What is a Measurement Variable?A measurement variable is a variable that can be measured and given a number, like 4 mm, 3 yards or 42. Measurement variables are sometimes called numeric variables. More formally, measurement variables are called quantitative variables. Which term you use is usually a matter of personal choice although in academic writing (especially in journals) the term quantitative variable is usually preferred.
Measurement variables can be continuous, discrete, interval or ratio variables.
- Discrete variables are countable and finite (i.e. they have an end). An example of a discrete variable is numbers on a birthday card: 1,2, 21, 40, 50, 60 and so on.
- Continuous variables are not countable as they have infinite possibilities. If you did try to count them you would be counting on and on until infinity. Weight is an example of a continuous variable. A Weight can be 20lbs, or 20.1 lbs or 20. 00000001 lbs…and so on.
Note: For more examples of the difference between discrete and continuous variables, see: Discrete vs. Continuous variables.
- Interval variables: a subset of continuous variables where measurements fall on a scale with meaningful intervals (like a thermometer).
- Ratio variables: also a subtype of continuous data, it is a type of interval variable that has a meaningful zero. For example, 0 years old means that you don’t exist.
Other broad types of variables
- Nominal variables: variables than can be placed into categories like male/female, young, adult, senior or freshman/sophomore/junior/senior. If you only have two measurement variables (e.g. under 18, 18 and over), and if you are analyzing your data (e.g. you’re performing a hypothesis test), it may make more sense to treat your measurement variables as nominal variables.
- Ranked variables: variables that are ranked in order like 1st, 2nd, 3rd…
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!