## Levels and scales of measurement in statistics static electricity how it works

Common examples within sociology include the nominal tracking of sex (male or female), race (white, Black, Hispanic, Asian, American Indian, etc.), and class (poor, working class, middle class, upper class). Of course, there are many other variables one can measure on a nominal scale.

The nominal level of measurement is also known as a categorical measure and is considered qualitative in nature. When doing statistical research and using this level of measurement, one would use the mode, or the most commonly occurring value, as a measure of central tendency. The Ordinal Level and Scale

Ordinal scales are used when a researcher wants to measure something that is not easily quantified, like feelings or opinions. Within such a scale the different values for a variable are progressively ordered, which is what makes the scale useful and informative. It satisfies both the properties of identity and of magnitude. However, it is important to note that as such a scale is not quantifiable—the precise differences between the **variable categories** are unknowable.

For example, if a researcher wants to measure the extent to which a population believes that racism is a problem, they could ask a question like "How big a problem is racism in our society today?" and provide the following response options: "it’s a big problem," "it is somewhat a problem," "it is a small problem," and "racism is not a problem."

Unlike nominal and ordinal scales, an **interval scale** is a numeric one that allows for ordering of variables and provides a precise, quantifiable understanding of the differences between them (the intervals between them). This means that it satisfies the three properties of identity, magnitude, and equal intervals.

One can also turn non-interval, ordered **variable categories** into an *interval scale* to aid statistical analysis. For example, it is common to measure income as a range, like $0-$9,999; $10,000-$19,999; $20,000-$29,000, and so on. These ranges can be turned into intervals that reflect the increasing level of income, by using 1 to signal the lowest category, 2 the next, then 3, etc.

Interval scales are especially useful because they not only allow for measuring the frequency and percentage of **variable categories** within our data, they also allow us to calculate the mean, in addition to the median, mode. Importantly, with the interval level of measurement, one can also calculate the standard deviation. The Ratio Level and Scale

A sociologist would use a *ratio scale* to measure actual earned income in a given year, not divided into categorical ranges, but ranging from $0 upward. Anything that can be measured from absolute zero can be measured with a **ratio scale**, like for example the number of children a person has, the number of elections a person has voted in, or the number of friends who are of a race different from the respondent.

One can run all the statistical operations as can be done with the **interval scale**, and even more with the *ratio scale*. In fact, it is so called because one can create ratios and fractions from the data when one uses a ratio level of measurement and scale.