Ordinal Data vs Interval Data

Both ordinal and interval data are two of the four main data types or classifications used in statistics and other related fields. Both data types allow the need to classify and express information.

Both ordinal data and interval data are also a unit of measurement for data quantities. By depicting the data on a scale, both types of data point out to a description of comparison and contrasts within the scale.

The differences between the two data types are as follows:

Ordinal data are characterized with a natural and clear ordering, ranking, or sequence in a scale. Also, the ordinal data are not concerned with certainty or equality between two values. The emphasis is on the position of the value.

Ordinal data have a defined category, and their scale is described as not uniform. Their main use is to describe data in order or rank form based on a particular scale of attributes.

Ordinal data can be expressed in various forms and with words like:

first, second, third
beginning, middle, end
one, two, three and so on…
A, B, C and so on…
1, 2, 3 and so on…
Low, medium, or high

An excellent example would also be the Likert scale with values ranging from one to ten. Besides forming the order or ranking, there is no further information aside from direction and organization that can be derived from this type of data. Any relationships between values are also not uniform or inconsistent compared to the interval data. There is also no identifying factor or distance between two variables.

Ordinal data are a form of non-parametric data which are a type of data that do not assume any particular pattern of distribution or predictability. Nominal data are also a form of non-parametric data.

It is a form of parametric data, along with ratio data. As a form of parametric data, the distribution within the scale of this type of data are predictable.

On the other hand, interval data have an emphasis on the differences between two consecutive values on a given scale. The in-between value has an equal split or even difference in a scale. The difference between two values can be easily seen and can be characterized as uniform and consistent intervals within each interval.

Interval data is often used in psychological experiments and cannot be subject to mathematical operations of multiplication or division.

Compared to ordinal data, interval data have more meaningful and a continuous scale of measurement. They also contain more quantitative information compared to ordinal data.
This type of data features a uniform scale.

Interval data are a form of parametric data along with ratio data. As a form of parametric data, the distribution within the scale of this type of data is predictable and distinguishable.

Summary:

1.Ordinal data are most concerned about the order and ranking while interval data are concerned about the differences of value within two consecutive values.
2.Ordinal data place an emphasis on the position on a scale while interval data are on the value differences of two values in a scale.
3.There is no certainty of equality in ordinal data while there is a presence of equality in interval data.
4.The scale and value of differences in an ordinal sequence is not uniform while the two factors in interval data are uniform.
5.Interval data are considered more informative kinds of quantitative data compared to ordinal data.
6.Interval data are a form of parametric data while ordinal data are a form of non-parametric data.
7.Interval data can also be placed in an ordinal manner.

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