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Introduction
Data is one of the most important part of any machine learning model. We need to understand our dataset properly. A dataset may contain different variables. These variables can be categorized by different levels of measurements: Nominal, Ordinal, Interval, and Ratio. Let us learn more about these levels of measurements.
It is the first level of measurement. It is also known as the categorical variable scale. This scale is used for labeling variables and don't have any numerical value. Arithmetic operations cannot be performed on this scale.
Example of Nominal scale measurement:
What is your preferred joining location?
Bangalore
Gurgaon
Pune
Here, the answer must be from the given options, and no meaningful operations can be performed on them.
It is the second level of measurement. We can order or rank the data on this scale, but we cannot know the actual difference. This scale is usually used for non-mathematical concepts like happiness, feeling, discomfort, etc.
Example of Ordinal scale:
How are you feeling today?
Sad
Neutral
Happy
Ranking of students
1st
2nd
3rd
Interval Scale
In this scale, the difference between the variables is meaningful. The variables are measured precisely and not in any relative manner. Here, there is no true zero. Only the difference between the values is significant. We can calculate the mean and median of the values in this scale.
Examples include temperature scale, IQ scale, etc.
This is the final level of measurement. It is similar to the interval scale, but we have a true zero on this scale. True zero implies that the variable that we want to measure does not exist. We can convert our values from one unit to another on this scale, for example, kilogram to gram. In this scale, we can do calculations like additions, subtraction, multiplication, calculating mean, median, mode, etc.
Examples of ratio scales: Number of employees in a company, length in centimeters, age in years, etc.
Are there any sublevels of measurement? Yes, there are sublevels of measurement. For example, nominal scale spss.
What is the difference between qualitative and quantitative data? Quantitative data deals with numerical values, while qualitative data are measures of types that can be represented as names, symbols, etc.
Conclusion
In this blog, we talked about the different levels of measurements. We looked at Nominal, Ordinal, Interval, and Ratio scale and some of their examples. Coding ninjas guided paths offer a complete understanding of machine learning, data structures, C++, Python, etc.
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