In the sphere of statistics, the comprehension of levels of measurement is essential for conducting proper data analysis and assessment. These levels are nominal level, ordinal level, interval level, and ratio level. Depending on their type and specific characteristics, they classify different values of data. It is influential for researchers to determine the appropriate level of measurement for their study to employ relevant analytical methods to draw valid conclusions.
Definition: Levels of measurement
The definition of levels of measurement – also sometimes called the scale of measure – is an approach to statistics that helps to organize data sets according to the sort of variables that are contained within them. Identifying the level means being able to apply the most appropriate statistical techniques accordingly, ensuring accurate conclusions and results.
As mentioned, there are usually four levels of measurement according to the common Stevens definition but other approaches exist, notably a system developed by Frederick Mosteller and John Tukey.
Types of measurement levels
To better understand why scientific researchers use levels of measurement, it is helpful to examine the four most common levels in turn.
Levels of measurement: Descriptive statistics
Descriptive statistics apply in levels of measurement. Their purpose is to assist when a measurement of variable spread or central tendency is being sought.
In terms of levels of measurement, descriptive statistics means being able to obtain a swift idea of where the middle of the data set might be. This is the aforementioned central tendency to give it its mathematical term. Equally, the spread of variables in a given data set can be more easily determined when levels of measurement are applied.
In this sense, spread really means variability – how low or how high a variable might be given the set of data under examination.
The importance of the four levels of measurement
The importance of levels of measurement as a classification system for variables in data sets comes down to how different sets of data are comparable with one another.
Note: Not all statistical methods can be used with all variables.
For example, because a ratio level of measurement contains a true zero, it is possible to say whether one variable is three or four times the quantity of another.
Overall, the four levels of measurement in common use offer a chance to introduce mathematical operations in the correct way, thereby providing insights into more advanced statistical operations.
|Level of measurement||Mathematical operand likely to be used||Statistical operation|
|Nominal||or||Data aggregation, grouping|
|Ordinal||≤ or ≥||Sorting, categorizing|
|Interval||+ or -||Measurement, comparison to a standard|
|Ratio||× or ÷||Measurement, comparison to a standard|
The four levels of measurement in order are:
Although these are the four widely adopted levels of measurement, some academics advocate for and use other classification systems.
This is a system for judging the interval type of measurement through pairwise comparisons developed by LL Thurstone. It is usually used in analytic hierarchy processes.
We use levels of measurement to decide the best way to interpret data.
Selecting the most appropriate level helps to produce an analysis that can be peer-reviewed and understood more easily within a pre-existing academic framework.
Levels of measurement that can be classified as nominal in the field of sociology include how people might choose to identify or characterize themselves.
Examples of nominal levels of measurement in sociology include:
- social class
- racial identity