A brief introduction on latent variable based ordinal regression models with an application to survey data

TL;DR

This paper reviews latent variable ordinal regression models, highlighting their advantages over linear models for survey data analysis, and demonstrates their application with real data and software tools.

Contribution

It provides a concise overview of latent variable ordinal models, including practical application, software guidance, and discussion of strengths and limitations.

Findings

Ordinal models offer probability estimates for all response categories.

Linear regression can be misleading for ordinal survey data.

Software tools facilitate the application of ordinal regression models.

Abstract

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.