**Lecture 2: Data Types **

There are four data types nominal, ordinal, interval, and ratio.

**Nominal:** Nominal data can be counted and used to calculate the percentage but you can’t take the average of nominal data.

e.g. you walk through each of the sections of a Grocery store, grabbing canned goods, fresh fruit and veg, dairy, frozen foods. If you were to make a list of what section of the store each item came from, the data would come into the nominal data type. Nominal data can also be referred to as category.

**Ordinal:** is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known

Like nominal data, you can count ordinal data and use them to calculate percents, but there is some disagreement about whether you can average ordinal data. On the one hand, you can’t average named categories like “strongly agree” and even if you assign numeric values, they don’t have a true mathematical meaning. Each numeric value represents a particular category, rather than a count of something. E.g.

Castello, M. (2014). Ordinal data example. Retrieved from https://infoactive.co/data-design/ch01.html

**Interval data:** Interval data is numeric and you can do mathematical operations on it, but it doesn’t have a “meaningful” zero point – that is, the value of zero doesn’t indicate the absence of the thing you’re measuring.

Interval data examples that you encounter in everyday life are time, calendar years and temperature. A value of zero for years doesn’t mean that time didn’t exist before that, and a temperature of zero (when measured in C or F) doesn’t mean there’s no heat.

**Ratio Data:** Ratio data is numeric and a lot like interval data, except it does have a meaningful zero point. In ratio data, a value of zero indicates an absence of whatever you’re measuring—zero minutes, zero people in line, zero dairy products in your basket.

Data can also be described as Qualitative or Quantitative.

**Qualitative:** refers to non-numeric data

**Quantitative:** data is numerical

**CLASS EXERCISE: Quality of Quantity **