Biostatistics short course: Modeling Ordinal Categorical Data
Taught by Alan Agresti, PhD, University of Florida, this intermediate level course presents models for analyzing categorical response variables that have a natural ordering of categories with a main focus on logistic regression models using cumulative logits, with and without proportional odds structure. Examples use R software, with some examples also showing SAS output. This course will take place via Zoom.
This short course present models for analyzing 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). The main focus is on logistic regression models using cumulative logits, with and without proportional odds structure. Examples use R software, with some examples also showing SAS output. The presentation emphasizes interpretation of the methods rather than technical details, with examples including randomized clinical trials and social survey data. The lectures will take material from the books written by Alan Agresti, “Analysis of Ordinal Categorical Data” (2nd ed., Wiley, 2010) and “An Introduction to Categorical Data Analysis” (3rd ed., Wiley, 2019).
This is an intermediate level course. You will need to be familiar with basic categorical data methods, such as chi-squared tests and binary logistic regression. Professor Agresti will use R mainly to have output to illustrate the methods, but it will not be a problem if you are not very familiar with it.
Alan Agresti, PhD
Alan Agresti is distinguished professor emeritus at the University of Florida, where he was a member of the statistics department from 1972 to 2010. He is author or co-author of more than 100 articles and seven books, including “Analysis of Ordinal Categorical Data” (2nd ed. 2010), “An Introduction to Categorical Data Analysis” (3rd ed. 2019), “Foundations of Linear and Generalized Linear Models” (2015), and “Statistics: The Art and Science of Learning from Data” (5th ed. 2021). A fellow of the American Statistical Association and of the Institute of Mathematical Statistics, Agresti has received an honorary doctorate from De Montfort University in the U.K., the Statistician of the Year award from the Chicago chapter of the American Statistical Association, and the first Herman Callaert Leadership Award in Biostatistical Education and Dissemination from Hasselt University, Belgium. In fall terms 2008-2014, he was a visiting professor at the statistics department of Harvard University.