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Ratio Scale Vs Interval Scale Measurements

Statistical analysis and data analysis require an understanding of different measurement scales. Yes! There are multiple ways to measure a variable (or scale or measure) in statistics. Apart from the mentioned ratio scale vs. interval scale, the nominal and ordinal scales exist.

As the name suggests, the Nominal scale is just a naming scale. Here, the variables are merely ‘named’ or tagged in no specific order.

Whereas on an ordinal scale, the variables are not just ‘named’ but also arranged in a specific order.

Moving on from the ordinal scale toward the interval and the ratio scales is where things get confusing.

So let’s get started!

What is Interval Scale?

An interval scale is a measurement scale that has equal intervals between any two given points.

On an interval scale, each variable is tagged, arranged in order, and has a specific interval with other variables.

You can rank, count, add, or subtract the measurements in this category. However, these measurements do not provide any sense of ratio between them.

An example might better explain it.

Interval Scale Example

The temperature scale is a perfect interval scale example.

Imagine you have a thermometer that measures temperature in degrees Celsius.

Now, you can tell that the difference between 10°C and 20°C is the same as the difference between 20°C and 30°C. This means that there is a fixed unit of measurement.

However, 0°C does not represent a complete absence of temperature, as negative temperature values do exist. Instead, it is simply used as a reference point.

Therefore, we cannot say that 20°C is twice as hot as 10°C.

The interval scale is often used when the nominal or ordinal scales fail i.e., when measuring the difference between variables. An interval scale is also used to calculate the mean and median values.

Despite the interval scale having various applications, it still falls short when it comes to calculating the true zero value. This is when we move to the next measurement scale: the ratio scale.

What is Ratio Scale?

Unlike other measurement scales, ratio scales have equal intervals between their values and an actual zero point.

Therefore, you can arrange the variables in a specific order, as well as calculate the difference between them and the true zero. The equation is calculated based on the assumption that all variables are in the same order, the difference is the same, and the variables have zero options.

Besides carrying out everything that a nominal, ordinal, and interval scale does, the ratio scale can also estimate the value of true zero. The best examples of a ratio scale are height and weight.

``ALSO READ: Nominal, Interval, Ordinal Scale: A Guide to Measurement & Data Scales ``

Ratio Scale Example

Consider you’re measuring weight in kilograms.

A value of 0 kg represents a complete absence of weight, with equal intervals between each value.

Therefore, we can rightly say that 10 kg is twice as heavy as 5 kg.

Negative values don’t exist in a ratio scale, and thanks to this, you can calculate the mean, mode, and median values.

Before deciding whether to use a ratio scale, the researcher should examine whether the variables have the interval scale attributes and a current absolute zero value.

``ALSO READ: Descriptive Statistics: The Concept and its Use in Survey ``
``Ratio Scale vs. Interval Scale: Differences ``
• In ratio scales, a value of zero represents a complete absence of the measured attribute. Hence, it is possible to make statements about relative magnitudes, such as “twice as much” or “half as much.”

On the other hand, interval scales have a fixed unit of measurement but do not have a true zero point, so it is not possible to make statements about relative magnitudes.

• Additionally, interval scales are able to perform different types of statistical analysis than ratio scales.

• Ratio scales can be used for all types of statistical analyses, including descriptive and inferential statistics. In contrast, interval scales can only be used for certain types of studies, such as mean and standard deviation calculations.
``ALSO READ: Nominal VS Ordinal Scale: Explore The Difference   ``

Conclusion

Understanding the measurement levels is crucial if you want your surveys to be precise. Failure to understand them, you face the risk of using improper analysis methods and drawing incorrect conclusions.

This is especially true for the ratio scale vs interval scale.

You shouldn’t rush into choosing the right level of measurement, as it is a crucial step in planning a research study. Take enough time to consider the measurement level that is most suitable for your survey.

If you know what types of analysis are appropriate for your data, you can make informed decisions.

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