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.
Ordinal Data vs Interval Data
The main difference between ordinal data and interval data is that ordinal data does not have a uniform scale or sequence, whereas interval data has a very uniform scale which means that the difference between any two values remains the same. Ordinal data is non-parametric, whereas interval data is parametric data.
Ordinal data is identified by a distinct and natural ordering, ranking, or succession on a scale. Furthermore, ordinal data do not care about the certainty or equality of two values. The value’s position is emphasized. It is a non-parametric data type. It also tends to offer less information than interval data.
In contrast to ordinal data, interval data offers a more meaningful and continuous scale of measurement. They also give more quantitative information than ordinal data. It is numerical data which means that it represents the quantifiable quantities of something. It provides a certain amount of variation between any two values. We can add or subtract the values of interval data in a meaningful way.
Comparison Table Between Ordinal Data and Interval Data
|Parameters of Comparison||Ordinal Data||Interval Data|
|Concern||More concerned about order and ranking.||More concerned about the difference between two values.|
|Equality||No certainty of equal intervals.||There are equal intervals.|
|Types of Data||Non-parametric data||Parametric data|
|Uniformity||The sequence and scale are not uniform.||The scale is uniform.|
|Information||Reveals less information||Reveals more information|
What is Ordinal Data?
Ordinal data is a scale-based organization of data. For example, variable X may be the number of days participants were given a specific diet, and variable Y could be the ranking of these people in a race. It is feasible to correlate the influence of variable X on variable Y in such data.
Ordinal data is based on a rating system. For example, in a 100-meter race, the winner may take 11 seconds, the second-place holder 11.5 seconds, and the third-place holder 12.5 seconds. Because the time gap between rankings is not defined, all you know are the ranks of various persons.
Ordinal data is categorical data which means that it defines various different characteristics of things. It has a natural rank order which means that we can sort the data naturally.
Ordinal data have a specific category and a non-consistent scale. Their major role is to characterize or rate data according to a certain scale of attributes. It is made up of non-parametric data that does not follow any certain distribution or prediction trend.
Some examples of ordinal data can be the different Olympic medals, I.e., bronze, silver, and gold, the letter grading system for test results.
What is Interval Data?
Interval data, often known as an integer, is a data type that is measured along a scale, with each point being put at an equal distance from the others. Interval data is always represented by numbers or numerical values in which the distance between two places is standardized and equal. This sort of data has a uniform scale.
Interval data, as the name indicates, is based on a continuous scale. On a temperature scale, there are values such as 50 and 51 degrees. You are aware that the difference is one degree.
Interval data is concerned with the differences between two consecutive values on a certain scale. The in-between number on a scale has an equal split or even difference. The difference between the two values is obvious, and it may be represented as regular and consistent intervals within each interval.
It is commonly used in psychological research and cannot be subjected to mathematical operations such as multiplication or division.
Interval data, like ratio data, contains parametric data. The distribution of this type of data within the scale is predictable and recognized as a type of parametric data.
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.
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.