Another example, a pH of 3 is not twice as acidic as a pH of 6, because pH is not a ratio variable. Clearly, 50 degrees is not twice 41 degrees.

If it were, a conflict would be created because 10 degrees C is 50 degrees F and 5 degrees C is 41 degrees F. However, a temperature of 10 degrees C should not be considered twice as hot as 5 degrees C. For example, because weight is a ratio variable, a weight of 4 grams is twice as heavy as a weight of 2 grams. When working with ratio variables, but not interval variables, the ratio of two measurements has a meaningful interpretation. When the variable equals 0.0, there is none of that variable.Įnzyme activity, dose amount, reaction rate, flow rate, concentration, pulse, weight, length, temperature in Kelvin (0.0 Kelvin really does mean “no heat”), survival time. Temperature (Farenheit), temperature (Celcius), pH, SAT score (200-800), credit score (300-850).Ī ratio variable, has all the properties of an interval variable, and also has a clear definition of 0.0. IntervalĪn interval scale is one where there is order and the difference between two values is meaningful. Make more informed and accurate analysis choices with Prism. For example, the difference between the two income levels “less than 50K” and “50K-100K” does not have the same meaning as the difference between the two income levels “50K-100K” and “over 100K”. Note the differences between adjacent categories do not necessarily have the same meaning. Socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”), satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”). Genotype, blood type, zip code, gender, race, eye color, political partyĪn ordinal scale is one where the order matters but not the difference between values. You can code nominal variables with numbers if you want, but the order is arbitrary and any calculations, such as computing a mean, median, or standard deviation, would be meaningless. Knowing the scale of measurement for a variable is an important aspect in choosing the right statistical analysis.Ī nominal scale describes a variable with categories that do not have a natural order or ranking. These are still widely used today as a way to describe the characteristics of a variable.

In the 1940s, Stanley Smith Stevens introduced four scales of measurement: nominal, ordinal, interval, and ratio.

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