Title: Ratio
Course: UGQuants (SSPS) > Section: Introduction to Quantitative Analysis

Ratio

With a ratio level of measurement, the values of a variable not only have a logical order to them in which each value can be described as higher and/or lower than another: unlike ordinal variables, we can also say that the unit difference between any pair of adjacent values is equal and that the values do have a meaningful arithmetic relationship to one another.

One sometimes finds the terms ‘ratio’ and ‘interval’ used interchangeably, but strictly speaking they are different. To resolve any confusion it might help to think of ratio variables as a particular category of interval variable, but it is simpler to introduce the ratio level of measurement first. This is because in social and political research we rarely encounter variables that may be measured at an interval level but do not also meet the criteria for the ratio level of measurement.

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Examples of the ratio level of measurement are age, income or fertility. For a case that takes the value 60 for age, we are able to say not only that this case may be ranked higher in terms of age than cases with the value 20 and 40, but also that the difference between cases with values of 60 and 40 is the same as for cases with values of 40 and 20, and that a case with a value of 60 is ‘three times as old’ (at least in full years) as a case with a value of 20. The same is true for different levels of individual income, or for fertility, measured e.g. by the average total number of children born to each woman in different countries of the world.

Variables in this category also tend to have another property: they are often simpler to deal with in terms of data collection and processing. For example, for age in years we do not need to devise a complex question setting out of all the possible options: we can merely ask an individual to tell us their age last birthday. And, unlike nominal and ordinal variables, we do not need to devise a series of numerical values that represent different value labels, because the numerical value, e.g. for age, is meaningful in itself.