Levels of Measurement

Definition of Levels of Measurement

Levels of measurement refer to the different ways numbers or scores can be classified and understood in research.
There are four recognised levels of measurement: Nominal, Ordinal, Interval, and Ratio. Each level provides different kinds of information and allows for various statistical techniques to be used.

At the nominal level, numbers or scores are used as labels or categories.
There is no specific order to the categories and the only mathematical operation that is applicable is the pure count to determine the frequency.
An example would be assigning ‘1’ to ‘Female’ and ‘2’ to ‘Male’ in a gender categorical variable. No arithmetic operation holds meaning here.

With an ordinal level of measurement, the numbers carry information about the order of the units being measured.
This level establishes a ranked data structure but the intervals between values are not necessarily equal. Median and mode are meaningful measures while mean is usually not.
Examples include ranks in a competition, where we know first place ranked higher than second but the difference between them is not quantifiable.

The interval level introduces equal spacing between values, in addition to maintaining order.
Unlike ordinal levels, it allows the calculation of the mean, as values represent fixed measurement units but there is no true zero point.
For instance, the temperature scale in Celsius or Fahrenheit, where zero does not indicate an absence of temperature.

The ratio level is the highest level of measurement which takes account of equal intervals as well as true zero points.
All statistical operations including mean, median, mode, standard deviation are meaningful and ratios between numbers do make sense.
Examples include measures such as weight or height, and scales like Kelvin where zero signifies an absence of temperature.

Identifying the correct level of measurement is crucial for deciding the type of analytical tools to be used, the kind of graphs to plot, and the interpretation of results.
The use of inappropriate statistical techniques due to wrong level assignment may lead to misleading or incorrect inferences.
By understanding the classification of data according to its level of measurement, researchers can accurately analyse and represent findings of their study.