Level of measurement
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Educational level: this is a tertiary (university) resource. 
Completion status: this resource is ~50% complete. 
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:
Levels of measurement

Categorical/nominal[edit]
 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[edit]
 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[edit]
 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[edit]
 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).