1.7:
Ratio Level of Measurement
Data is classified into four levels of measurement.
The ratio level of measurement is an extension of the interval level of measurement. It deals with data that have a natural zero point. The difference between the values, and the ratio of values, both are meaningful in the ratio level of measurement.
For instance, a statistics book may cost 50 dollars and a comic book 5 dollars. This is countable data, can be ordered as expensive or cheap, and the difference between the values is also meaningful. The statistics book costs 45 dollars more than the comic book.
It also has a natural zero point – zero dollar means no cost.
Additionally, the ratio between the values is also significant – the price of the statistics book is ten times that of the comic book.
1.7:
Ratio Level of Measurement
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For example, four Statistics final exam scores are 80, 68, 20, and 92 (out of a possible 100 points). The exams are machine-graded. The data can be ordered from lowest to highest: 20, 68, 80, 92. The differences between the data values hold meaning. The score of 92 is greater than 68 by 24 points. Therefore, ratios can be calculated. For example, 80 is four times 20. Thus, a score of 80 is four times better than the score of 20. Also, the smallest possible score is 0.
In summary, the ratio level of measurement is an extension of the interval level of measurement. It includes data with a natural zero point, and the difference between the values and the ratio of values is meaningful.
This text is adapted from Openstax, Introductory Statistics, Section 1.2 Data, Sampling, and Variation in Data and Sampling