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Retrofitting Diagnostic Classification Models to Responses From IRT-Based Assessment Forms.

Ren Liu1, Anne Corinne Huggins-Manley1, Okan Bulut2

  • 1University of Florida, Gainesville, FL, USA.

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|August 25, 2018
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Summary
This summary is machine-generated.

Retrofitting existing assessments with diagnostic classification models offers a way to gain deeper insights from examinee responses. This approach can provide actionable scores and enhance construct understanding, even if not always ideal for diagnostic measurement.

Keywords:
diagnostic classification modelingdiagnostic measurementdimensionalityitem response theoryretrofittest development

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Cognitive Science

Background:

  • Diagnostic measurement frameworks provide multidimensional feedback.
  • Existing assessments often use classical test theory or item response theory.
  • End users desire diagnostic feedback from current assessments.

Purpose of the Study:

  • To explore the feasibility of retrofitting diagnostic classification models (DCMs) to existing assessments.
  • To propose a structured framework for the retrofitting process.
  • To demonstrate the potential gains in information through an empirical example.

Main Methods:

  • Discussion of the conceptual possibilities and inherent challenges of retrofitting.
  • Development of a step-by-step procedural framework for implementation.
  • Empirical application to illustrate the information yielded by the retrofitting process.

Main Results:

  • Retrofitting DCMs to existing assessments is possible, though challenges exist.
  • A practical framework for retrofitting is proposed and demonstrated.
  • The empirical example highlights the added value of retrofitted diagnostic feedback.

Conclusions:

  • Retrofitting offers a viable, though not always optimal, path to diagnostic feedback from existing assessments.
  • The study invites further discussion on the practical application and implications of retrofitting.
  • This approach can enhance score interpretability and deepen construct understanding.