Multi-category variables
These variables may have more than two categories. There is no fixed order between the categories and each type has the same probability of appearing.
Example
Answer
Select your ethnicity
British, Asian, African, American
Please specify your marital status
Married, single, divorced, widowed
Ordered nominal variables
It represents a type of nominal variable with categories that have a buy view like ranking order. However, the difference between categories may not be uniform or measured accurately.
Example
Answer
Would you recommend our product to others?
Extremely likely, probable, neither likely nor unlikely, unlikely, extremely unlikely
(Extremely likely would have the highest score, while unlikely would have the lowest.)
What is your highest level of qualification?
Less than high school, high school, bachelor's degree, master's degree, doctorate
(Here, less than a bachelor's degree might rank lowest, while a PhD would rank highest.)
Unordered nominal variables
These variables represent categories with no inherent order or hierarchy. Each type has the same weight and there is no specific sequence.
Example
Answer
Select the payment method you prefer
Cash, credit card, debit card, online bank transfer, PayPal
How did you hear about this job opportunity?
LinkedIn, Indeed, Company Website, Recruitment Agency, Other
These examples allow a clear understanding of the type of nominal variables.
A detailed analysis of categorical data can be performed using several library functions available in Python.
Ways to analyze nominal variables
The type of data research techniques employed depends on the research problem, the quality of the data, the size of the data set, and other factors.
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