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Item Response Modeling of Presence-Severity Items: Application to Measurement of Patient-Reported Outcomes created by Ying Liu, Jay Verkuilen

By: Contributor(s): Material type: TextTextSeries: ; Volume , number ,New York : Sage; 2013Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): Online resources: Summary: The Presence-Severity (P-S) format refers to a compound item structure in which a question is first asked to check the presence of the particular event in question. If the respondent provides an affirmative answer, a follow-up is administered, often about the frequency, density, severity, or impact of the event. Despite the popularity of the P-S format in areas such as patient reported outcomes, little attention has been paid to their psychometric analysis, which is necessary for making key design decisions about a scale. In this study, an item response theory–based framework is proposed to perform item analysis involving P-S data, which improves psychometric analysis for (a) scoring response categories, (b) calibrating items, (c) calculating reliability or internal consistency, and (d) selecting and revising items. A real-data example involving the Memorial Symptom Assessment Scale–Short Form, which is used as symptom distress measure for terminally ill cancer patients, demonstrates how the new framework can be used to address various psychometric issues in practice.
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The Presence-Severity (P-S) format refers to a compound item structure in which a question is first asked to check the presence of the particular event in question. If the respondent provides an affirmative answer, a follow-up is administered, often about the frequency, density, severity, or impact of the event. Despite the popularity of the P-S format in areas such as patient reported outcomes, little attention has been paid to their psychometric analysis, which is necessary for making key design decisions about a scale. In this study, an item response theory–based framework is proposed to perform item analysis involving P-S data, which improves psychometric analysis for (a) scoring response categories, (b) calibrating items, (c) calculating reliability or internal consistency, and (d) selecting and revising items. A real-data example involving the Memorial Symptom Assessment Scale–Short Form, which is used as symptom distress measure for terminally ill cancer patients, demonstrates how the new framework can be used to address various psychometric issues in practice.

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