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Categorical data analysis

Categorical data analysis, as the name hints, is simply the process of grouping information into different categories. For example, if an organization is trying to collect biodata of its staff, the data collected is often referred to as categorical. This is because, this data can further be grouped into different variables such as gender, residence, age, etc. Sometimes, categorical data can be assigned numerical values such as “1” and “2” to indicate “Yes” or “No” respectively. However, these values do not have any arithmetic value, hence they cannot be added together or subtracted from each other.

Types of categorical data analysis

Categorical data analysis falls under two classes: - nominal data analysis and ordinal data analysis.
  • Nominal data analysis: This type of analysis is performed on data that does not have a numerical value such as name, gender, hair color, etc. Nominal data is usually collected using questionnaires or surveys and is descriptive in nature, meaning, the respondents are having the freedom to provide typed responses. However, even though typing in responses helps researchers arrive at better conclusions, sometimes analyzing such data can pose problems for researchers because they have to deal with a huge amount of irrelevant data.
  • Ordinal data analysis: In ordinal data analysis, researchers deal with data that has a set scale or order to it, like client satisfaction survey data, bug severity, interval scale, Likert scale, etc.
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Features and characteristics of categorical data

  • Categories: There are two major categories of categorical data – nominal data, which is used to identify and name a variable, and ordinal data, which is used to show the order or the scale of a given variable.
  • Qualitativeness: Categorical data is said to be qualitative because it describes a sample using a defined string of words instead of numbers.
  • Graphical analysis: Categorical data can be analyzed and presented graphically using a pie chart and a bar chart. A pie chart is used to analyze percentage while a bar chart analyzes frequency.
  • Interval scale: Categorical data has a standardized interval scale. However, this is only applicable to nominal data; ordinal data does not have a standard scale interval.
  • Numerical values: Even though categorical data is said to be qualitative, sometimes it can take numerical values. Nevertheless, these values do not exhibit the characteristics of quantitative data and hence researchers cannot perform arithmetic operations on them.
  • Nature: Categorical data can also be grouped into binary or non-binary based on its nature. For instance, a question with options “Yes” or “No” can be said to be binary because it contains two options. Adding “Maybe” to the options, however, makes the question non-binary.

Applications of categorical data analysis

Categorical data analysis can be performed in a wide range of events. The most common one includes calculating:
  • Household income: Businesses use categorical data analysis to investigate the spending power of the group of customers they are targeting. This helps them determine the most affordable price for their services or products. Consider the example below:
What is your household income?
  • Below $25,001
  • $25, 001 - $35, 000
  • $35, 001 - $45, 000
  • $45, 000 and above
  • Level of education: The education level may be requested when an individual is filling out forms for training, admission, job applications, etc. This information is used to determine whether the person is qualified for the role or not. Example:
What is your highest education level?
  • High school
  • BSc.
  • MSc.
  • PhD
  • Gender: Individuals are requested to state their gender when providing their biodata. This type of data is commonly classified as male or female, although sometimes it may also be categorized as non-binary. Example:
What is your gender?
  • Male
  • Female
  • Customer satisfaction: When a business is rendering its services to its customers, sometimes it may need to find out whether the customers are happy with the services or not. This feedback helps the business identify areas that need improvement. Example:
How can you rate our services?
  • Very poor
  • Poor
  • Neutral
  • Good
  • Excellent
The above is a great example of ordinal data because the responses follow a specific order; they are listed in ascending order.

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