Data can be divided into two broad categories:

Qualitative

Qualitative data are categorical rather than numeric. However, be careful because it is fairly common practice to code data using numbers to represent categories, for example 1 = male and 2 = female. **Nominal** data are categories with no meaningful order, such as individual students on a class roster. **O****rdinal** data do have a meaningful order, such as students' level of satisfaction with their instructor: *Very Unsatisfied*, *Unsatisfied*, *Neutral*, *Satisfied*, or *Very Satisfied*. With both types of qualitative data, the degree of difference between data points cannot be determined. In other words, we have no way of actually measuring the difference between *Satisfied* and *Very Satisfied*.

Quantitative

Quantitative data are numeric and measured on a continuous scale. **Interval** data have an arbitrary zero value and the degree of difference between data points can be measured. For example, a temperature of 0°C is not the absence of temperature, its just cold, and the difference between 0°C and 10°C can be calculated as 10 units. **Ratio** data do have a meaningful zero value and the difference between two data points can be calculated as a ratio. For example, if there are 0 dollars in a bank account, there is an absence of money, and if Person A has 5 dollars and Person B has 10 dollars, we can accurately say that Person B has twice as much money as Person A.

The type of data will determine the best method for visual enconding, see Best Practices.