The statistical data that comprises of various categorical variables of data that has been converted into categories. A grouped set of data is one of the best examples to explain it. To add to that, the qualitative data that is accountable or the quantitative data that analyzes groups within the given intervals from which the categorical data definition is derived. These data are usually summarized in the form of a probability table. But, when we analyze this data, we refer to them as categorical data and this is also applied to the data sets. Besides that, while some data sets contain the categorical variables, it may happen sometime that the data set might not contain categorical data.
In statistics, it is vital to notice the different types of categorical data. This happens because statistical methods can be accomplished only with the help of the available data types. When you understand the different data types, it becomes easier for you to understand when you’re supposed to use the right data type. Data is an actual piece of information. You collect data through studies and surveys. You can categorize data into two following groups:
Numerical Data or in other words it is Quantitative Data
Categorical Data or in other words it is Qualitative Data
Let us now understand these concepts in depth.
The data which has characteristics that represent a person’s age, behaviour, gender, or hometown is known as categorical data. These are usually expressed or resented with the help of natural language descriptions but it cannot be numbers or other special characters. Some examples of the categorical data are:
Pincode
Phone Number
Year
School they went to
The place they were born
Place they lived
You may notice that some of the above-mentioned categories like the age and Pincode might contain numerical as their value but this doesn’t mean that they are not categorical values. The most effective and the simplest way to find whether the given data is categorical or not, you simply find the average of the data. If you can calculate the average of a given data set, then you can consider it as numerical data. In the examples that are mentioned above, the numerical data is the pin code, the phone number, and the age because you can’t really calculate the average of pin code or phone number or year. Please note categorical and numerical data are different.
In Statistics, categorical data has observations and values that can be sorted in groups and categories. One of the best ways to represent this is with the help of a pie graph or a bar graph. Next, the categorical data is now divided into two different categories, namely,
Nominal Data
Ordinal Data
Nominal Data is used to label the variables without providing any numerical value. In other words, it is also called nominal scale. Also, the nominal data cannot be ordered nor it cannot be measured. But, it can be quantitative and also quantitative. The most common examples of nominal data are:
Words
Symbols
Gender
Letters, etc.
With the help of the grouping method, you can analyse this data. The frequency or percentage is calculated after grouping the data into categories. It can be visually represented with the help of a pie chart.
Any data that follows a natural order is known as ordinal data. The vital notable feature of ordinal data is that the difference in the values cannot be found easily. You come across this kind of data during surveys, finance, economics, and questionnaire. You can analyze this data with the help of some visualisation tools such as graphs or charts. The most commonly used chart to represent ordinal data set is the bar graph. Besides that, sometimes the data can be represented using tables indicating the distinct categories.
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In statistics, when a variable consists of a fixed number of values or a limited number of values, it is called as a categorical variable. The values can normally be variables like the blood group, labels, names, etc. Some of the examples related to categorical variables are:
The colour of a wall, like red, blue, pink, gree, etc., - has to be described.
Gender of people - like a male, female and transgender - cannot be anything other than this
The blood group of a person - like A, B, O, AB, etc., cannot be anything other than this.
These variables can be assigned to another unit of observation or an individual to a particular group or a nominal category based on certain qualitative properties. Generally, each of the potential values of a categorical variable is said to be as a level. The probability distribution linked with a random categorical variable is known as categorical distribution.
1) What is Categorical Data Analysis?
The data which has characteristics that represent a person’s age, behaviour, gender, or hometown is known as categorical data. These are usually expressed or resented with the help of natural language descriptions but it cannot be numbers or other special characters. Some of the categorical data examples are Pincode, Phone Number, Year, School they went to, The place they were born, Place they lived, etc You may notice the some of these categories like the age and Pincode might contain numerical as their value but this doesn’t mean that they are not categorical values. The most effective and the simplest way to find whether the given data is categorical or not, you simply find the average of the data. If you can calculate the average of a given data set, then you can consider it as numerical data. In the examples that are mentioned above, the numerical data is the pin code, the phone number, and the age because you can’t really calculate the average of pin code or phone number or year.
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