Levels of Measurement

Before analyzing a dataset, it is crucial to identify the type of data it contains. Luckily, all data can be grouped into one of four categories: nominal, ordinal, interval, or ratio data. Although these are often referred to as “data types,” they are actually different levels of measurement. The level of measurement reflects the accuracy with which a variable has been quantified, and it determines the methods that can be used to extract insights from the data.

The four categories of data are not always straightforward to distinguish and instead belong to a hierarchy, with each level building on the preceding one.

There are four types of data: categorical, which can be further divided into nominal and ordinal, and numerical, which can be further divided into interval and ratio. The nominal and ordinal scales are relatively imprecise, which makes them easier to analyze, but they offer less accurate insights. On the other hand, the interval and ratio scales are more complex and difficult to analyze, but they have the potential to provide much richer insights.

• Nominal Data – Nominal data is a basic data type that categorizes data by labeling or naming values such as Gender, hair color, or types of animal. It does not have any hierarchy.
• Ordinal Data – Ordinal data involves classifying data based on rank, such as social status in categories like ‘wealthy’, ‘middle income’, or ‘poor’. However, there are no set intervals between these categories.
• Interval Data – Interval data is a way of organizing and comparing data that includes measured intervals. Temperature scales, like Celsius or Fahrenheit, are good examples of interval data. However, interval data doesn’t have a true zero, meaning that a measurement of “zero” can still represent a quantifiable measure (like zero degrees Celsius, which is just another point on the scale and doesn’t actually mean there is no temperature present).
• Ratio Data – The most intricate level of measurement is ratio data. Similar to interval data, it categorizes and arranges data, utilizing measured intervals. But, unlike interval data, ratio data includes a genuine zero. When a variable is zero, there is no presence of that variable. A prime illustration of ratio data is height measurement, which cannot be negative.

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.