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There are four levels of measurement:
Levels of Measurement
Nominal Ordinal Interval Ratio
Nominal
In nominal measurement, the numerical values represent a unique “name” of the attribute. The cases may be ordered in
any manner. For example, jersey numbers in cricket are measures at the nominal level. A player with the number 20 is not
more of anything than a player with the number 3 and is certainly not twice whatever number 10 represents.
Ordinal
In ordinal measurement, attributes can be ordered. The distances or intervals between attributes are irrelevant
here. For example, in a survey, you can code educational qualification as 0 = secondary; 1 = senior secondary; 2
= graduation; 3 = post-graduation; 4 = PhD. In this level of measurement, higher numbers mean more education.
However, is the distance from 0 to 1 equal to 3 to 4? Of course, no. The interval between the values cannot be
interpreted as an ordinal measure.
Interval
While measuring intervals, the distance between attributes is important. For example, if we measure temperature
(in Fahrenheit), the distance between 30 – 40 is equal to the distance between 70 – 80. The interval between the
values is interpretable. Interval level data can be used in calculations, but any comparisons cannot be done. 80 ° C is
not four times hotter than 20 ° C (and 80 ° F is not four times hotter than 20 ° F). The ratio of 80:20 (or four to one)
doesn't matter.
Ratio
When measuring ratio, there is always an absolute zero point that makes sense. This means that you can use a ratio
variable to construct a significant fraction (or ratio). Weight is a variable of proportion. In applied social research, most
“number” variables are ratios, for example, the number of clients for a product. You can have zero customers and it
makes sense to say, "... we had twice as many customers last year compared to what we have this year."
04 – RATIO
(named + ordered + proportional interval between variable + can have absolute zero values)
03 – INTERVAL
(named + ordered + proportional interval between variables
02 – ORDINAL
(named + ordered variables)
01 – NOMINAL
(named variables)
Data Analysis (Computational Thinking) 253

