In the mathematical and statistical fields, categorical data is called the group of gathered information. The information can then be anything and can be used to confirm or disprove any scientific experiment or claim. In this article, we will highlight the definition of categorical data from all sides, what it is and what it is for.
Definition of categorical data
As mentioned earlier, categorical data is a selection of information separated by groups. That is, the data that the company and employers are trying to extract from their employees can be considered categorical. That’s because the data are clustered based on the occurrence of the variables in the biodata, such as gender, state of residence, etc.
This type of data can acquire a numerical value, but there is nothing mathematical about it because it would be impossible to calculate or add them together.
Types of categorical data
Data types are divided into two categories, namely named and ordinal data.
Nominal data is data that provides names of some variables, but which do not represent any numerical value. These data received their names from the Latin word nomen, which means name and is a subcategory of categorical data. Examples of such data are height, weight, hair color, name, gender, and more. These types of data can easily be obtained from online surveys or questionnaires because it is descriptive information. And while this characterization may help researchers come to better conclusions, they can run into a problem with a large number of irrelevant responses.
Sequential data are data with a well-defined order or scale. But these data do not have all the standard scales of the study. Of course, this kind of data is characterized by categorical data, but it also has numerical characteristics. Examples of ordinal data include:
- Likert scale
- Interval scale
- Error severity
- Customer satisfaction survey data, etc.
General characteristics / features of categorical data
If we’ve already talked about data subcategories in the previous section, there are still a few aspects that will help give you more information about what is categorical data.
- Quality – categorical data is qualitative because the information about it is provided in words, not numbers
- Analysis – these data are analyzed using mode and median distributions. Nominal data use only one of these distributions, while ordinal data use both. But there are cases where the latter can also be examined using univariate and bivariate statistics, regression applications, linear trends, and classification methods
- Graphic analysis – graphical analysis is done with charts and histograms, and when a histogram is used to analyze the frequency, a pie chart visualizes percentages. Graphical analysis is performed after grouping the data into a table
- Numerical values -We said at the beginning that categorical data is described in words, but sometimes it can be a numerical value, even if you can’t do anything mathematical with it