Modeling Uncertainty in Ordinal Regression: The Uncertainty Rating Scale Model

Gerhard Tutz ()
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Gerhard Tutz: Ludwig-Maximilians-Universität München, Akademiestraße 1, 80799 München, Germany

Stats, 2025, vol. 8, issue 2, 1-13

Abstract: In questionnaires, respondents sometimes feel uncertain about which category to choose and may respond randomly. Including uncertainty in the modeling of response behavior aims to obtain more accurate estimates of the impact of explanatory variables on actual preferences and to avoid bias. Additionally, variables that have an impact on uncertainty can be identified. A model is proposed that explicitly considers this uncertainty but also allows stronger certainty, depending on covariates. The developed uncertainty rating scale model is an extended version of the adjacent category model. It differs from finite mixture models, an approach that has gained popularity in recent years for modeling uncertainty. The properties of the model are investigated and compared to finite mixture models and other ordinal response models using illustrative datasets.

Keywords: ordinal regression; adjacent category model; finite mixture models; uncertainty modeling (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:8:y:2025:i:2:p:42-:d:1663100

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