  The analysis of survey data is a frequently arising issue in clinical trials,
particularly when capturing quantities which are difficult to measure. Typical
examples are questionnaires about patient's well-being, pain, or consent to an
intervention. In these, data is captured on a discrete scale containing only a
limited number of possible answers, from which the respondent has to pick the
answer which fits best his/her personal opinion. This data is generally located
on an ordinal scale as answers can usually be arranged in an ascending order,
e.g., "bad", "neutral", "good" for well-being. Since responses are usually
stored numerically for data processing purposes, analysis of survey data using
ordinary linear regression models are commonly applied. However, assumptions of
these models are often not met as linear regression requires a constant
variability of the response variable and can yield predictions out of the range
of response categories. By using linear models, one only gains insights about
the mean response which may affect representativeness. In contrast, ordinal
regression models can provide probability estimates for all response categories
and yield information about the full response scale beyond the mean. In this
work, we provide a concise overview of the fundamentals of latent variable
based ordinal models, applications to a real data set, and outline the use of
state-of-the-art-software for this purpose. Moreover, we discuss strengths,
limitations and typical pitfalls. This is a companion work to a current
vignette-based structured interview study in paediatric anaesthesia.
