Description: Ordinal regression is a type of regression analysis used to predict an ordinal variable, that is, a variable that has a natural order but not necessarily a uniform distance between its categories. Unlike linear regression, which assumes that the dependent variable is continuous and normally distributed, ordinal regression focuses on situations where responses are classified into categories that have an order, such as ‘low’, ‘medium’, and ‘high’. This approach allows modeling the relationship between one or more independent variables and an ordinal dependent variable, using techniques that respect the ordinal nature of the data. Key features of ordinal regression include the ability to handle categorical data, the interpretation of coefficients in terms of cumulative probabilities, and the use of models such as the ordinal logit model or the ordinal probit model. Ordinal regression is particularly relevant in fields such as psychology, sociology, and survey analysis, where responses are often categorical and ordered. Its implementation in machine learning tools and libraries enables researchers and data scientists to develop more accurate and meaningful models for predicting ordinal variables, thereby improving the quality of data-driven decisions.
Uses: Ordinal regression is used in various fields, such as psychology to assess levels of satisfaction or anxiety, in market studies to classify consumer preferences, and in education to analyze student grades. It is also useful in medicine for classifying the severity of diseases or symptoms, and in surveys where responses are categorized on Likert scales.
Examples: A practical example of ordinal regression is the analysis of customer satisfaction surveys, where responses can be ‘very dissatisfied’, ‘dissatisfied’, ‘neutral’, ‘satisfied’, and ‘very satisfied’. Another example is the evaluation of product quality on a scale from 1 to 5, where each number represents a specific level of quality.
