Data Science & Statistics: Levels of measurement


Levels of measurement – these 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.

4 Comments

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