A study of alternative approaches to non-normal latent trait distributions in item response theory models used for health outcome measurement

Niels Smits*, Oğuzhan Öğreden, Mauricio Garnier-Villarreal, Caroline B. Terwee, R. Philip Chalmers

*Corresponding author for this work

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It is often unrealistic to assume normally distributed latent traits in the measurement of health outcomes. If normality is violated, the item response theory (IRT) models that are used to calibrate questionnaires may yield parameter estimates that are biased. Recently, IRT models were developed for dealing with specific deviations from normality, such as zero-inflation (“excess zeros”) and skewness. However, these models have not yet been evaluated under conditions representative of item bank development for health outcomes, characterized by a large number of polytomous items. A simulation study was performed to compare the bias in parameter estimates of the graded response model (GRM), polytomous extensions of the zero-inflated mixture IRT (ZIM-GRM), and Davidian Curve IRT (DC-GRM). In the case of zero-inflation, the GRM showed high bias overestimating discrimination parameters and yielding estimates of threshold parameters that were too high and too close to one another, while ZIM-GRM showed no bias. In the case of skewness, the GRM and DC-GRM showed little bias with the GRM showing slightly better results. Consequences for the development of health outcome measures are discussed.

Original languageEnglish
Pages (from-to)1030-1048
Number of pages19
JournalStatistical Methods in Medical Research
Issue number4
Publication statusPublished - 1 Apr 2020

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