## What Students Can Learn by Analyzing Categorical Data

Data is a gathered group of facts in mathematical and statistical analysis. In this case, information may refer to anything that could be utilized to prove or deny a scientific hypothesis during an experiment. Age, a person's opinion, name, hair color, type of pet, and other information may be collected by learners during research. Although there are no restrictions on the format in which this data can be presented, it is divided into two groups based on its nature: categorical and numerical data. Categorical data, as the name suggests, is frequently divided into one or more categories. Numerical data, on the other hand, deals with numerical variables, as the name implies.

**Definition of Categorical Data**

Categorical data is a collection of data that has been separated into categories. For example, if a company or government agency attempts to obtain bio data about its personnel, the resulting data is categorical. Because the factors exist in the biodata, such as sex, state of residence, among others, this data is referred to be categorical. Categorical data can have numerical values (for example, “1” for Yes and “2” for No or another number for a different response), but those numbers have no mathematical significance. They cannot be added to each other or subtracted from each other.

**Application in Surveys**

Nominal data is a subclass of categorical data used to name variables without providing a numerical value. “Labeled” or “named” data is another term for nominal data. Name, hair color, and sex are examples of nominal data. This data type, which is mostly acquired through surveys or questionnaires, is descriptive since it sometimes allows respondents to type in their responses to the categorical data questions, thus helping to develop superior conclusions. Ordinal data is a sort of data that has a predetermined order or scale. However, there is no standard scale on which the differences in variables in each scale can be quantified in this sequence. Although it is most commonly classed as categorical data, it is somewhere in the middle. Its categorical data classification stems from the fact that it exhibits more categorical data characteristics. Likert scales, interval scales, bug severity, and customer satisfaction survey data are all instances of ordinal data. Although the collecting and analysis methodologies for each of these cases vary, they are all ordinal data.

**Conclusion**

The contrast between categorical and quantitative variables is critical when determining which sorts of data analysis methodologies to use. Understanding categorical data is the first step toward choosing the best data analysis approach today. Descriptive statistics, linear regression models, time series, and other methods are used to examine quantitative data. Only graphical and descriptive methods are normally employed with categorical data.