Variables can be classified in terms of whether they are discrete or continuous in nature. Based on the level or scale of measurement the variables are classified as nominal, ordinal, interval and ratio scales.
Discrete variables usually consist of whole number units or categories. They are made up of chunks or units that are detached and distinct from one another. A change in value occurs a whole unit at a time, and decimals do not make sense with discrete scales.
Most nominal and ordinal data are discrete. For example, gender, designation, and Location are discrete scales.
Some interval or ratio data can be discrete. For example, the number of Covid infections reported as a whole number (discrete data), yet it is also ratio data (you can have a true zero and form ratios).
Continuous variables usually fall along a continuum and allow for fractional amounts. The term continuous means that it “continues” between the whole-number units.
Examples of continuous variables are Age (22.7 years), Height (64.5 inches), and Weight (113.25 pounds). Most interval and ratio data are continuous in nature.
Discrete and continuous data are more important in research design and data presentation.