Scales of measurement refer to the different ways variables can be categorized, ranked, or quantified, shaping how researchers analyze data.
What Are Scales of Measurement?
In social science research, scales of measurement—or levels of measurement—describe how data are classified and interpreted. These levels determine what kind of statistical analysis is appropriate and how researchers can compare or manipulate variables. There are four widely accepted scales: nominal, ordinal, interval, and ratio. Each level adds complexity and offers more mathematical possibilities than the one before it.
Researchers must understand these scales to make informed decisions about research design, data collection, and data analysis. The level of measurement chosen affects everything from which questions can be asked to which statistical tests can be used.
Why Scales of Measurement Matter
Knowing the level of measurement helps ensure that researchers analyze their data appropriately. Using the wrong statistical method for a given scale can lead to invalid or misleading results.
For example, imagine a sociologist using survey data to measure job satisfaction. If job satisfaction is measured on an ordinal scale (like “very dissatisfied” to “very satisfied”), calculating an average score may not make sense. On the other hand, if the scale is interval (like a 0–10 rating), averages and standard deviations are more appropriate.
Each scale provides different information about the variable, and understanding the distinctions helps researchers use their data correctly.
The Four Scales of Measurement
Nominal Scale
The nominal scale is the most basic level. It classifies data into categories that do not have a specific order or rank. Each value is simply a label or name. Examples include gender, race, religion, or political party affiliation.
In this scale, numbers may be assigned to categories for convenience, but these numbers do not represent any order or quantity. For example, coding “Male” as 1 and “Female” as 2 does not mean Female is greater than Male.
Examples from social sciences:
- A political scientist coding survey responses for party identification (1 = Democrat, 2 = Republican, 3 = Independent).
- An anthropologist categorizing cultures based on subsistence strategy (e.g., hunter-gatherer, agricultural, industrial).
Key features:
- Categories are mutually exclusive.
- No inherent order.
- Mode is the only meaningful measure of central tendency.
Ordinal Scale
The ordinal scale provides information about order or rank but does not specify the exact difference between values. Variables measured on an ordinal scale can be arranged in a meaningful sequence, but the intervals between them are not necessarily equal.
For example, a Likert scale asking respondents to rate agreement from “Strongly Disagree” to “Strongly Agree” uses ordinal data. While we know that “Agree” is more favorable than “Neutral,” we can’t say the difference between “Agree” and “Neutral” is the same as between “Agree” and “Strongly Agree.”
Examples from social sciences:
- A criminologist ranking neighborhoods by perceived safety (1 = low safety, 5 = high safety).
- An education researcher using letter grades (A, B, C, etc.) to assess performance.
Key features:
- Categories are ordered.
- Intervals between categories are not equal.
- Median and mode are meaningful; mean is not recommended.
Interval Scale
The interval scale offers both order and equal spacing between values. However, it lacks a true zero point, meaning zero does not represent a complete absence of the variable.
Temperature in Celsius is a classic example. The difference between 20 and 30 degrees is the same as between 30 and 40, but 0 degrees does not mean “no temperature.”
In the social sciences, interval scales are less common, but examples can include IQ scores or standardized test scores, assuming the test has been designed to produce equal intervals.
Examples from social sciences:
- A psychologist using IQ scores to compare cognitive ability.
- An education researcher using standardized reading scores on a scale from 200 to 800.
Key features:
- Equal intervals between values.
- No true zero.
- Mean, median, and standard deviation can be calculated.
Ratio Scale
The ratio scale includes all the properties of an interval scale, plus a meaningful zero point. With a true zero, researchers can make statements about how many times greater one value is than another. Ratio scales support the widest range of mathematical operations.
Common examples include income, age, number of children, and years of education. These variables can be added, subtracted, multiplied, and divided.
Examples from social sciences:
- A sociologist analyzing household income levels.
- A public health researcher measuring the number of visits to a clinic.
Key features:
- Ordered categories with equal intervals.
- True zero point.
- All mathematical operations are valid.
How Levels of Measurement Affect Data Analysis
The level of measurement influences which statistical tests are valid. Using techniques meant for interval or ratio data on nominal or ordinal variables can lead to faulty conclusions.
Descriptive Statistics
- Nominal: Frequencies, percentages, mode.
- Ordinal: Median, range, mode.
- Interval and Ratio: Mean, median, mode, standard deviation, variance.
Inferential Statistics
- Nominal: Chi-square tests.
- Ordinal: Mann-Whitney U, Kruskal-Wallis, Spearman’s rho.
- Interval and Ratio: t-tests, ANOVA, correlation, regression.
Using the correct test protects the integrity of the analysis. For example, applying a Pearson correlation to ordinal data could misrepresent the strength of the relationship. In contrast, using Spearman’s rho—a rank-order correlation—provides a more accurate estimate.
Examples Across Social Science Disciplines
Sociology
A sociologist studying social class might use:
- Nominal for the occupation category.
- Ordinal for social status rankings.
- Ratio for household income.
Psychology
A psychologist studying anxiety might use:
- Ordinal for self-reported anxiety levels on a scale of 1 to 5.
- Interval for scores on a standardized anxiety test.
- Ratio for number of panic attacks in a month.
Education
An education researcher studying student achievement might use:
- Nominal for school type (public, private, charter).
- Ordinal for teacher ratings.
- Interval for test scores.
- Ratio for number of days attended.
Political Science
A political scientist studying voter behavior might use:
- Nominal for party affiliation.
- Ordinal for strength of party identification.
- Ratio for number of times voted in the past decade.
Criminology
A criminologist examining criminal behavior might use:
- Nominal for crime type.
- Ordinal for severity of offense.
- Ratio for number of prior arrests.
Challenges and Considerations
Misclassifying Variables
Researchers sometimes treat ordinal variables as interval to apply more advanced statistics. For example, treating Likert-scale items (e.g., 1 to 5) as interval allows for calculations like means and standard deviations. While this is common, it should be done cautiously and with justification, especially when presenting findings.
Data Transformation
Sometimes researchers convert continuous variables into categories (e.g., grouping ages into ranges). While this simplifies analysis, it can reduce precision and statistical power.
Measurement Validity
Choosing the right scale also relates to validity. If a researcher uses a nominal scale when an ordinal or interval scale would be better, the measure may not capture the variable accurately.
Conclusion
Scales of measurement are fundamental to the research process. They guide how data are collected, analyzed, and interpreted. From simple categories to complex numerical values, understanding these levels ensures that researchers choose the right tools and draw valid conclusions. By matching statistical methods to the scale of measurement, social scientists produce research that is both accurate and meaningful.
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Last Modified: 03/27/2025