Categorical Data Analysis I

Charles DiMaggio
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Charles DiMaggio: Columbia University, Departments of Anesthesiology and Epidemiology College of Physicians and Surgeons Mailman School of Public Health

Chapter Chapter 8 in SAS for Epidemiologists, 2013, pp 99-118 from Springer

Abstract: Abstract In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous. We will encounter them again as potential explanatory or exposure variables when we discuss ANOVA and dummy variables in linear regression. We now, though, consider them exclusively as both exposures and outcomes. When both our exposure and outcome are dichotomous categorical variables we can apply the classic epidemiological 2 ×2 table.

Keywords: Proc Format; Column Total; Cross Tabulation; Categorical Data Analysis; Proc Freq (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-1-4614-4854-9_8

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