Ordinal scale

Definition

An ordinal scale is a type of measurement scale that ranks variables in a specific order but does not establish the exact distance between the ranks. This means that while you can tell which values are higher or lower, you can't quantify how much higher or lower they are. Ordinal scales are commonly used in surveys and assessments where preferences or rankings are important, allowing for a clear understanding of relative positions without precise numerical differences.

5 Must Know Facts For Your Next Test

1. Ordinal scales can be applied in various fields such as psychology, market research, and education to assess preferences or levels of satisfaction.
2. Examples of ordinal scales include Likert scales used in surveys to measure attitudes, where respondents rank their agreement from 'strongly disagree' to 'strongly agree.'
3. While ordinal data can be analyzed with non-parametric statistics, it limits the types of analyses that can be performed compared to interval and ratio data.
4. In an ordinal scale, the difference between ranks is not uniform; for instance, the difference between ranks 1 and 2 may not be the same as between ranks 2 and 3.
5. Ordinal scales provide valuable information about order but should be used cautiously when interpreting distances or making assumptions about equal intervals.

Review Questions

• How does an ordinal scale differ from nominal and interval scales in terms of measurement capabilities?
  • An ordinal scale differs from a nominal scale in that it provides a ranking order among values, whereas a nominal scale categorizes data without any order. In contrast to an interval scale, which offers both order and equal distances between values, an ordinal scale does not guarantee equal spacing between ranks. Therefore, while ordinal scales indicate relative positions of items, they lack precise numerical measurement compared to interval scales.
• Discuss how ordinal scales can be applied in survey research and what limitations they might present.
  • Ordinal scales are often used in survey research through formats like Likert scales, which allow respondents to express levels of agreement or satisfaction on a ranked basis. However, the limitations of ordinal scales arise from their inability to quantify the exact differences between ranks. For instance, if one person rates their satisfaction as a '4' and another as a '2,' we cannot assume that the first person's satisfaction is exactly twice as much as the second's. This lack of precise measurement can complicate data analysis and interpretation.
• Evaluate the implications of using ordinal scales for data analysis compared to other measurement scales.
  • Using ordinal scales for data analysis can have significant implications due to their unique properties. While they allow for ranking and provide insights into preferences or behaviors, the non-uniform differences between ranks limit statistical methods that can be employed. For example, applying mean calculations on ordinal data would be inappropriate since we cannot infer equal intervals. Analysts must carefully consider these limitations when drawing conclusions from ordinal data and may need to resort to non-parametric methods that respect the ranking nature without assuming equal spacing.

Related terms

Nominal scale: A nominal scale is a measurement scale that categorizes data without any order or ranking among the categories. Examples include gender, race, or types of fruit.

Interval scale: An interval scale is a measurement scale that not only ranks variables but also establishes equal distances between values. Temperature measured in Celsius or Fahrenheit is an example.

Ratio scale: A ratio scale is the highest level of measurement that includes all the properties of an interval scale, but also has a true zero point. Examples include height, weight, and age.