# Qualitative Data

**The Four Horsemen of Measurement** - hear their names: Nominal, Ordinal, Interval, Ratio.
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As mentioned before there are quantitative and qualitative data.

When you do not have numbers, you have got **qualitative data**. This data is non-numerical or also called categorical or discrete data.

## Qualitative Data (“categorical”)

Non-numerical data that is usually textual and descriptive, like “mostly satisfied”, “brown eyes”, “female”, “yes/no”, etc.

### Nominal Scale Data (named)

Nominal scales are used for labeling variables, without any quantitative value. Notice that all of these scales are mutually exclusive (no overlap) and none of them have any numerical significance. A good way to remember all of this is that “nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels. When there are only two categories (e.g. yes/no True/False. Presence/Absence), it is called a dichotomous variable. You can use frequency counts, mode, and contingency tables to describe and analyze the data. Measures of central tendency, variance, or correlation are not meaningful for nominal data. Bar charts, pie charts, and stacked bar charts are useful for visualizing nominal data. They display the frequency or proportion of each category.

### Ordinal Scale Data (orderd)

With ordinal scales, the order of the values is what is important and significant, but the differences between each one is not known. For example, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is. Another example is the difference between “OK” and “Unhappy”. Is it the same as the difference between “Very Happy” and “Happy?”

Ordinal scales are typically measures of non-numeric concepts like satisfaction, happiness, discomfort, etc.

For ordinal data, you can use median, mode, and non-parametric tests like the Mann-Whitney U-test. You can order the categories but can’t perform arithmetic operations on them. Bar charts, stacked bar charts, and ordered categorical plots are suitable for displaying ordinal data, emphasizing the order or ranking.