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Related Experiment Video

Updated: Oct 15, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model.

Steven P Reise1, Anne S Hubbard1, Emily F Wong1

  • 1University of California, Los Angeles, USA.

Assessment
|October 28, 2021
PubMed
Summary

Collapsing response categories in the Health Care Engagement Measure (HEM) improved its psychometric properties. This analysis highlights the importance of response scale design for accurate patient-reported outcome measurement.

Keywords:
category boundary discriminationitem discriminationitem response theorynominal response modelpatient engagement

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Area of Science:

  • Psychometrics
  • Health Services Research
  • Item Response Theory

Background:

  • The Health Care Engagement Measure (HEM) is a patient-reported outcome measure.
  • Scale development requires rigorous psychometric evaluation of response formats.

Purpose of the Study:

  • To evaluate the psychometric properties of the HEM using a nominal response item response theory model.
  • To assess the impact of response category structure on measurement precision.

Main Methods:

  • A nominal response item response theory (IRTree) model was applied to HEM data.
  • Response categories were analyzed for intended ordering and discrimination.
  • The effect of collapsing response categories was examined.

Main Results:

  • Six of 23 items did not have intended category ordering with the original 5-point format.
  • Lower response categories (0 and 1) showed weak discrimination, indicating uninformative response options.
  • Collapsing the two lowest categories significantly improved psychometric properties.
  • Higher response category distinctions were more discriminating than lower ones.

Conclusions:

  • Collapsing the lowest two response categories enhances the psychometric performance of the HEM.
  • The nominal response model is valuable for analyzing category functioning and informing scale revisions.
  • Improving measurement precision at lower levels of the construct requires careful consideration of response scale design.