Interval scale offers labels, order, as well as, a specific interval between each of its variable options. Ordinal scale has all its variables in a specific order, beyond just naming them. Nominal scale is a naming scale, where variables are simply “named” or labeled, with no specific order. Before we discuss all four levels of measurement scales in details, with examples, let’s have a quick brief look at what these scales represent. Nominal, Ordinal, Interval, and Ratio are defined as the four fundamental levels of measurement scales that are used to capture data in the form of surveys and questionnaires, each being a multiple choice question.Įach scale is an incremental level of measurement, meaning, each scale fulfills the function of the previous scale, and all survey question scales such as Likert, Semantic Differential, Dichotomous, etc, are the derivation of this these 4 fundamental levels of variable measurement. What are Nominal, Ordinal, Interval and Ratio Scales? The mathematical nature of a variable or in other words, how a variable is measured is considered as the level of measurement. The level of measurement of a variable decides the statistical test type to be used. Using statistical tests, you can conclude the average hourly rate of a larger population. Then the average hourly rate of this sample audience is calculated. So, a sample audience is randomly selected such it represents the larger population appropriately. The variables for this set of the population can be industry, location, gender, age, skills, job-type, etc The value of the variables will differ with each employee.įor example, it is practically impossible to calculate the average hourly rate of a worker in the US. For instance, consider a sample of employed individuals. A quantity whose value changes across the population and can be measured is called variable.
There are different levels of measurement in statistics and data measured using them can be broadly classified into qualitative and quantitative data.įirst, let’s understand what a variable is.
To perform statistical analysis of data, it is important to first understand variables and what should be measured using these variables.