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Ratio Data : Definition, Examples, Survey Questions & more

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Data analysis is an important step in research. You can only say your research was a success when you have analyzed the data and created report on it. So, to be able to explore deep into your data you must have clear understanding of the different types of data. 

There are four types of data aka levels of measurement – nominal (categorical), ordinal, interval, and ratio data. In this blog, we will define ratio data, look through some ratio variable examples, and learn to calculate it.

What is Ratio Data?

Playing an effective role in market research, ratio data is a form of numerical data which is quantitative in nature. The ratio data collected on a ratio scale has an equal distance between adjacent values. This characteristic makes ratio data similar to interval data. 

However, the data from ratio scale stands apart due to the factor of “absolute zero”. The zero-point ratio data is not arbitrary but it has a meaningful presence. The presence of zero means there can be no negative variable in ratio data. 

Its characteristic is that the data can be measured and put in order. Additionally, the variables are equidistant and include meaningful zero. Data gathered in ratio scale can be continuous as well as discrete.

Now that we understand what ratio data means, let’s see what are some of the common examples of ratio variables.

What are ratio variables?

When you use data from ratio scale in analysis the values are called ratio variables. When you gather ratio variables you can calculate the data to generate insights. 

With the ratio variables you can compare if the data are equal or not, you can rank them in order, or perform mathematical calculations such as add, subtract, multiple, and divide. 

Here are some of the common ratio variable examples 

  • Kelvin scale. 
  • Height.
  • Speed. 

Kelvin Scale: One most noted example of ratio data is the temperature on a Kelvin scale. The O degrees in a Kelvin scale represent the total absence of thermal energy. 

Height: Height or length is measured in meters, inches, or feet. Height cannot have a negative value. The zero is the starting point in height and the distance between two adjacent variables is also the same. 

  • For instance, we can call a 10ft tall tree twice as a 5ft tall tree. 

Speed: Speed can also be an example of a ratio scale. Two speeds on one scale will have the same ratio as two speeds on another scale. 

  • For example, The Ratio between 72km/h to 36km/h is 2
    • The ratio between 44.738 mph to 22.369 mph is 2

Other examples include time interval, weight, age, etc.

Let’s now learn how you can analyze ration data.

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How can you calculate Ratio Data?

Ratio data with its property of equidistant and meaningful zero is exposed to many methods of calculation. This makes it a popular and convenient option for market research. 

There are four ways to calculate the data gathered on ratio scale: 

  1. Grouping. 
  2. Sorting.
  3. Difference.
  4. Magnitude. 

We have explained these four methods below. 

  1. Grouping: You can calculate the ratio variables by comparing if they are equal or not, same or different.
  2. Sorting: You can compare the degree of the variables. Whether one value is greater or lesser than another value can also be figured. 
  3. Difference: The ratio variables can be added or subtracted. 
  4. Magnitude: You can also multiply and divide the ratio variables to derive depending on your research. 

A ratio data example would be –  you can weigh if a football is heavier than a basketball and by how much by using the mentioned methods.

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What are the characteristics of ratio data?

When we define ratio data we also highlights its special characteristics and what makes it different from the rest of the data types. 

The characteristics of data gathered from ratio scale can be summed up as following. 

It can be measured and sorted in order with variables in equidistant and with a meaningful zero. 

Absolute Zero: The one distinctive feature it has is the absolute zero point. This enables the calculation of the degree of difference between two variables and also the ratio between them.

The data can positively answer “how much” between two ratio variables. A ratio data example can be, that the weight of 90 kg is twice 45kg. 

No negative value: On a ratio scale there cannot be any negative value. The zero is the starting point in a ratio scale which means numerical value less than zero cannot exist. 

For example, a person cannot have negative height. We cannot say someone is -3ft tall.

Calculation: Variables in ratio data are subjected to addition, subtraction, division, and multiplication. 

The data can also be calculated using the following method

  • Mean, median, or mode for Central Point
  • Percentiles, interquartile range, and standard deviation
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How to conduct statistical analysis of ratio data?

Ratio data is one of the four levels of measurement scales. Naturally, all types of statistical analysis can be applied. Some of the popular analysis techniques are listed below.

  1. Trends. 
  2. SWOT. 
  3. Conjoint. 
  4. Contingency tables. 
  5. TURF. 

Let’s learn about these five ways to analyze ratio data. 

1. Trend

Trend Analysis is conducted by using the same questions in multiple recurrences for the purpose of the survey. The data from such surveys are gathered over time to determine trends and insights. 

This also proves integral in predictive analysis. For predicting future trends, a set of data of a specific period is analyzed and compared.

2. SWOT 

Strength, Weakness, Opportunities, and Threats, of an institute, are evaluated in this staitsical analysis. This implies that the strength or weakness of a marketing strategy along with the internal policies of the organization is analyzed. Additionally, the threats and opportunities posed by external factors are also taken under consideration and analyzed for future planning.

3. Conjoint 

This statistical technique is used for market research to how people value different attributes of a service or product. The ratio data can help understand this complicated decision made by individuals when they have multiple attributes at their disposal to choose from. 

4. Contingency tables

This method helps analyze the relationship between multiple values. Also known as cross-tabulation, this analysis method uses the tabular format to determine the correlation between multiple variables in ratio data. 

This statistical analysis is used in marketing research to analyze customer inter and product/service performance. 

Voxco’s market research software allows you to create cross tabs to uncover trends and patterns and also configure the visual board as per your requirement. 

5. TURF

Total Unduplicated Reach and Frequency Analysis, enables the researcher to explore potential marketing research for a combination of products or services. This method examines how well the product or service will be received in the target market. 

Initially, TURF analysis was used by media schedulers to increase the reach and frequency of media spending. However, now this statistical analysis technique is used to determine the market potential of a product or service.

For example, a company has plans of launching eight flavors of chocolate, but only four out of eight might be purchased the most.

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Examples of ratio data questions in survey

Which age group do you fall under?

  • 15 – 20 years
  • 21 – 25 years
  • 26 -30 years
  • 31 – 35 years

How much time do you spend reading storybooks?

  • 0 – 3 hours
  • 3 – 6 hours
  • 6 – 9 hours
  • More than 9 hours

How much does your dog weigh?

  • 20 – 25 kg
  • 25 – 30 kg
  • 35 – 35 kg
  • 35 – 40 kg

Wrapping up;

In this blog, we have defined ratio data and ratio variables, shared how you analyze the data. We have also showed you what kind of questions you can ask to gather this data. 

It is important that you learn about the different types of data while conducting surveys and research. Knowing about the different types can help you decide what kind of questions you should ask and how you can evaluate the data.

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