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Why we use Measurement Levels?
In Statistics, we collect data and then analyze it. We can manage the data in different ways, and then we have other ways to explore it. For analyzing the data, it needs to be well arranged. For this, we use different measures. We can measure the data in many ways: we can categorize it, rank it, and make proper groupings.
- Nominal– data used to categorize, for example, grouping people or things together based of similar characteristics, such as age or race.
- Ordinal– data used to rank certain things. For example, when someone is asked to rank customer service on a scale using numerical values ( lets say 1-5) 1 being bad service, 5 being good service, and the numbers in between to represent average service.
- Interval– data that is measured in increments on a scale, such as temperature or academic testing scores.
- Ratio– data that encompasses characteristics of nominal, ordinal, and interval data.
Nominal, ordinal, interval, and ratio data
Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.
|Nominal level||Examples of nominal scales|
|You can categorize your data by labelling them in mutually exclusive groups, but there is no order between the categories.||
|Ordinal level||Examples of ordinal scales|
|You can categorize and rank your data in an order, but you cannot say anything about the intervals between the rankings.
Although you can rank the top 5 Olympic medallists, this scale does not tell you how close or far apart they are in number of wins.
|Interval level||Examples of interval scales|
|You can categorize, rank, and infer equal intervals between neighboring data points, but there is no true zero point.
The difference between any two adjacent temperatures is the same: one degree. But zero degrees is defined differently depending on the scale – it doesn’t mean an absolute absence of temperature.
The same is true for test scores and personality inventories. A zero on a test is arbitrary; it does not mean that the test-taker has an absolute lack of the trait being measured.
|Ratio level||Examples of ratio scales|
|You can categorize, rank, and infer equal intervals between neighboring data points, and there is a true zero point.
A true zero means there is an absence of the variable of interest. In ratio scales, zero does mean an absolute lack of the variable.
For example, in the Kelvin temperature scale, there are no negative degrees of temperature – zero means an absolute lack of thermal energy.
Why are levels of measurement important?
The level at which you measure a variable determines how you can analyze your data.
The different levels limit which descriptive statistics you can use to get an overall summary of your data, and which type of inferential statistics you can perform on your data to support or refute your hypothesis.
In many cases, your variables can be measured at different levels, so you have to choose the level of measurement you will use before data collection begins.