Numerical data are gathered by businesses every day that represent a wide variety of items and transactions. For example, numbers in business can represent monetary costs, regional sales, shipment weights, and sales percentages of merchandise.
Data levels need to be analyzed statistically in different ways because the items and transactions represented by the data can be unique from one another. For this reason, business analysts need to know what the different levels of data measurement are that are represented by the numbers being analyzed.
Four Common Levels of Data
There are four common levels of data that businesses commonly collect and analyze. They are nominal level, ordinal level, interval level, and ratio level.
The lowest level of data measurement is the nominal level. Nominal data form qualitative data sets that group variables into categories. Numbered employee identification cards are an example of nominal data. Here nominal data are categorized according to labels (employees) which are purely descriptive in nature and don’t provide any quantitative or numeric value statistically speaking.
Ordinal-level data measurement is higher than the nominal-level data. In addition to the nominal-level capabilities, ordinal-level measurement can be used to rank and order objects. Using ordinal data a manager could rank three employees, with numbers from 1 to 3. The manager could assign the number 1 to the most helpful employee and a 3 to the least helpful employee.
Interval-level data measures the distance between consecutive numbers. An example of interval measurement is the temperature in Celsius degrees. In Celsius degrees, the numbers rank the temperatures and the amount of heat between consecutive readings such as 20 degrees Celsius, 21 degrees Celsius, and 22 degrees Celsius are the same.
Ratio-level data measurement is the highest level of data measurement. Ratio data have the same properties as interval data, but ratio data have an absolute zero, and the ratio of two numbers is meaningful. Examples of ratio data could be height, weight, and volume.
Elaine Allan, BA, MBA
Technology & Business Blogger
Vancouver, BC, Canada