When it comes to research, data is the foundation of any successful project. But what types of data are there? In this article, we'll explore the different types of data and when to use them. Qualitative and quantitative data are the two main types of data. Quantitative data is numerical and can be measured, while qualitative data is non-numerical and cannot be measured.
Quantitative data can be further divided into discrete and continuous data. Discrete data is a count that involves only whole numbers, while continuous data can be broken down into more precise levels. Nominal data is used only to label variables, without any type of quantitative value. Ordinal data shows where a number is in order.
Data can take many forms, from numerical values to images and sound recordings. Surveys remain one of the most effective types of core data that provide feedback directly from consumers. A good rule of thumb for defining whether a data is continuous or discrete is that if the measurement point can be halved and still makes sense, the data is continuous. If you are a businessman, marketer, data scientist, or other professional working with some types of data, you should be familiar with the key list of data types. Quantitative and qualitative data provide valuable information and do not conflict with each other.
The best way to combine qualitative and quantitative data in surveys is to include both multiple-choice and open-ended questions. In short, understanding the different types of data and when to use them is essential for any successful research project. Data variables can't be divided into smaller parts, so it's important to use the same type of currency for all values in the study. Working in the area of data management and having a good range of data science skills involves a deep understanding of various types of data and when to apply them.