Variables

# Ordinal Variable – Definition, Examples

## Ordinal Variable

An ordinal variable is a variable that can be assigned a rank. This rank can be used to determine the order in which the variables are arranged. The most common way to rank ordinal variables is by using numbers, but other methods, such as letters or words, can also be used.

Ranking ordinal variables can be useful when comparing them to other variables. For example, if two variables are both ranked from 1-10, it is easy to see which one is higher or lower on the scale. However, if one variable is ranked from 1-5 and another from 1-10, it may not be as clear which one is more important.

### Ordinal Variable in Research

Ordinal variables are often used in research to study the relationships between different groups of people. For example, researchers may use ordinal variables to compare the income levels of different groups of people.

Ordinal variables are often used in research because they provide a way to measure data that is not easily quantifiable. For example, it is difficult to quantify how happy someone is on a scale of 1-10. However, by using an ordinal variable, researchers can ask people to rate their happiness on a scale of 1-10 and then compare the results.

### Example of Ordinal Variable

An example of the ordinal variable would be: A survey might ask respondents to rate their satisfaction with a product on a scale of 1-5, with 1 being “very dissatisfied” and 5 being “very satisfied.” Ordinal variables are different from numerical variables in that they cannot be meaningfully added or subtracted from one another.

One common example of an ordinal variable is educational attainment. Educational attainment can be measured on a scale of 1-8, with 1 being “no schooling” and 8 being “doctoral degree.” Educational attainment is often used as a proxy for socioeconomic status.

Another common example of an ordinal variable is income. Income can be measured on a scale of 1-5, with 1 being “low income” and 5 being “high income.

### When to use Ordinal Variable

You might use an ordinal variable in situations where you want to be able to say that something is better or worse than something else. For example, you might use an ordinal scale to measure satisfaction with a product on a scale from 1 (very unsatisfied) to 5 (very satisfied).

### Purpose of Ordinal Variable

The purpose of using an ordinal variable is to allow for comparisons to be made between different groups or to track changes over time.

When using an ordinal variable, it is important to keep in mind that the numbers are only relative to each other and do not indicate the actual magnitude of the difference between groups. For example, if Group A has a satisfaction rating of 3 and Group B has a satisfaction rating of 4, it does not necessarily mean that Group B is twice as satisfied as Group A.

There are some advantages to using ordinal variables in research:

• Ordinal variables are easy to understand and interpret. This is because they are often represented by familiar objects, such as numbers or letters.
• It can be used to measure change over time. This is because ordering the items on the scale can show how much change has occurred from one time period to another.
• Ordinal scales provide more information than nominal scales. This is because ordinal data can be placed in an order, whereas nominal data cannot.
• Ordinal data can be used to determine trends and patterns.
• The results of ordinal data can be used in decision-making. This is because ordinal data can be placed in an order, whereas nominal data cannot.

#### Limitations of Ordinal Variable

Some Limitations of Ordinal Variable are:

• Ordinal variables are often affected by measurement error. This can lead to inaccuracies in the data and potentially distort the findings of any analysis.
• It can be affected by changes in how people respond to questions over time. This can make it difficult to compare data from different periods or to track changes over time.
• Ordinal data can be susceptible to biases such as response bias and self-selection bias. These biases can again distort the findings of any analysis and make it difficult to interpret the results.