Nominal, ordinal, interval, and ratio are the four fundamental levels of measurement scales used to capture, classify, and analyze collected data. These levels of measurement tell us how precisely variables are recorded and determine how we can analyze the data. The four levels of measurement are cumulative, meaning that they each take on the properties of lower levels and add new properties. Here is a brief overview of each level:
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Nominal: This level of measurement is the simplest and classifies variables qualitatively. It divides them into named groups without any quantitative meaning. Nominal variables can be coded with numbers, but the order is arbitrary, and any calculations, such as computing a mean, median, or standard deviation, would be meaningless.
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Ordinal: This level of measurement has all its variables in a specific order beyond just naming them. The order matters, but not the difference between values. Ordinal variables can be ranked, but the distance between them is not known. For example, academic grades (A, B, C, and so on) are ordinal data.
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Interval: This level of measurement offers labels, order, as well as a specific interval between each of its variable options. The distance between the values is known, and the difference between them is meaningful. However, there is no true zero point. For example, temperature measured in Celsius or Fahrenheit is interval data.
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Ratio: This level of measurement bears all the characteristics of an interval scale. In addition to that, it can also accommodate the value of “zero” on any of its variables. This means that a value of zero represents the absence of the variable being measured. Ratio scales allow for the calculation of ratios and the use of all statistical techniques. For example, age is typically considered to be measured on a ratio scale.
The level of measurement of a variable decides the statistical test type to be used. Depending on the level of measurement of the variable, what we can do to ana...
