This book is a step-by-step data story for analysing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with data sets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting (growth) stage (of a plant or animal) based upon a multitude of factors. This is true in environmental sciences (for example: stage of a storm), chemical sciences (for example: type of reaction), and physical sciences (for example: stage of damage when force is applied), medical sciences (for example: degree of pain) and social sciences (for example: demographic factors like social status categorized in brackets) as well. There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctance and ret
