Course on Introduction to Categorical Data Analysis at the RSS in London

Author: Irena Ograjenšek, Created 2013/06/21 09:35, Last updated: 2013/06/21 09:36

The course entitled Introduction to Categorical Data Analysis will take place at the RSS in London on August 20th and 21st. It will be presented by Alan Agresti and Maria Kateri. 

Alan Agresti is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has written six books, including “Categorical Data Analysis,” which has received more than 10,000 citations in journal articles. He has presented lectures and short courses on categorical data methods in more than 30 countries. 

Maria Kateri is Professor of Statistics and Stochastic Modelling in the Institute of Statistics at the RWTH University of Germany.  Previously, she was Associate Professor of Statistics at the Universities of Piraeus and Ioannina, Greece. Her main research area comprises methods of categorical data analysis, with special emphasis on ordinal data. She has presented courses and seminars on categorical data methods for more than 10 years. Her research interests also include biostatistics, Bayesian analysis, and reliability.

The main objective of the course is to introduce attendees to the most important methods for categorical data analysis, and in particular to show them that there’s a lot more that one can do than Pearson’s chi-squared test. Categorical data are common in practice, and when there are more than two categories, different methods apply for ordered than for unordered categories; it is useful for a methodologist to know the various options for analysing such data as well as the pros and cons of those approaches. The main emphasis is on introducing various models (emphasizing logistic regression) and their interpretations. Through examples, attendees will learn how to use the models and weigh the advantages and disadvantages of the various model types.

More information can be found on the website of the Royal Statistical Society. Register for the course before 9th July and receive an early bird discount!

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