An interval measure is one where the distance between the attributes, or response options, has an actual meaning and is of an equal interval. Differences in the values represent differences in the attribute. For example, the difference between 3 and 4 is the same as the difference between 234 and 235.

Examples of interval level data include temperature and year. Examples of ratio level data include distance and area (e.g., acreage). The scales are similar in so far as units of measurement are arbitrary (Celsius versus Fahrenheit, Gregorian versus Islamic calendar, English versus metric units).

Age is also a variable that can be measured on an interval scale. For example if A is 15 years old and B is 20 years old, it not only clear than B is older than A, but B is elder to A by 5 years.

When you ask someone to select a meal from a menu, you’re using a nominal scale. When you ask someone to rate their meal on a scale of one to ten, you’re using an interval scale.

An organization can calculate its interval measure by dividing the average daily operating expenses by current assets minus inventory. The result is the number of days the company can continue to use its assets to meet its expenses.

Age is considered a ratio variable because it has a “true zero” value. It’s possible for an individual to be zero years old (a newborn) and we can say that the difference between 0 years and 10 years is the same as the difference between 10 years and 20 years.

Then he realized shoe size is an interval variable. Eureka! An interval variable has a defined interval between values but lacks a zero point. Consider shoe sizes, we can say that the difference in shoe size 8 and shoe size 7 is equal to the difference in sizes 2 and 3.

One question students often have is: Is “time” considered an interval or ratio variable? The short answer: Time is considered an interval variable because differences between all time points are equal but there is no “true zero” value for time.

Physical characteristics of persons and objects can be measured with ratio scales, and, thus, height and weight are examples of ratio measurement. A score of 0 means there is complete absence of height or weight.

In this case, salary is not a Nominal variable; it is a ratio level variable.

For example, gender is a nominal variable that can take responses male/female, which are the categories the nominal variable is divided into. A nominal variable is qualitative, which means numbers are used here only to categorize or identify objects.

Most physical measures, such as height, weight, systolic blood pressure, distance etc., are interval or ratio scales, so they fall into the general “continuous ” category.

There are 4 levels of measurement, which can be ranked from low to high:

Nominal: the data can only be categorized.Ordinal: the data can be categorized and ranked.Interval: the data can be categorized and ranked, and evenly spaced.Ratio: the data can be categorized, ranked, evenly spaced and has a natural zero.

For example, temperature in Celsius or Fahrenheit is at an interval scale because zero is not the lowest possible temperature. In the Kelvin scale, a ratio scale, zero represents a total lack of thermal energy.

An interval is a range of numbers between two given numbers and includes all of the real numbers between those two numbers. As you may recall, real numbers are pretty much any number you can think of: 3.56, 171, √5, -0.157, π, etc.

A Nominal Scale is a measurement scale, in which numbers serve as “tags” or “labels” only, to identify or classify an object. This measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value. Below is an example of Nominal level of measurement.

Ratio measure refers to the highest (most complex) level of measurement that a variable can possess.