Distinguishing between the different types of variables is a basic and integral part of applied statistics. The methods to analyze these data are very different and therefore it is important to make the distinction. The two types of variables are Qualitative and Quantitative.
- Qualitative (Categorical)
- Data that serves the function of a name only. Categorical values may be:
- Binary – where there are two choices, e.g. Male and Female
- Ordinal – where the names imply levels with hierarchy or order of preference, e.g. level of education
- Nominal – where no hierarchy is implied, e.g. political party affiliation.
For example, for coding purposes, you may assign Male as 0, Female as 1. The numbers 0 and 1 stand only for the two categories and there is no order between them.
- Data that takes on numerical values that has a measure of distance between them. Quantitative values can be:
- Discrete - or “counted” as in the number of people in attendance
- Continuous - or “measured” as in the weight or height of a person.
Additional examples of both include:
- Number of females in this class (Quantitative, Discrete)
- Nationality (Categorical, nominal)
- Amount of milk in a 1-gallon container (Quantitative, Continuous)
- Sex of students (even if coded as M = 0, F = 1) (Categorical, Binary)