4.4: Levels of Measurement
- Page ID
- 124461
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Define and provide examples for each of the four levels of measurement.
Levels of Measurement
When researchers measure concepts, they often use the language of variables and attributes. A variable is a grouping of several characteristics, while attributes are the specific characteristics themselves. There are four levels of measurement: nominal, ordinal, interval, and ratio.
1. Nominal Level
At the nominal level, attributes meet the criteria of being exhaustive and mutually exclusive. Nominal-level measures cannot be mathematically quantified; there is no inherent ranking. This is the most basic level of measurement. Examples include relationship status, gender, race, political party affiliation, and religious affiliation.
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Example: To measure relationship status, respondents might be asked if they are "partnered" or "single." These categories are exhaustive (covering all possibilities) and mutually exclusive (an individual cannot be both).
2. Ordinal Level
Attributes at the ordinal level are exhaustive and mutually exclusive, but they are in a rank order. While one can determine if an attribute is "more" or "less" than another, the mathematical distance between those attributes cannot be calculated.
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Example: Social class, degree of support for policy initiatives, or prejudice levels. A researcher can say one person’s support for a public policy is higher than another’s, but they cannot mathematically specify "how much" higher.
3. Interval Level
Measures at the interval level meet all the criteria of the previous levels, with the addition that the distance between attributes is known and equal.
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Example: IQ scores or temperature (Fahrenheit or Celsius). Defining characteristics allow researchers to state exactly how much more or less one attribute differs from another. However, these measures lack a true zero point, meaning one cannot claim that one value is a specific ratio of another (e.g., 50° is not "half as hot" as 100°).
4. Ratio Level
At the ratio level, attributes are exhaustive, mutually exclusive, rank-ordered, have equal distances between them, and possess a true zero point. Because of this zero point, researchers can determine the ratio of one attribute compared to another.
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Example: Age and years of education. A person who is 12 years old is exactly twice as old as someone who is 6 years old because age possesses a meaningful zero.
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Nominal Level: Categorical data that is exhaustive and mutually exclusive but cannot be ranked (e.g., race, gender).
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Ordinal Level: Categorical data that can be ranked, though the distance between ranks is unknown (e.g., social class).
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Interval Level: Quantitative data where the distance between values is known and equal, but lacking a true zero (e.g., temperature).
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Ratio Level: Quantitative data possessing all the characteristics of other levels plus a true zero point (e.g., age, income).