Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression

Steven P. Reise (), Han Du, Emily F. Wong, Anne S. Hubbard and Mark G. Haviland
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Steven P. Reise: University of California, Los Angeles
Han Du: University of California, Los Angeles
Emily F. Wong: University of California, Los Angeles
Anne S. Hubbard: University of California, Los Angeles
Mark G. Haviland: Loma Linda University

Psychometrika, 2021, vol. 86, issue 3, No 11, 800-824

Abstract: Abstract Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is “a better fit” or more “valid” than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Keywords: graded response model; log-logistic model; IRT model assumptions (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11336-021-09802-0

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