Ordinal regression

Ordinal regression, also known as “ordered logit” or “ordered probit”, is a type of regression analysis used in statistics to predict a set of numeric values in a non-linear sequence. It is commonly used in predictive analytics which helps to determine the probability of an event occurring in a range of values. Ordinal regression is used to predict ranking or ordering of two or more categories in binary or non-binary data sets. It allows for the model to not only consider the data points (observations) but also the order of the observations, thus allowing for more accurate predictions.

Ordinal regression is useful for fields such as biology where the ordering of classifications can provide more insight to the study than just the discrete values. For example, in measuring patient temperaments, the ordinal regression is able to distinguish between “calm” versus “irritated” versus “angry” versus “violent” instead of just assigning each patient to a single categorical scale. In this way, it can capture more levels of detail.

In most cases, ordinal regression employs the same techniques as linear regression, with the main difference being that the output is ordered. Multiple regression models and logistic regression models can be used in conjunction with ordinal regression. To make predictive models more accurate, ordinal regression uses “dummy variables” to represent each of the categories (or ordinal level) and its associated weighting. The weights are used to evaluate probabilities (of occurrence or transition) for each of the categories.

Ordinal regression is used in a broad range of fields to accurately identify, measure, and predict relationships between nominal, ordinal, and categorical datasets. It can be used in market research to assess consumer preferences, criminal justice to predict recidivism, and healthcare to classify diseases or patient health.