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Abstract:
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This half-day short course presents an overview of models for categorical response variables that have a natural ordering of the categories. Such data often occur in the social sciences (e.g., for measuring attitudes and opinions) and in medical and public health disciplines (e.g., pain, quality of life, severity of a condition). Topics to be covered include logistic regression models using cumulative logits with proportional odds structure, nonproportional odds and partial proportional odds models, other ordinal logistic regression models such as using adjacent-categories logits, and other multinomial response models such as the cumulative probit. Examples presented include social survey data and randomized clinical trials and use R software, with emphasis on the VGAM package. The examples are taken from Foundations of Linear and Generalized Linear Models (2015) and Analysis of Ordinal Categorical Data (2010), both by Alan Agresti.
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