# Statistics/Different Types of Data/Quantitative and Qualitative Data

## Qualitative data[edit | edit source]

Qualitative data is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description. In statistics, it is often used interchangeably with "categorical" data.

For example: favorite color = "blue" height = "tall" i hated the most = "zen"

Although we may have categories, the categories may have a structure to them. When there is not a natural ordering of the categories, we call these **nominal** categories. Examples might be gender, race, religion, or sport.

When the categories may be ordered, these are called **ordinal** variables. **Categorical variables** that judge size (small, medium, large, etc.) are ordinal variables. Attitudes (strongly disagree, disagree, neutral, agree, strongly agree) are also ordinal variables, however we may not know which value is the best or worst of these issues. Note that the distance between these categories is not something we can measure.

## Quantitative data[edit | edit source]

Quantitative data is a numerical measurement expressed not by means of a natural language description, but rather in terms of numbers. However, not all numbers are continuous and measurable. For example, the social security number is a number, but not something that one can add or subtract.

For example: molecule length = "450 nm" height = "1.8 m"

Quantitative data always are associated with a scale measure.

Probably the most common scale type is the ratio-scale. Observations of this type are on a scale that has a meaningful zero value but also have an equidistant measure (i.e., the difference between 10 and 20 is the same as the difference between 100 and 110). For example, a 10 year-old girl is twice as old as a 5 year-old girl. Since you can measure zero years, time is a ratio-scale variable. Money is another common ratio-scale quantitative measure. Observations that you count are usually ratio-scale (e.g., number of widgets).