## Levels of measurement can be split into two groups: qualitative and quantitative data.

They are very intuitive, so don’t worry. Qualitative data can be nominal or ordinal. Nominal variables are like the categories we talked about just now – Mercedes, BMW or Audi, or like the four seasons – winter, spring, summer and autumn. They aren’t numbers and cannot be put in any order.

Ordinal data, on the other hand, consists of groups and categories but follows a strict order. Imagine you have been asked to rate your lunch and the options are: disgusting, unappetizing, neutral, tasty, and delicious. Although we have words and not numbers, it is obvious that these preferences are ordered from negative to positive, thus the data is qualitative, ordinal. Okay, so what about quantitative variables? Well, as you may have guessed by now, they are also split into two groups: interval and ratio. Intervals and ratios are both represented by numbers but have one major difference. Ratios have a true zero and intervals don’t. For example, length is a ratio variable. You all know that 0 inches or 0 feet means that there is no length.

With temperature, however, we have a different story. It is usually an interval variable. Let me explain. Usually, it is expressed in Celsius or Fahrenheit. They are both interval variables. 0 degrees Celsius or 0 degrees Fahrenheit don’t not mean anything, as the absolute zero temperature is actually -273.15 degrees Celsius, or -459.67 degrees Fahrenheit. However, we can easily say that 80 degrees Fahrenheit is less than 100 degrees Fahrenheit. In the case of interval variables, the difference is meaningful, but the 0 is not. Continuing this temperature example, there is another scale – Kelvin’s. According to it, the absolute minimum temperature is 0 degrees Kelvin. Therefore, if the degrees are stated in Kelvin’s the variable will be a ratio. So. Numbers like 2, 3, 10, 10.5, Pi, etc. can be both interval or ratio, but you have to be careful with the context you are operating in.

Alright! We’ve quickly gone through the types of data and the measurement levels.