When identifying variables, an important characteristic to understand is the variable level of measurement. The level of measurement describes how specific of a value the measurement is. One of the practical implications of understanding variable level of measurement is that statistical tests assume a particular level of measurement from the variable. For example, an independent-samples t-test requires the independent variable to be a categorical variable, while the dependent variable must be a continuous variable. Using the correct level of measure will allow you to be successful in your statistical test.
| Categorical
Variables that take categories as their values, such as yes, no, blue, male, female, freshman, etc. |
Continuous
Variables that have values that represent a counted or measured quality |
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| Nominal | Ordinal | Interval | Ratio |
| A nominal scale consists of a set of categories that have different names. Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations. E.g., teacher, principal, White, Hispanic, Black, etc. | An ordinal scale consists of a set of categories that are organized in an ordered sequence. Measurements on an ordinal scale rank observations in terms of size or magnitude. E.g., freshman, sophomore, junior, senior. Likert scale items. | An interval scale consists of ordered categories that are all intervals of exactly the same size. Equal differences between numbers on a scale reflect equal differences in magnitude. However, the zero point on an interval scale is arbitrary and does not indicate a zero amount of the variable being measured. E.g., Likert scale means or sums. | A ratio scale is an interval scale with the additional feature of an absolute zero point. With a ratio scale, ratios of numbers do reflect the magnitude. E.g., Reading scores, height. |
