Ordered Logit Model, Proportional Odds Assumption and Marginal Effects: Demysfying the Derivational steps

Abstract

Ordered models, such as the Ordered Logistic regression model, are used when the dependent variable of a model is categorical and  ordinal. However, the literatures on Ordered models often skip the derivational steps of the model, which may make researchers apply  the model as a dogma without knowing how the output of the model is expected to be. This study provides a detailed breakdown of the  derivational steps of the model; the Proportional Odds assumption; the marginal effects and some practical examples, with a view to  helping researchers have a better understanding of the output of the model when used in their studies.