Ratio Data: Definition, Examples, and Analysis

There are a total of four types of data in statistics primarily. They are nominal data, ordinal data, ratio data, and interval data.

Ordinal Data: This type of data is classified into categories. However, the distance between these categories is unknown.

Nominal Data: Nominal data is used to label variables without assigning any quantitative value to them.

Ratio Data: This is a kind of qualitative data that measures variables on a continuous scale.

Interval Data: This data type is measured along a scale and has an equal distance between its values.

If you’re looking to learn everything there is to know about the ratio data, then you’re at the right place. Let’s take a look into the definition, examples, and analysis of ratio data.

What is a Ratio Data?

A Ratio scale is the fourth type of measurement scale and is quantitative in nature. It is similar to interval data, where each value is placed at an equal distance from its subsequent value. However, it has a ‘true zero,’ which means that zero possesses a meaning.

The ratio scale contains the characteristics of nominal, ordinal, and interval scales and is, therefore, used widely in market research.

Examples of Ratio Data

Here are some of the common examples of ratio data:

• Age
• Mass and Weight
• Distance

In Market Research, it is used to evaluate:

• Sales
• Prices
• Number of customers

Ratio Data vs. Interval Data – The Difference

Both interval and ratio data have equal values placed between two variables. However, one significant difference between the two is the presence of the ‘true zero.’ The ratio data has a true zero, which denotes an absence of a variable. For example, in interval data, you can measure temperature beyond 0 degrees because zero, in this case, holds a value. However, in ratio data, the variables never fall beneath zero.

An example of this is body mass. We know mass is measured from 0 as a starting point and goes above. It does not fall beneath zero.

Characteristics of Ratio Data

Here are some of the characteristics of the ratio data:

• Absolute zero: As explained earlier, ratio scales have an absolute zero. Although they are not categorical, they have a specific order and have equal distance between their values. We can use multiplication or division to describe them because of the absolute zero characteristics in the ratio scales. For example, 40kg is twice more than 20kg, or the distance from point A to B is 50km which is twice less than the distance from point B to C, i.e., 150km.
• No negative number: Because ratio scales have an absolute zero, they do not have significant negative numbers. Unlike interval scales where -10 would hold a meaningful, ratio scales begin from a certain point as a way of measurement.
• Possibilities for statistical measurement: Since ratio scales have an absolute zero, variables in this type of data can be added, subtracted, multiplied, or divided. Furthermore, all measures of central tendency can be performed with ratio scales. These include the mean, mode, and median.

How is Ratio Data Collected?

There are several ways to collect ratio data. Your method solely depends on the type of research you’re doing. Common ways to collect data are surveys, questionnaires, and interviews.

• Interviews: In this method, participants are interviewed ad data is collected from there. Such discussions are often structured, semi-structured, or unstructured. They can be either face-to-face, telephonic, panel or group, and computer-assisted.
• Questionnaires and Surveys: This is a standard data collection method for qualitative data. A survey or a questionnaire is handed out to participants, and they are asked to answer a series of questions. Their answers are then recorded for qualitative analysis.
• Direct Observation: Ratio data can also be collected through direct observation. For example, if you were to measure the height of some athletes, there will be a point zero. A measurement of 0 would indicate an absence of the athlete.
• Automated Data Collectors: Some roads have automated speed calculators, which are an efficient and practical way to collect data.

Here’s an example,

Question: How much is your family’s monthly income?

Possible answers: $0-$5000, $5000-$10,000, $10,000-$15,000, $15,000 or more.

Note: The distance between the intervals is equal, i.e., $5000.There is also a true zero. Also, the answer can not be negative, i.e., $ -20

Analyzing Ratio Data

Ratio Data can be analyzed and interpreted in precisely the same way as the interval way. These include the following methods:

• Mean: This is the average of the interval data set. Mean can be calculated by adding the numbers in the interval data set and dividing this total by the number of values in the set.
• Median: Median is used to determine the middle value in the data set. Since all values in the interval data are equal distance apart, the median is easy to calculate.
• Mode: This is the value that is the most occurring in the data set.
• Standard Deviation: This measures the dispersion of a data set relative to its mean.
• Percentiles: This is the percentage of values that fall below a specific number from the data set. For example, let’s suppose the 75th percentile of an IELTS test is 6.0 overall. If you scored an overall 6.0, this would mean that your score was better than 75% of the IELTS test takers.
• Range: This is the difference between the highest and the lowest value in the data set.

What Statistical Tests can you do with Ratio Data?

You can perform several statistical tests with ratio scales, mainly,

• T-test
• ANOVA
• Pearson Correlation
• Simple Linear Regression

And that’s a wrap! That’s all you need to know about ratio data, its definition, examples, and analysis.

FAQs About Ratio Data