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Statistical measurements require variables, but all variables are not the same. Some variables like weight or speed or dollars spent can be precisely measured. Opinions, though, are a different matter. Patients can rate their level of pain on a scale of one to ten, or movie-goers can rate how well they enjoyed a film they just saw. These types of indicators are ordinal measurements. They are not precise the way physical or economic measures can be, but ordinal measures can nevertheless provide valuable information for researchers.

#### TL;DR (Too Long; Didn't Read)

Ordinal measures generally refer to surveys, where user opinion is being quantified.

## Categorical and Interval Variables

The different statistical variables include categorical, interval, ratio and ordinal variables. Categorical variables refer to types without order. Birds, mammals, reptiles and fish are types that can be named but have no mathematical order in relation to one another. Interval variables are variables that relate equally along a common scale; for example, temperature changes, where the difference between 50 and 60 degrees is the same as the difference between 60 and 70 degrees -- 10 degrees.

## Ratio and Ordinal Variables

Ratio variables begin with zero representing equality between two things, and proceed to factors representing relative difference. Comparing the population of China to the United States, a ratio variable might take the United States as the zero-base with 311 million people, which gives China, with 1.3 billion people, a ratio value of 4.29. China has 4.29 as many people as the United States. Ordinal variables measure qualities; for example, a survey might say, “With your current governor, you are: (1) very unsatisfied, (2) unsatisfied, (3) have no opinion, (4) satisfied or (5) very satisfied.”

## Conclusions

Ordinal measurement is designed to infer conclusions, while other methods are used to describe conclusions. Descriptive conclusions organize measurable facts in a way that they can be summarized. If a statistical analysis of average per capita income in a town changes over three years, that change can be stated quantitatively. No inference, however, can be drawn about why the average changed. What you see is what you get: numbers. Inferential conclusions attempt to see beyond the actual numbers to some qualitative conclusion, for example, "Most customers of Frosty Boy Ice Cream are satisfied."