25 Categorical Variable Examples

Categorical variables are a kind of statistical data type, also known as qualitative variables, that divide data into various categories or groups based on certain features, characteristics or labels (Lewis-Beck, Bryman & Liao, 2004).

These sorts of variables are commonly used in cross-sectional studies such as a population census and on likert scale questionnaires.

Examples could range from the color of the eyes (blue, green, brown, etc.) to the type of housing a person lives in (apartment, bungalow, townhouse, etc.)

There are three types of categorical variables: nominal, ordinal, and dichotomous (aka binary).

The key point with categorical variables is that the categories are fixed, and there is no intrinsic numerical relationship between the categories (Katz, 2006b; Stockemer, 2018). To determine which type of categorical variable is best suited for a given set of data, it’s important to carefully examine the characteristics, features, or labels under consideration.

Categorical Variables Examples

1. Hair Color (Nominal)
Hair color is a prototypical categorical variable, with categories including “blonde”, “brunette”, “black”, and “red”. The labels represent specific groups, but without an inherent order or numerical relationship among them.

2. Eye Color (Nominal)
Eye color can also be categorized into qualitative variables. Common categories may include “blue”, “green”, “brown”, and “hazel”. These are distinct categories without any inherent order.

3. Sex (Dichotomous)
Sex is often considered a dichotomous categorical variable, with the usual categories being “male” and “female”. In such a binary categorization, there are strictly two distinct groups representing the data. Today, gender and even sometimes sex are seen as nominal rather than binary because more types of genders tend to be recognized than the male-female binary.

4. Marital Status (Nominal)
Marital status is another example. Your status could be “single”, “married”, “divorced” or “widowed” – distinct categories without an explicit order.

5. Nationality (Nominal)
Nationality is a nominal categorical variable that categorizes individuals by their country of citizenship. Categories could include “American”, “Canadian”, “French”, “Japanese”, etc.

6. Occupation (Nominal)
Different jobs form distinct categories like “physician”, “engineer”, “artist”, “teacher”, etc. This categorical variable has no inherent order among its categories.

7. Has a Pet (Dichotomous)
A survey question asking if the respondent owns a pet, with possible answers being “yes” or “no”, provides a binary variable.

8. Blood Type (Nominal)
Blood types, such as “A”, “B”, “AB”, and “O”, fall into definite categories without any intrinsic order or ranking, making it a nominal categorical variable.

9. Animal Species (Nominal)
The animal kingdom is full of diverse species — cats, dogs, elephants, lions, etc. This type of categorization is purely nominal, with no inherent order or numerical relationship between the groups.

10. Food Preferences (Nominal)
People’s preference for different types of food is a typical example of a categorical variable. Categories may include “vegetarian”, “vegan”, “non-vegetarian”, “pescatarian” and more without any inherent order.

11. Coffee Strength (Ordinal)
Coffee strength might be considered an ordinal variable if grouped into categories such as “light”, “medium”, or “bold”. These categories have a specific order—light is less strong than medium, which in turn is less strong than bold—but the difference between each category isn’t quantifiable or consistent.

12. Bread Freshness (Ordinal)
When determining the freshness of bread, labels such as “fresh”, “stale”, or “moldy” might be used. While these categories provide an order, the exact level of freshness each term represents is not uniformly defined.

13. Employee Department (Nominal)
In an organization, an employee’s department can be a nominal categorical variable. The individual may fall into different categories like “marketing”, “finance”, “sales”, “human resources”, etc.

14. Type of Accommodation (Nominal)
The variable “type of accommodation” could include categories such as “apartment”, “house”, “studio”, and “shared housing”. These categories, while distinct, lack any specific order or ranking.

15. Volunteering Activity (Nominal)
Different volunteering activities like “cleaning beach”, “tree plantation”, “teaching underprivileged kids”, and “serving food in shelter” form distinct categories under a nominal categorical variable.

16. Performance Status (Ordinal)
In performance reviews, employers can rate an employee’s performance as “below expectations”, “meets expectations”, “exceeds expectations”. These levels represent a distinct sequence, yet the difference between each level isn’t uniformly defined.

17. Competitive Position (Ordinal)
In competitions, positions like “first”, “second”, “third”, and so on, represent an ordinal variable. The positions clearly depict a ranking order, but the quantitative difference (e.g. speed or points) between each position is not consistent across all competitions.

18. Severity of Damage (Ordinal)
The degree of destruction, such as after a natural disaster, can be rated as “minor”, “moderate,” or “severe”. These categories indicate an order but the specific level of damage each category represents isn’t uniformly defined.

19. Cooking Skills (Ordinal)
Cooking skills could be described as “beginner”, “intermediate,” or “advanced”. These levels clearly illustrate an increment in ability, but the difference between each is not consistently defined.

20. Frequency of Exercise (Ordinal)
Frequency of exercise can also be determined through ordinal variables like “never”, “occasionally”, “regularly,” or “daily”. The categories reflect an order but the specific duration or intensity difference represented by each label isn’t consistently defined.

21. Social Media Platforms (Nominal)
Popular social media platforms — “Facebook”, “Instagram”, “Twitter”, “LinkedIn” — are categories under a nominal categorical variable.

22. Pass/Fail (Dichotomous)
The result of a test or examination often comes in a binary format, “pass” or “fail”.

23. Types of Insurance (Nominal)
Different types of insurance such as “life insurance”, “car insurance”, “health insurance”, and “home insurance” are categories in a nominal categorical variable.

24. Employee/Unemployed (Dichotomous)
One’s employment status can sometimes be reduced to a binary format: “employed” or “unemployed”. Like the gender example, this can often oversimplify reality (for instance, it doesn’t account for part time work, students, or retirement).

25. Buying Behavior (Nominal)
The buying behavior of a person could be categorized into “impulsive”, “careful”, “budgeted”, or “luxury”, etc. These are nominal categorical variables, without any inherent order.

Types of Categorical Variables

Categorical variables can be either nominal, binary, or ordinal.

Each is explained below.

• Dichotomous (Binary) variables are simply categorical variables that have only two categories or levels. They are essentially “yes” or “no” questions (Katz, 2006a; Katz, 2006b). For instance, whether you have a pet at home (“yes” or “no”) would be a binary variable predicament.
• Nominal variables, sometimes referred to as polychotomous variables, have two or more categories but without an inherent order. Examples include color of hair, type of dwelling, or brand of cereal eaten in the morning.
• Ordinal variables, a type of categorical data, have a specific order among the categories but do not have a standard scale to determine the precise differences between the categories (De Vaus, 2001). Grading scheme like “excellent”, “good”, “average”, “poor” is a typical ordinal variable example.

Categorical vs Continuous Variables

Categorical variables identify the membership of an individual or a thing into one of several distinct classes or categories. Continuous variables, on the other hand, have an infinite number of possible values within their descriptive range (Powers & Xie, 2008; Punch, 2003).

Continuous variables represent quantities and possess the properties of both magnitude and distance (Powers & Xie, 2008). Temperature measurements, for instance, are a good example of continuous variables. They can be taken at any point along a continuum from the lowest possible temperature (absolute zero) to the highest, with each measurement offering a precise reading (e.g., 72.6 degrees or 72.7 degrees).

The primary difference between categorical and continuous variables lies in how they are utilized and interpreted within research settings. Categorical variables don’t necessarily provide ordered arrangement or precise readings. By contrast, continuous variables bear values that can be quantitatively measured on a continual scale, providing a spectrum of detailed, exact outcomes (Powers & Xie, 2008; Punch, 2003).

Choosing between these variable types for your study depends largely on the nature of the data you plan to analyze and the level of measurement precision you aim to achieve.

Conclusion

Categorical variables portray a primary component in data analysis, offering invaluable insights into the qualitative nature of data, irrespective of the field of study. These variables allow data to be divided into various groups based on unique characteristics or features. However, with the absence of a concrete numerical relationship or order among the categories (unless they’re ordinal), conducting mathematical calculations or establishing relative magnitude tends to be challenging. Despite these constraints, the ability to sort data into distinct, meaningful groups makes categorical variables a potent tool in the realm of data analysis and research. Suitable application and interpretation of categorical variables can enrich understanding and contribute significantly to the robustness of research findings.

References

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Katz, M. (2006). Study Design and Statistical Analysis: A Practical Guide for Clinicians. Cambridge: Cambridge University Press.

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Powers, D., & Xie, Y. (2008). Statistical Methods for Categorical Data Analysis. Emerald Group Publishing Limited.

Punch, K. (2003). Survey Research: The Basics. London: SAGE Publications.

Stockemer, D. (2018). Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata. London: Springer International Publishing.

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