Ratio Scale: Definition, Characteristics & Examples

To use a ratio scale or any other type of measurement, we need to understand what a variable is.

A variable is something whose value changes and can be measured. For example, if you are looking at the voters in a particular area, there are many variables that you can use. It could be area, age, sex, job, previous voting history, and so on.

Here’s a ratio scale example to help you understand it better:

Which age group do you fall into?

1. 15-25
2. 25-40
3. 40-60
4. 60+

In this blog, we’ll cover:

• What is Ratio Scale?
• Ratio Scale Examples
• Characteristics of Ratio Scale
• How to Measure Ratio Scale?
• Popular Ratio Data Analysis Techniques

What is Ratio Scale?

The ratio scale is the variable measurement scale that produces the order of variables. Moreover, the ratio scale of measurement reveals the difference between the variables.

A zero on a ratio scale means there is no presence of the variable that you are measuring.

The ratio scale of measurement helps understand the intervals, values, true zero characteristics, and the ultimate order in calculating ratios. For this reason, the ratio scale is one of the most informative scales of measurment.

Here’s a simple ratio scale questionnaire created with SurveySparrow.

Create a free account below to create similar ratio scale questions in your surveys. Plus, you can try all of SurveySparrow’s features FREE for 14 days.

What is a true zero?

On a ratio scale, zero represents the total absence of the variable.

For example, the number of teenagers in a ward or high-rise buildings is a ratio variable. It implies that the ward does not have teenagers or high-rise buildings. If you have a true zero on your ratio scale of measurement, then you can measure the ratios of values.

The Four Levels of Measurement

The level of measurement of a variable helps you understand the statistical test that you need to use. How you measure a variable is considered as the level of measurement.

1. Nominal scale:  A nominal scale is used to label variables in different classifications. Moreover, the nominal level of measurement is not used to denote a quantitative value or order
2. Ordinal scale: The ordinal level of measurement is used for things that cannot be mathematically measured, such as satisfaction, happiness, etc.
3. Interval scale: The interval level of measurement is denoted as a numerical scale where the order of the variables and the difference between the variables are known.
4. Ratio scale: The ratio level of measurement is a variable measurement scale that is used for the order of the variables. But it also makes the difference between the known variables along with the information about the true zero’s value.

Each level of measurement is used to capture data in the form of surveys and questionnaires, with each of them being a multiple-choice question. Since each scale is an incremental level of measurement, they fulfill the function of the previous scale.

In this article, we are going to look at the ratio scale, the 4th level of measurement, which is the ratio scale of measurement.

Four Ratio Scale Examples

The following are some of the most commonly used ratio scale examples.

#1. What is your weight range?

1. 50-60 kgs
2. 61-70 kgs
3. 71-80 kgs
4. 81-90 kgs
5. More than 91 kgs

#2. What is your height on the metric scale?

1. Less than 4 feet
2. 4 feet one inch to 4 feet 6 inches
3. 4 feet 7 inches to 5 feet 2 inches
4. 5 feet 3 inches to 5 feet 8 inches
5. More than 5 feet 9 inches

#3. How much time do you exercise in a day?

1. 15 to 30 minutes
2. 31 to 60 minutes
3. 61 to 75 minutes
4. 76 to 90 minutes.

#4. What is the amount of calories you consume every day?

1. 1200 to 1500 cals
2. 1501 to 1800 cals
3. 1801 to 2100 cals
4. 2101 to 2400 cals
5. Greater than 2400 calories

Some other examples are sales figures, ruler measurements, income during a period of time, years of education, number of children, etc.

Characteristics of Ratio Scale

1. Highly informative: If you want to do highly effective market research, then the ratio scale is the most effective of all. It tells you about the order and the equal distance between two adjacent values. The zero in the ratio scale also has a lot of relevance. It tells the difference between “how much.”
2. It has an absolute zero characteristic. The ratio level of measurement can be represented in orders and the equal distance between two units.
3. The units in the ratio scale have several unique and significant properties. One of the biggest advantages is that it allows for unit conversion. For example, energy is measured in terms of Joules, kilogram calories, gram calories, and British thermal units. There are many more units of energy per unit of time that exist.
4. The ratio scale offers unique statistical analysis possibilities. The variables can be systematically added, subtracted, multiplied, and divided. The statistical analysis such as median, mode, and mean can be calculated. In fact, even chi-square values can be calculated using a ratio scale.
5. It doesn’t have a negative number. If you want to measure any object on this scale, the researchers must first see to it that the object meets the criteria for the interval scale and also has an absolute zero characteristic.
6. It accommodates the characteristics of three other variable measurement scales. You can label the variables, arrange the order of variables, and find the equal distance between the variables.
7. If researchers want to find out when to use a ratio scale, then they should make sure that the variables have all the characteristics of an interval scale and the presence of the absolute zero value.
8. It can quantify the difference between each value and it is possible to calculate the ratios too.

How to Measure Ratio Scale?

The data that you get from the ratio scale is useful for statistical analysis. Once you collect the necessary input, you can collect descriptive and inferential statistics.

For inferential statistics:

When the ratio scale is of normal distribution, then parametric tests are best for analysis. The latter helps make strong conclusions using your data.

Some of the common examples are:

• Using Pearson’s r for finding a correlation between two variables.
• Deploying the T-test for comparison of means.
• Using ANOVA for the comparison of means and the requirement for this test is at least 3 or more samples.
• Simple linear regression tests also require 2 variables.

For descriptive statistics:

#1. Frequency distribution: To find the frequency distribution, you can group the data in a table or plot it in a graph.

#2. Central Tendency: You can use mean, median, or mode to calculate this. Mode is the most repeating value in your data. The Median is the value that is in the middle of the data. Mean uses all the values to give the researcher a single value for a central tendency.

If the data you are using is distributed quantitative data, then it is advisable to use mean since it uses a single value of the data to measure the result.

#3. Variability: The standard deviation, range, and variance are pivotal to indicating the presence of variability.

If you want a range, you need to subtract the lowest value from the highest value of data.

#4. Standard deviation indicates how far the score in your data lies from the mean on average. It is considered the average amount of variability.

Moreover, a variance is the average standard deviation from your data’s mean. The square of the standard deviation gives you the variance.

What are some of the popular ratio data analysis techniques?

SWOT analysis:

• It is used to calculate the strengths, weaknesses, opportunities, and threats of an organization.
• SWOT analysis also helps evaluate ratio data. Strength and weaknesses constitute the internal aspects of an organization, while opportunities and threats come from external sources.
• Organizations measure ratio data to understand what are the areas that they need to be careful about and which are the ones that they should be taking advantage of.

Conjoint Analysis:

• It is a popular market research technique to analyze how individuals make complicated decisions based on a ratio scale.
• Conjoint analysis helps organizations understand which are the various purchasing factors for customers when they have several choices in front of them.
• Businesses use conjoint analysis to conduct price research, test websites, improve product functionalities, add better user interfaces, and so on.

Trend Analysis:

• It is a popular ratio data analysis technique for finding out the various trends and insights by gathering data.
• Trend analysis captures insights with the help of survey data from recurring surveys, using the same question.
• Trend analysis is also big on predictive analysis; it lets you capture time-bound data and uses it to predict future needs.

TURF Analysis:

• TURF is short form for Totally Unduplicated Reach and Frequency Analysis. Using the analysis method, the business can analyze the potential of combining products and services.
• TURF values the ratio data of customers who have been contacted through a particular source of communication and frequency.
• Will the target market accept a new product, service, or combination? TURF analysis will give a pretty comprehensive idea of that. It is of great help in product distribution and line analysis.

Cross-Tabulation:

• This method helps understand the relationship between multiple variables.
• Also known as the contingency table, cross-tabulation helps establish a correlation between multiple ratio data variables in the form of a table. Using the contingency table, businesses can make data-driven decisions.
• Businesses employ cross-tabulation analysis to understand the correlation between customer intent and product performance.

Moreover, there are a number of descriptive and inferential statistics that we can apply to the ratio scale. A ratio scale can calculate all statistical analyses including mean, median, mode, variance, and standard deviation. It can also calculate statistical tests such as t-test, f-test, chi-square, correlation, and so on.

Wrapping Up

To perform statistics on data, you have to understand the variables and find out what should be measured using the variables. Statistics offers different kinds of measurement and they are usually classified into qualitative and quantitative data.

The ratio scale has all the characteristics necessary to make it a research scale, especially for pure sciences. Not only does it offer everything that the interval scale does, but its importance is also even more so prominent in the field of market research because of a well-defined zero. The non-arbitrary zero in ratio scale allows for the easy calculation of the value behind the attributes collected in the data for research.

If you are looking to find an online survey tool to capture data from your potential customers, you have come to the right place. SurveySparrow is one of the best online survey tools in the market, and you will be able to ask brilliant ratio scale survey questions and conduct simple to complex surveys using them. Get in touch with us to understand how we can help you.