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Categorical Data Analysis I

  • Charles DiMaggio
Chapter

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Charles DiMaggio
    • 1
  1. 1.Departments of Anesthesiology and Epidemiology College of Physicians and Surgeons Mailman School of Public HealthColumbia UniversityNew YorkUSA

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