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Reporting Subscore Profiles Using Diagnostic Classification Models in Health Professions Education.

Yoon Soo Park1, Amy Morales2, Linette Ross2

  • 1Department of Medical Education, College of Medicine, University of Illinois at Chicago, IL, USA.

Evaluation & the Health Professions
|August 30, 2019
PubMed
Summary
This summary is machine-generated.

Diagnostic classification models (DCMs) offer reliable subscores for health professions education assessments. This approach provides fine-grained proficiency profiles, enabling targeted feedback for learners and educators.

Keywords:
latent variablepsychometric modelsreliabilityscore reportingsubscoresvalidity

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Health Professions Education

Background:

  • Health professions education requires detailed assessment feedback beyond overall scores.
  • Concerns about reliability have limited the practical use of subscores.
  • Diagnostic classification models (DCMs) offer a solution for reliable subscore reporting.

Purpose of the Study:

  • To examine the application of DCMs for generating subscores in health professions education.
  • To assess the psychometric properties and practical utility of DCMs using large-scale assessment data.

Main Methods:

  • Retrospective analysis of National Board of Medical Examiners Subject Examinations in pathology and medicine.
  • Fitting and analyzing DCMs to generate examinee subscores and proficiency profiles.
  • Evaluating model fit, classification reliability, and parameter estimates.

Main Results:

  • DCMs demonstrated good psychometric properties and reliable examinee classification into subscore profiles.
  • Analysis revealed useful information regarding varying subscore distributions.
  • High consistency in classification indicates reliable fine-grained subscore results.

Conclusions:

  • The DCM framework is a promising approach for reporting reliable subscores in health professions education.
  • DCMs enable targeted and specific feedback to learners based on fine-grained proficiency.
  • This methodology addresses the need for more detailed assessment information in medical education.