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Interval vs Ratio Scales: Insights from Survey Methodology

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The level or scale of measurement defines the nature of the data collected. In statistical observation, the presentation of data depends on the type of collected data, discrete or continuous random variables.

Of the four levels of measurement, the interval scale and ratio scale are the higher levels. The interval scale is third in the four “levels of measurement,” while the ratio scale is the fourth and highest in order.

There are but few differences between an interval scale and a ratio scale. We will discuss the difference between the two in this blog.
To understand these differences, let’s first understand their definition.

What is the interval scale?

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The interval scale collects and measures data where intervals between two points are of equal distance. The scale provides a degree of difference along with the rank and order of the values as collected from a research or survey.

An interval scale, however, has a zero point with an arbitrary presence. This means that the value of zero has no real meaning.

What is the ratio scale?

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The ratio scale, on the other hand, has the characteristics of all the levels of measurement as created by S.S. Stevens. It can rank and categorize the data obtained through the scale.

Moreover, the distance between two variables in a ratio scale is also equal in distance. In addition, a ratio scale has a true zero point, meaning the value of zero is not arbitrary.

Interval scale vs ratio scale: characteristics

Interval scale vs ratio scale: Characteristics
Measurement scales provide a framework to understand and interpret data systematically. Among the various scales, interval and ratio scales have unique characteristics.




Measurement interval 

Equal intervals between consecutive points.

Equal intervals with the presence of a true zero.

Absolute zero

Lacks a true zero point.

Possesses a true zero point.

Statistical analysis

Limited to addition and subtraction

Allows for meaningful multiplication and division.

Meaningful ratios

Ratios are not meaningful due to the lack of zero.

Ratios are meaningful due to the presence of zero.


IQ scores, celsius temperature, NPS data, etc.

Height, weight, income, etc. 

To understand the difference between the two scales, with the help of the definition, we can take the example of temperature.
Interval Scale

– Celsius and Fahrenheit scales are examples of interval scales.

The point of degrees in both these scales has equal intervals of exactly one degree. For instance, the distance between 29 and 30 degrees is the same as the distance between 99 and 100 degrees.

However, due to the absence of absolute zero, you cannot tell by how much the temperature is higher or lower. For example, you cannot say if 40 degrees is twice as hot as 20 degrees or if -20 degrees is half as cold as -40 degrees.

[Related Read: How to appropriately use an Interval Scale in your Survey?]

Ratio Scale

-A Kelvin scale is an ideal example of a ratio scale.

Along with all the other values, on a Kelvin scale, the zero point has a relevant meaning. For instance, you can tell on a Kelvin scale that 40K is twice as hot as 20K.
Also, the presence of absolute zero in a Kelvin scale means that nothing can be colder than 0K. This is because, on a ratio scale, there can be no negative number.

Key differences in interval scale vs ratio scale

Let’s look into some of the ways interval scale and ratio scale differ in characteristics.

1. Variable:

In an interval scale, the data collected can be added, subtracted, and multiplied. The scale allows computing the degree of difference but not the ratio between them.
A ratio scale permits not only addition, subtraction, and multiplication but also division. That is, you can calculate the ratio of the values.

2. Statistical analysis:

In the interval scale data, you cannot perform multiplication and division. It has no meaning due to the absence of absolute zero. You can analyze the data using a t-test, frequency distribution (mean, mode, and median), range, standard deviation, and variance.
The coefficient of variation is not permitted because the interval scale cannot define the ratio.
In the ratio scale data, you can perform multiplication and division. You can employ descriptive statistics like frequency distribution: mean, mode, median, and range, standard deviation, coefficient of variation, and variance. Additionally, you can also analyze ratio data using ANOVA, linear regression, etc.

3. Magnitude:

In an interval scale, the available data size and the magnitude of the variable can be measured as the multiple factors of a defined unit.
The case of a ratio scale is so that the magnitude as a multiple is a factor of one defied unit in terms of another.

4. Absolute zero:

An interval scale can have negative values because the zero point does not mean an absence of value. Zero degrees Celsius does not mean the absence of temperature.
On a ratio scale, there are no negative values. The zero in a ratio scale means a total absence of variables.

5. Measurement properties:

Interval scale, while providing a consistent measurement interval, lacks the ability to make meaningful statements about the ratio between two measurements.
Ratio scale, on the other hand, enables you to form a meaningful ratio.
Understanding these distinct properties is essential in choosing the appropriate scale to ensure accurate and relevant data collection.

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Interval vs. ratio scale: examples

Interval scale examples:

Time is the value of the interval scale because there is no zero. You cannot tell when the time started.
An interval scale is mostly used to gather feedback based on agreement, satisfaction level, or likelihood. It is commonly found in question-type surveys where the choice of options is so scaled that a numerical value can be allotted to them in order to calculate.

Interval scale question:

How likely are you to refer our product to a friend?

1 (very unlikely) 




5 (very likely)

Interval scale examples in market research:
Likert scale for product feature preferences:
→ You can use a Likert scale ranging from 1 to 5 and ask participants to express their preference for various features of a product.

Customer satisfaction score:
→ Respondents can rate their satisfaction on a rating scale of 0 to 10. In this scale, the intervals between the scores are equal, but the absence of true zero means that a score of 0 does not represent zero satisfaction.
Ratio scale examples:
Duration is a case of ratio scale for the fact that duration has a starting point. The zero in duration has a meaningful presence.

A ratio scale can measure any data that has “zero points” characteristics. A ratio scale is ideal for measuring age, weight, height, etc. In marketing research, it can calculate sales, shares, the volume of the customer, etc.

Ratio scale questions:
How much does your cat weigh?
Less than 30 kg
30 to 50kg
50 to 70 kg

How much time do you spend studying after school?
Less than 2 hours
2 – 4 hours
4 – 6 hours
Ratio scale examples in market research:
Annual household income:
→ You can use a ratio scale when asking questions about the annual income of the respondents. In this scale, a true zero indicates no income. As a result, this allows for a meaningful ratio like one respondent earning twice as much as another.

Price of consumer goods:
→ When asking about the spending of respondents on a particular goods the data can help analyze the variable in a ratio scale. If one participant spends $200 on groceries and another spends $100, you can interpret the ratio of their spending meaningfully.

Summing up;

As per the properties of measurement scales – Identity, Magnitude, Equal Units, & True Zero – Interval Scale and Ratio Scale differ only in the presence of True Zero. 

Variables of the interval scale are equally divided, however, there is no predefined zero. 

In contrast, zero is the starting point of the ratio scale. This means that within ratio data, you can say 6 is twice 3. 

Understanding the difference between the four levels of measurement scale can help you determine which scale to use to generate insightful data and conduct efficient analysis. 


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