Convergence Between Cluster Analysis and the Angoff Method for Setting Minimum Passing Scores on Credentialing Examinations
| Autor: | Raja Subhiyah, Carolyn Giordano, Brian J. Hess |
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| Rok vydání: | 2007 |
| Předmět: |
Models
Educational Stability (learning theory) computer.software_genre Credentialing 0504 sociology Consistency (statistics) Convergence (routing) Statistics Cluster (physics) Cluster Analysis Humans Analysis method Mathematics Primary Health Care Health Policy 05 social sciences Reproducibility of Results 050401 social sciences methods 050301 education United States Test (assessment) Clinical Competence Educational Measurement Data mining Clinical Medicine 0503 education computer |
| Zdroj: | Evaluation & the Health Professions. 30:362-375 |
| ISSN: | 1552-3918 0163-2787 |
| Popis: | Cluster analysis can be a useful statistical technique for setting minimum passing scores on high-stakes examinations by grouping examinees into homogenous clusters based on their responses to test items. It has been most useful for supplementing data or validating minimum passing scores determined from expert judgment approaches, such as the Ebel and Nedelsky methods. However, there is no evidence supporting how well cluster analysis converges with the modified Angoff method, which is frequently used in medical credentialing. Therefore, the purpose of this study is to investigate the efficacy of cluster analysis for validating Angoff-derived minimum passing scores. Data are from 652 examinees who took a national credentialing examination based on a content-by-process test blueprint. Results indicate a high degree of consistency in minimum passing score estimates derived from the modified Angoff and cluster analysis methods. However, the stability of the estimates from cluster analysis across different samples was modest. |
| Databáze: | OpenAIRE |
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