**Ratio Data: Definition**

Ratio Data is defined as quantitative data, having the same properties as interval data, with an equal and definitive ratio between each data and absolute “zero” being treated as a point of origin. In other words, there can be no negative numerical value in ratio data.

For example:

Four people are randomly selected and asked how much money they have with them. Here are the results : $20, $40, $60, and $80.

- Is there an order to this data? Yes, $20 < $40 < $60 < $80.
- Are the differences between the data values meaningful? Sure, the person who has $40 has $20 more than the person with $20.
- Can we calculate ratios based on this data? Yes, because $0 is the absolute minimum amount of money a person could have with them.
- The person with $80 has four times as much as the person with $20.

Ratio data has all properties of interval data such as – data should have numeric values, a distance between the two points are equal, etc. but, unlike interval data where zero is arbitrary, in ratio data, zero is absolute.

An excellent example of ratio data is the measurement of heights. Height could be measured in centimeters, meters, inches, or feet. It is not possible to have a negative height. When comparing to interval data, for example, the temperature can be – 10-degree Celsius, but height cannot be negative, as stated above.

Ratio data can be multiplied and divided, and this is one of the significant differences between ratio data and interval data, which can only be added and subtracted. In ratio data, the difference between 1 and 2 is the same as the difference between 3 and 4, but also here 4 is twice as much as 2. This comparison is impossible in interval data.

**Ratio Data Analysis**

Ratio data, alongside the 3 other variable measurement scales, is fundamentally a quantitative data capturing method. This means all types of statistical analysis techniques can be applied to Ratio Data.

Below are some of the popular ratio data analysis techniques:

**Trend analysis**

Trend analysis is a popular ratio data analysis technique used to draw trends and insights by capturing survey data over a certain period of time. In other words, trend analysis on ratio data is conducted by capturing data using a ratio scale survey in multiple iterations, using the same question. Trend analysis also plays a critical role in the predictive analysis, where a set of time-bound data is compared and analyzed for predicting future trends.

**SWOT Analysis**

Analysis conducted to evaluate an organization’s strengths, weaknesses, opportunities, and threats is called SWOT analysis and is widely used to evaluate ratio data. Strengths and weaknesses are internal aspects of an organization, while opportunities and threats are external to an organization. An organization can measure ratio data to evaluate market competition as well as plan future marketing activities using the SWOT analysis results.

**Conjoint Analysis**

Conjoint Analysis is an advanced level market research technique usually implemented to analyze how individuals make complicated decisions on a ratio scale. It helps find important factors for customers before they make decisions when they have multiple options available at their disposal. Marketers can test their websites, conduct price research, or improve product features using conjoint analysis.

**Cross Tabulation**

Cross-tabulation, in statistics, is a method to understand the relationship between multiple variables. The contingency table, also known as a crosstab, is used to establish a correlation between multiple ratio data variables in a tabular format. Informed decisions can be taken after analyzing the data from a contingency table. Market researchers usually analyze customer intent and product performance using cross-tabulation as they provide a comparison between two or more variables.

**TURF Analysis**

TURF analysis stands for Totally Unduplicated Reach and Frequency analysis- is a method that allows a marketer to analyze the potential of market research for a combination of products and services. It evaluates the ratio data of customers reached by a particular source of communication and its frequency. This analysis technique is used by researchers to understand whether a new product or service will be well-received in the target market or not. This analysis method was used mainly for designing media campaigns but has expanded to being used in product distribution and line analysis.

**Characteristics of Ratio Data**

**1. Absolute Point Zero **– Ratio data is measured on a ratio scale. One of the distinctive characteristics of ratio data is the true absolute zero point, which makes the data relevant and meaningful in a manner where it is right to say, “one object is twice as long as the other” or 4 has twice the value as 2.

**2. No Negative Numerical Value **– Ratio data doesn’t have any negative numerical value. For a value to be a ratio data researcher, first must evaluate if it meets all the criteria of interval data and has an absolute zero point. For example, weight cannot be negative, -20 Kgs doesn’t exist.

**3. Calculation **– Ratio data values can be added, subtracted, divided, and multiplied. A unique statistical analysis is possible for ratio data. Chi-square can be calculated using a ratio scale for ratio data. Mean, mode and median can also be calculated for the ratio data.

**Ratio Data Examples**

Following are the most commonly used examples of ratio data that can be used in surveys to extract ratio data:

**What is your weight in kgs?**

- Less than 50 kgs
- 51-60 kgs
- 61-70 kgs
- 71-80 kgs
- 81-90 kgs
- Above 90 Kgs

**What is your height in feet and inches?**

- Less than 5 feet.
- 5 feet 1 inch – 5 feet 5 inches
- 5 feet 6 inches- 6 feet
- More than 6 feet

**What is the number of burgers you can eat daily?**

- 1-2
- 2-3
- 3-4
- 4-5
- 5-6
- More than 6