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Abstract:
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This short course surveys the most common methods for analyzing categorical data. The first part of the course focuses on contingency table analysis, logistic regression for binary data, logistic model building, and loglinear models. The second part introduces logistic models for multi-category ordinal and nominal responses and for clustered data using generalized estimating equations (GEE) and random effects. The presentation emphasizes interpretation rather than technical details, with examples including social surveys and randomized clinical trials. Examples show the use of R, with SAS and Stata code also given for some examples.
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