A User’s Guide to Measuring Data
Discover the way to measuring data.
In the last article We already talk about What Data is. Now, it’s time to take that knowledge and apply it.
Level of measurement
is a way to categorize different types of data based on the level of information they provide.
Four main levels of measurement, each with distinct characteristics:
- Nominal
When data is at the nominal level, it means that the categories or labels have no natural order or ranking. This means you can’t say that one category is “greater” or “lesser” than another. Simply its Predetermined categories and can’t be sorted.
For example, think of colors (like red, blue, green), or types of fruit (like apple, banana, orange). These categories have no inherent order or ranking — they are just different labels representing distinct categories. You can’t say that “blue” is greater or less than “green” in the same way you can with numbers.
- Ordinal
At the ordinal level, data have a meaningful order or ranking, but the intervals between values are not consistent or meaningful. So…it can be sorted but still lack of scale.
Sure, let’s use a survey example to illustrate ordinal data.
Imagine conducting a survey about customer satisfaction with a product, and you ask respondents to rate their satisfaction on a scale from 1 to 5, where:
1 = Very Dissatisfied
2 = Dissatisfied
3 = Neutral
4 = Satisfied
5 = Very Satisfied
This type of survey response is considered ordinal data. The responses have a meaningful order or ranking, as “Very Dissatisfied” is considered lower on the satisfaction scale than “Dissatisfied,” and so on.
However, it’s important to note that while you can sort these responses from least satisfied to most satisfied, you can’t accurately say that the difference in satisfaction level between a “Very Dissatisfied” (1) and a “Dissatisfied” (2) response is the same as the difference between a “Satisfied” (4) and a “Very Satisfied” (5) response. This is why it’s considered ordinal.
- Interval
At the interval level, data have a meaningful order, and the intervals between values are consistent and meaningful. However, there is no true zero point.
The Example is your favorite temperature range.
- Ratio
At the ratio level, data have a meaningful order, consistent intervals, and a true zero point. This means that ratios between values are meaningful.
For instance, if one person is 100 Kg and another is 50 Kg, we can say that the first person is twice as heavy as the second.
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