Coefficient of Variation

Abstract

Categorical data consists of counts of observations falling into specified classes. We can distinguish between various types of categorical data: • Binary, characterizing the presence or absence of a property; • Unordered multicategorical (also called "nominal"); • Ordered multicategorical (also called "ordinal"); • Whole numbers. We represent the categorical data in the form of a contingency table. DOMAINS AND LIMITATIONS Variables that are essentially continuous can also be presented as categorical variables. One example is "age", which is a continuous variable, but ages can still be grouped into classes so it can still be presented as categorical data. EXAMPLES In a public opinion survey for approving or disapproving a new law, the votes cast can be either "yes" or "no". We can represent the results in the form of a contingency table: Yes No Votes 8546 5455 If we divide up the employees of a business into professions (and at least three professions are presented), the data we obtain is unordered multicategorical data (there is no natural ordering of the professions). In contrast, if we are interested in the number of people that have achieved various levels of education, there will probably be a natural ordering of the categories: "primary, secondary" and then university. Such data would therefore be an example of ordered multicategorical data. Finally, if we group employees into categories based on the size of each employee's family (that is, the number of family members), we obtain categorical data where the categories are whole numbers. FURTHER READING Analysis of categorical data Binary data Category Data Dichotomous variable Qualitative categorical variable Random variable REFERENCES See analysis of categorical data.