Ordinal and interval data are two of the four main types of data or classifications used in statistics and related fields. Both data formats satisfy the need to categorize and express information. Both types of data are important because they provide users with information that can be utilized to calculate statistics on various aspects.
It has an arbitrary zero point of its scale. This means that there is no way to meaningfully multiply or divide two values or get a ratio.
Main Differences Between Ordinal Data and Interval Data
- Ordinal data is more concerned about the order and ranking of the given data, whereas interval data is more concerned about the difference between two different values.
- In ordinal data, there is no certainty of equal intervals. In interval data, there are equal intervals between all the values.
- Ordinal data consists of non-parametric data, and interval data consists of parametric data.
- In ordinal data, the sequence and scale of the values are not uniform, whereas, in interval data, the scale and sequence of values a very uniform, I.e., the difference between two values remains the same.
- Ordinal data reveals less information, and interval data tends to reveal more information.
Conclusion
Ordinal and interval data are two types of data measuring units. By displaying the data on a scale, both types of data correspond to a description of comparisons and contrasts within the scale.
As a result, the major distinction between ordinal and interval data is that ordinal data’s scale is not uniform, whereas interval data’s scale is uniform. Another contrast is that ordinal data has less information than interval data.
Ordinal data can be used in surveys and questionnaires because it has a very ordered nature. Interval data can be used for statistical research, scientific studies to find out the different probabilities, school grading, etc.
Some examples of ordinal data are movie ratings, military ranks, political ranking, socio-economic status, etc. Some examples of interval data are test scores like SAT, ACT, etc., temperature, IQ tests, age, pH scale, credit score, time throughout the day according to the 12-hour clock, etc. Interval data is also used in scientific data due to its reliability.
References
- https://journals.sagepub.com/doi/abs/10.3102/00346543071001105
- https://www.archives-pmr.org/article/0003-9993(89)90151-2/abstract