Levels of measurement, also known as scales of measurement or data types, refer to the different ways in which variables can be classified and measured. There are four commonly recognized levels of measurement:
1. Nominal Level: The nominal level of measurement is the lowest level of measurement. Variables at this level are categorical and can be classified into distinct categories or groups. However, the categories have no inherent order or magnitude. Examples include gender (male/female), marital status (single/married/divorced), or types of cars (sedan/SUV/truck). In nominal measurement, variables are typically represented by labels or codes.
2. Ordinal Level: The ordinal level of measurement introduces a rank or order among the categories of a variable. While the categories are still distinct, they now have a relative position or order. However, the differences between categories are not necessarily equal or quantifiable. Examples of ordinal variables include rankings (1st place, 2nd place, 3rd place), Likert scale ratings (e.g., strongly agree, agree, neutral, disagree, strongly disagree), or educational levels (elementary, high school, bachelor's degree, master's degree).
3. Interval Level: The interval level of measurement not only has distinct categories and an order but also provides equal intervals between the categories. However, it does not have a true zero point. Interval variables allow for comparisons of the differences or intervals between values, but they do not permit meaningful ratio calculations. Examples include temperature on the Celsius or Fahrenheit scales (0°C doesn't represent the complete absence of temperature) or calendar dates (the difference between two dates is meaningful, but the concept of a "zero" date is arbitrary).
4. Ratio Level: The ratio level of measurement has all the properties of the interval level but also includes a true zero point, indicating the absence of the variable being measured. Variables at the ratio level allow for meaningful ratios and calculations. Examples include height, weight, time, or income. A value of zero in these variables represents a complete absence of the attribute being measured.
These levels of measurement form a hierarchy, with each level including the characteristics of the preceding levels. For example, a ratio variable is also nominal, ordinal, and interval.
Understanding the level of measurement is essential for selecting appropriate statistical analyses and determining the appropriate descriptive statistics and graphical representations for the data. The choice of measurement level guides the selection of appropriate statistical tests and determines the type of mathematical operations that can be performed on the data.
