Introduction
A categorical variable consists of a set of non-overlapping categories. Categorical data are counts for those categories. The measurement scale is ordinal if the categories exhibit a natural ordering, such as opinion variables with categories from “strongly disagree” to “strongly agree.” The measurement scale is nominal if there is no ordering. The types of possible analysis depend on the measurement scale.
When the subjects measured are cross-classified on two or more categorical variables, the table of counts for the various combinations of categories is a contingency table. The information in a contingency table can be summarized and further analyzed through appropriate measures of association and models. A standard reference on association measures is Goodman and Kruskal (1979).
Most studies distinguish between one or more response variables and a set of explanatory variables. When the main focus is on the association and interaction structure among a set of response...
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References and Further Reading
Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, New York
Agresti A (2010) Analysis of ordinal categorical data, 2nd edn. Wiley, New York
Bishop YMM, Fienberg SE, Holland PW (1975) Discrete multivariate analysis: theory and practice. MIT Press, Cambridge
Goodman LA, Kruskal WH (1979) Measures of association for cross classifications. Springer, New York
Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York
McCullagh P (1980) Regression models for ordinal data (with discussion). J R Stat Soc B 42:109–142
Nelder J, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc A 135:370–384
Stokes ME, Davis CS, Koch GG (2000) Categorical data analysis using the SAS system, 2nd edn. SAS Institute, Cary
Thompson LA (2008) R (and S-PLUS) manual to accompany Agresti’s Categorical data analysis (2002), 2nd edn. https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf
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Agresti, A., Kateri, M. (2011). Categorical Data Analysis. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_161
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