What is Ordinal Data?

Ordinal data is a form of qualitative data that classifies variables into descriptive categories. It is characterized by the fact that the categories it employs are ranked on some sort of hierarchical scale, such as from high to low. Ordinal data is the second most complicated type of measurement, following nominal data. Although it is more intricate than nominal data, which lacks any inherent order, it is still relatively simplistic.

For example, Ordinal data is a type of data used to categorize items with a meaningful hierarchy or order. These categories help us to compare and rank different achievements, positions, or performance of students, even if the intervals between them are not equal. Ordinal data is useful for understanding ordered choices or preferences and for assessing relative differences.

• School Grades: Grades like A, B, C are ordinal data, ranked by achievement, but intervals between them vary.
• Education Level: Levels like high school, bachelor’s, master’s are ordinal data, ordered by education, but gaps between levels differ.
• Seniority Level: Job levels like entry, mid, senior are ordinal data, indicating hierarchy, but the gap varies by job and industry.

Characteristics of Ordinal Data

• Ordinal data falls under the category of non-numeric and categorical data, but it can still make use of numerical values as labels.
• Ordinal data are always ranked in a hierarchy (hence the name ‘ordinal’).
• Ordinal data may be ranked, but their values are not evenly distributed.
• With ordinal data, you can calculate frequency distribution, mode, median, and range of variables.

Example

Here are a few examples of how ordinal data is used in fields and domains:

1. Educational Levels: Ordinal data is commonly used to represent education levels, such, as ” school,” “bachelors degree,” “masters degree,” and “Ph.D.” These levels have an order.

2. Customer Satisfaction Ratings: Another application of data is in customer satisfaction surveys. These surveys often ask respondents to rate their experience on a scale, from “poor” to “excellent.”

3. Economic Classes: classes including ” class ” “middle class,” and “upper class ” can be classified as ordinal data based on their ranking.

These examples demonstrate the ways in which ordinal data is utilized across fields and domains.

Data science revolves around the processing and analysis of data utilizing a range of tools and techniques. In today’s data-driven world, we come across types of data each requiring handling and interpretation. It is important to understand different types of data for proper data analysis and statistical interpretation. The type of data determines the proper statistical methods and operations that should be used. Various data types need different analysis and interpretation methods to draw significant conclusions. In this article we will explore the concept of data, and its significance provide real-world examples, and guide you through ways to work with it.