Ordered Logit Model, Proportional Odds Assumption and Marginal Effects: Demysfying the Derivational steps
DOI:
https://doi.org/10.2022/lje.v2i2.7Keywords:
Ordered model, Derivational Steps, ResearchersAbstract
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.
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