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The analysis of survey data is a frequently arising issue in clinical trials,particularly when capturing quantities which are difficult to measure using,e.g., a technical device or a biochemical procedure. Typical examples arequestionnaires about patient's well-being, pain, anxiety, quality of life orconsent to an intervention. Data is captured on a discrete scale containingonly a limited (usually three to ten) number of possible answers, of which therespondent has to pick the answer which fits best his personal opinion to thequestion. This data is generally located on an ordinal scale as answers canusually be arranged in an increasing order, e.g., "bad", "neutral", "good" forwell-being or "none", "mild", "moderate", "severe" for pain. Since responsesare often stored numerically for data processing purposes, analysis of surveydata using ordinary linear regression (OLR) models seems to be natural.However, OLR assumptions are often not met as linear regression requires aconstant variability of the response variable and can yield predictions out ofthe range of response categories. Moreover, in doing so, one only gainsinsights about the mean response which might, depending on the responsedistribution, not be very representative. In contrast, ordinal regressionmodels are able to provide probability estimates for all response categoriesand thus yield information about the full response scale rather than just themean. Although these methods are well described in the literature, they seem tobe rarely applied to biomedical or survey data. In this paper, we give aconcise overview about fundamentals of ordinal models, applications to a realdata set, outline usage of state-of-the-art-software to do so and point outstrengths, limitations and typical pitfalls. This article is a companion workto a current vignette-based structured interview study in paediatricanaesthesia.

A primer on computational statistics for ordinal models with applications to survey data