When will you use Nominal, Ordinal, Interval or Ratio statistics?
Each scale of measurement is used depending on the nature of the data you are dealing with:
- Nominal: This is the simplest level of measurement. Variables at the nominal level are categorized into mutually exclusive groups that have no order or structure. Examples include gender, nationality, eye color, etc.
- Ordinal: This level of measurement represents variables that can be ranked. The distances between each interval on the scale, however, are not necessarily equal. For instance, a customer satisfaction survey rating from 1-5 would be an ordinal measurement.
- Interval: This level of measurement represents variables that can be ranked, and also have equal distances between intervals. However, there is no absolute zero point. An example of interval measurement would be temperature in degrees Celsius or Fahrenheit.
- Ratio: This is the highest level of measurement and includes variables that can be ranked, have equal distances between intervals, and have a true zero point. Examples include height, weight, age, etc.
What are the benefits of NOIR statistics?
Every measurement level within the NOIR statistics framework offers distinct benefits:
- Nominal: These data are straightforward to collect and analyze, making them suitable for simple analyses and broad comparisons. They’re great for grouping data into distinct categories and allow for frequency and mode analyses.
- Ordinal: These data maintain simplicity while providing an extra layer of detail over nominal data, i.e., the order. This allows us to conduct more sophisticated analyses, such as calculating medians or percentiles.
- Interval: Interval data offer even more analytical possibilities. In addition to order, they provide information about the differences between values, allowing for computations involving addition and subtraction, such as computing the mean or standard deviation.
- Ratio: Ratio data provide the most detail. They allow for a full range of mathematical and statistical operations because they have a meaningful zero point. Ratio data allow for a deep understanding of a distribution’s characteristics, making them ideal for complex statistical analyses like regression, correlation, or t-tests.
“NOIR” is an acronym representing the four statistical measurement scales: Nominal, Ordinal, Interval, and Ratio. Each provides a different method for classifying variables in statistical analysis.
The appropriate measure of central tendency for data depends on whether the data is Nominal, Ordinal, Interval, or Ratio:
- Nominal: The mode, or the most frequently occurring value, is used.
- Ordinal: The median, which is the middle value in a set of ordered data, is used.
- Interval: The mean (average), median, and mode can all be used, but the choice depends on the data distribution.
- Ratio: Like interval data, the mean, median, and mode are all suitable. However, because ratio data has a true zero, it allows for a wider range of statistical tests.