Level of measurement (LoM) is an important characteristic of data. The LoM determines what types of descriptive, graphical, and inferential statistical analyses can be used. There are four levels of measurement:
The simplest type of variable is dichotomous (or binary, e.g., 0 = male/ 1 = female; 0 = black/ 1= white; 0 = yes/ 1 = no).
Categorical or nominal variables simply provide numerical labels (or names) for two or more categories e.g., 0 = red/ 1 = blue/ 2 = green / 3 = yellow; 0 = car; 1 = bus; 2 = bicycle; 3 = aeroplane; 4 = train.
Ordinal
When categorical variables can be meaningful ordered, they become ordinal variables
The distance between the ordered categories may vary
e.g., 1 = 1st, 2 = 2nd, 3 = 3rd in a race; verbal frequency scale (0 = never, 1 = sometimes, 2 = often, 3 = always)
Interval
Ordered categories (discrete values) which have equal distances (e.g., Strongly Disagree - Disagree - Neither Agree or Disagree - Agree - Strongly Agree)
Allows use of parametrics statistics (which assume a normal distribution)
Ratio
Continuous (not discrete) - values can take on (in theory) infinite decimal points
Has a meaningful 0 (e.g., the 0 point isn't arbitrary), which allows ratio comparisons (e.g,. according to the sample of participants, males are, on average, 20% taller than females).
