What is Ordinal Data?

Ordinal data is data that can be ranked in order, but where the difference between two values does not necessarily mean the same thing. For example, if you asked people to rank their favorite pizza toppings, the data would be ordinal because the toppings could be ranked in order (from least favorite to most favorite), but the difference between two toppings does not necessarily mean the same thing. For example, someone who ranks pepperoni as their favorite topping might only like it a little more than someone who ranks sausage as their favorite topping.

Examples of Ordinal Data

Ordinal data is common in many different fields, including:

• Social sciences: Ordinal data is often used in surveys and questionnaires to measure people's attitudes, opinions, and preferences.
• Natural sciences: Ordinal data can be used to measure the strength of earthquakes, the intensity of hurricanes, and the speed of rivers.
• Business: Ordinal data can be used to measure customer satisfaction, employee engagement, and product quality.

Examples of ordinal data include:

• Rating scales (e.g., 1-5 stars)
• Likert scales (e.g., strongly disagree to strongly agree)
• Satisfaction scores (e.g., very dissatisfied to very satisfied)
• Importance rankings (e.g., not important to very important)

How to Analyze Ordinal Data

There are a number of different ways to analyze ordinal data. Some common methods include:

• Descriptive statistics: Descriptive statistics, such as the mean, median, and mode, can be used to describe the distribution of ordinal data.
• Non-parametric statistics: Non-parametric statistics, such as the Wilcoxon signed rank test and the Kruskal-Wallis test, can be used to compare two or more groups of ordinal data.
• Regression analysis: Regression analysis can be used to model the relationship between a dependent variable (ordinal data) and an independent variable (categorical or continuous data).

Tips for Analyzing Ordinal Data

Here are a few tips for analyzing ordinal data:

• Use reputable survey analysis software like mTab. There are a number of different software programs that can be used to analyze ordinal data, mTab has been a leading platform for analyzing ordinal data for over 20 years.
• Be aware of the assumptions of the statistical methods you are using. Some statistical methods, such as the Wilcoxon signed rank test, make assumptions about the distribution of the data.
• Interpret your results carefully. The results of your analysis can be used to make predictions, but it is important to remember that these predictions are based on the data you have collected.

Conclusion

Ordinal data is a type of data in which the values are ranked in a specific order, but the differences between them are not equal or measurable. It differs from other types of data such as nominal, interval, and ratio data. Ordinal data is commonly used in social sciences and business research to measure variables such as satisfaction, quality, or preference. It can be analyzed using descriptive statistics and non-parametric tests, which are robust to violations of assumptions and suitable for analyzing ranked data. Understanding ordinal data is important for researchers and decision-makers to make informed decisions based on the data.