECT method can be useful for examining the internal structure of assessments, such as licensing examinations, that possess relatively many domains and content areas.

Citations

Citations to this article as recorded by

Meanings of Rough Sex across Gender, Sexual Identity, and Political Ideology: A Conditional Covariance Approach
Dubravka Svetina Valdivia, Debby Herbenick, Tsung-chieh Fu, Heather Eastman-Mueller, Lucia Guerra-Reyes, Molly Rosenberg
Journal of Sex & Marital Therapy.2022; 48(6): 579. CrossRef
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 Examinat
Dong Gi Seo, Jae Kum Kim
Journal of Educational Evaluation for Health Professions.2021; 18: 15. CrossRef
Estimation of item parameters and examinees’ mastery probability in each domain of the Korean Medical Licensing Examination using a deterministic inputs, noisy “and” gate (DINA) model
Younyoung Choi, Dong Gi Seo
Journal of Educational Evaluation for Health Professions.2020; 17: 35. CrossRef
Linear programming method to construct equated item sets for the implementation of periodical computer-based testing for the Korean Medical Licensing Examination
Dong Gi Seo, Myeong Gi Kim, Na Hui Kim, Hye Sook Shin, Hyun Jung Kim
Journal of Educational Evaluation for Health Professions.2018; 15: 26. CrossRef

Original Article

Applicability of Item Response Theory to the Korean Nurses' Licensing Examination: Geum-Hee Jeong, Mi Kyoung Yim; J Educ Eval Health Prof. 2005;2(1):23-29. Published online June 30, 2005; DOI: https://doi.org/10.3352/jeehp.2005.2.1.23

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Abstract PDF

To test the applicability of item response theory (IRT) to the Korean Nurses' Licensing Examination (KNLE), item analysis was performed after testing the unidimensionality and goodness-of-fit. The results were compared with those based on classical test theory. The results of the 330-item KNLE administered to 12,024 examinees in January 2004 were analyzed. Unidimensionality was tested using DETECT and the goodness-of-fit was tested using WINSTEPS for the Rasch model and Bilog-MG for the two-parameter logistic model. Item analysis and ability estimation were done using WINSTEPS. Using DETECT, Dmax ranged from 0.1 to 0.23 for each subject. The mean square value of the infit and outfit values of all items using WINSTEPS ranged from 0.1 to 1.5, except for one item in pediatric nursing, which scored 1.53. Of the 330 items, 218 (42.7%) were misfit using the two-parameter logistic model of Bilog-MG. The correlation coefficients between the difficulty parameter using the Rasch model and the difficulty index from classical test theory ranged from 0.9039 to 0.9699. The correlation between the ability parameter using the Rasch model and the total score from classical test theory ranged from 0.9776 to 0.9984. Therefore, the results of the KNLE fit unidimensionality and goodness-of-fit for the Rasch model. The KNLE should be a good sample for analysis according to the IRT Rasch model, so further research using IRT is possible.

Citations

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Item difficulty index, discrimination index, and reliability of the 26 health professions licensing examinations in 2022, Korea: a psychometric study
Yoon Hee Kim, Bo Hyun Kim, Joonki Kim, Bokyoung Jung, Sangyoung Bae
Journal of Educational Evaluation for Health Professions.2023; 20: 31. CrossRef
Study on the Academic Competency Assessment of Herbology Test using Rasch Model
Han Chae, Soo Jin Lee, Chang-ho Han, Young Il Cho, Hyungwoo Kim
Journal of Korean Medicine.2022; 43(2): 27. CrossRef
Can computerized tests be introduced to the Korean Medical Licensing Examination?
Sun Huh
Journal of the Korean Medical Association.2012; 55(2): 124. CrossRef

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