When conducting research, categorical data is of the utmost importance. Research involves the application of two types of data — categorical data and numerical data. Categorical data refers to a kind of qualitative data that may be classified into several categories. The data comprises categorical variables that can only be expressed in natural language. While numerals may be used to represent categorical data, there is no mathematical aspect of categorical data.
Categorical data generally includes data on birth, hair colour, body weight, height, and other factors relevant to any specific research. A deep understanding of categorical data is crucial to conducting data analysis in research.
If you seek to delve deeper into categorical data, then you may consider pursuing a career in data science. Enrol in a data science certification course to gain a deep insight into the nuanced aspects of categorical data for data analytics. Read on to learn more about categorical data to become a data analyst and conduct in-depth data analysis in your research.
Categorical Data: Types
Categorical data primarily includes points of observation and values that can be grouped into definite classes based on characteristics. Specifically, categorical data is of two types: nominal and ordinal.
Nominal data is a type of categorical data that cannot be ranked hierarchically. While nominal data can be both quantitative and qualitative at times, yet, it cannot be measured or arranged in a ranked order. Symbols, letters, and words are some instances of nominal data. Ordinal data is the type of categorical data that possesses a natural order. Ordinal data is generally used in surveys and questionnaires.
A deeper understanding of nominal and ordinal data can be acquired by pursuing a Data analytics certification course.
Primary Characteristics of Categorical Data
The key features of categorical data are listed below:
- Just as the name suggests, categorical data can be classified into groups. Based on the nature of the data, categorical data can be grouped into non-binary and binary categories.
- The classes into which categorical data is classified are created based on qualitative characteristics
- Categorical data can constitute numerical values that do not have a mathematical aspect.
- Categorical data can be represented in the form of bar charts and pie charts.
- Data science recommends using median and mode functions for analysing categorical data. While the mode function is used for nominal data analysis, both median and mode functions are used for ordinal data analysis.
If you seek to better understand the characteristics of categorical data in data analytics, you may consider enrolling in data science training courses.
Ways to Analyse Categorical Data in Data Analytics
Analysing categorical data may be a bit complex, which is why you may need to enrol in a data analytics course to learn the fundamentals of data analysis. The procedures for analysing categorical data are briefly described below:
Tabulation
The tabulation procedure is for summarising a column of variable data. This procedure is for tabulating the incidence of occurrence of every distinct value in the column. Each incidence is then represented in tabular and graphical forms.
Frequency tables
This procedure is for analysing singular and tabulated categorical factors. The frequency of occurrence of the singular categorical factor is represented in the form of a pie chart or a bar chart. Data analysts also conduct statistical tests to ensure that the singular categorical factor is aligned with multinomial probabilities.
Contingency tables
This procedure is for the analysis and display of frequency data tabulated in two-way tables. Data analysts apply statistical analysis techniques to quantify the degree of relationship between the columns and rows of the contingency tables.
Correspondence analysis
This analysis involves the creation of a map of columns and rows in a 2-way contingency table. The map provides insight into the degree of association among the categories of column and row variables.
Multiple correspondence analysis
This procedure involves the creation of a map denoting the relationships among the categories of at least two variables. The map also discloses interrelationships among the data variables.
Crosstabulation
This procedure is for the summarisation of two columns of variable data. Analysts construct a two-way table to indicate the incidence of occurrence of every unique pair of attributes in the columns. In this procedure, the degree of association among the columns is quantified, and statistical tests are conducted to determine the degree of dependence between the value in one column and the value in the other column.
Item reliability analysis
This procedure refers to the analysis of categorical data in a way to estimate the consistency of a group of attributes. The output of item reliability analysis is graphically represented in a Cronbach’s alpha plot.
There are several other procedures for analysing categorical data in data analytics. To understand the statistical procedures of categorical data analysis, you may sign up for a data analytics course and consider a career in data analytics.
Examples of categorical data
The example herein may make understanding the basics of categorical data easier. Let’s say that you are throwing a party and want to serve your guests welcome drinks. So, you make a quick survey and jot down the data in a table, as given below:
| Drinks | Frequency |
| Mirinda | 04 |
| Coke | 02 |
| Sprite | 06 |
| Fanta | 01 |
The data in the table is categorical, as evident from how the data has been grouped into distinct classes.
Conclusion
Determining between categorical and numerical data is crucial for data analysis. While categorical data possesses distinct labels or categories, numerical data comprises quantifiable variables. It is also important to be well aware of the procedures of analysing categorical data to conduct and conclude a successful research work. To be an expert in the fundamental and advanced concepts of categorical data, you may sign up for the Postgraduate Program In Data Science And Analytics, the data science course offered at Imarticus. Regularly participate in the data science training sessions and pave the way to become a data analyst today.
