When you have categorical data with a natural order or preference, you can call that ordinal data. When you describe data, you must first distinguish whether you are talking about numerical or categorical data. This is important for the proper selection and use of statistical and analytical tools.

Categorical data is a type of data that consists of distinct categories or groups. It is often represented by labels or names, rather than numerical values. Categorical data can be further classified into nominal data and ordinal data.

Nominal data is a type of categorical data where the categories have no inherent order or ranking. Examples of nominal data include gender, eye color, or favorite color. Ordinal data is a type of categorical data where the categories have a natural order or ranking such as strongly agree, agree, neutral, disagree, and strongly disagree.

Overview: What is ordinal data? 

Ordinal data is a type of categorical data that has a natural order or ranking between its categories. This means that the categories can be arranged in a specific order, such as from the lowest to the highest or from the smallest to the largest. 

Examples of ordinal data include survey responses such as “strongly agree,” “agree,” “neutral,” “disagree,” and “strongly disagree,” or educational levels such as “elementary school,” “high school,” “college,” and “graduate school.” Ordinal data is different from nominal data, which does not have a natural order or ranking between its categories.

An industry example of ordinal data 

The Human Resources (HR) department was interested in the percentage turnover as a function of organizational structure. They defined the turnover data by function (sales, manufacturing, finance, QC etc.) as being nominal data since there is no logical sequencing or order to organizational departments. On the other hand, they also analyzed the data by organization structure. That data was ordinal since there is a rational order to look at turnover for supervisors, managers, directors, vice presidents and executive vice presidents.

Frequently Asked Questions (FAQ) about ordinal data

Here are some frequently asked questions about ordinal data:

What is the difference between ordinal data and nominal data?

Ordinal data and nominal data are both types of categorical data, but ordinal data has a natural order or ranking between its categories, whereas nominal data does not.

What is the best way to represent ordinal data?

Ordinal data is often represented using a bar chart, histogram, or frequency plot. It is important to maintain the natural order of the categories when creating these graphical tools.

How do you analyze ordinal data?

Ordinal data can be analyzed using nonparametric statistical tests such as the Mann-Whitney U test or the Kruskal-Wallis test. These tests do not assume a normal distribution of the data.

Can ordinal data be converted into numerical data?

Ordinal data can be converted into numerical data by assigning numerical values to each category. However, this can lead to loss of information about the natural order of the categories.

Is it appropriate to calculate the mean for ordinal data?

Calculating the mean for ordinal data is not appropriate because the categories do not have a fixed numerical value. Instead, measures of central tendency such as the median or mode can be used.

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