Smits N, Öğreden O, Garnier-Villarreal M, Terwee CB, Chalmers RP. A study of alternative approaches to non-normal latent trait distributions in item response theory models used for health outcome measurement.
Stat Methods Med Res 2020;
29:1030-1048. [PMID:
32156195 PMCID:
PMC7221458 DOI:
10.1177/0962280220907625]
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Abstract
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.
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