25 Qualitative Variable Examples

Qualitative variables, also known as categorical variables, classify observations into defined, non-numerical groups (Babbie, Halley, & Zaino, 2007).

Distinct from numerical variables that characterize data along a numeric continuum, qualitative variables capture important attributes in non-numeric ways. They provide the means to discern and categorize data according to specific properties, styles, or characteristics that hold relevance in the scope of the investigation.

The types of qualitative variables are: nominal, ordinal, and dichotomous, which are defined after the examples provided below.

An Academic Definition: “If a variable indicates which of several nonnumerical categories an item falls into, it is a qualitative variable. If the categories have a natural, meaningful order, then it is an ordinal qualitative variable. If there is no such order, it is a nominal qualitative variable.” (Siegel, 2011, p. 30)
Contents

Qualitative Variables Examples

1. Sexual Orientation (Nominal)
Sexual orientation is a nominal, qualitative variable because it categorizes people into distinct groups based on their preferred sexual orientation. It doesn’t depict a numerical measure or amount and its categories do not have a specific order.

Someone’s academic degree is qualitative because it places holders into categories representing their highest achieved education level. These categories have an inherent order, but they don’t translate into a quantifiable value or a specific numerical difference between levels.

3. Blood Type (Nominal)
Blood type is a qualitative, nominal variable because it classifies individuals into groups based on blood characteristics. The categories (A, B, AB, O) are not numerical, and there isn’t a hierarchical order between them.

4. Satisfaction Ratings (Ordinal)
Satisfaction ratings are qualitative and ordinal. They categorize responses according to levels of satisfaction, which are not numeric. While they do convey an order (from very unsatisfied to very satisfied), the intervals between these categories aren’t uniform or quantifiable.

5. Employment Status (Dichotomous)
Employment status is a qualitative, dichotomous variable. It categorizes individuals into two groups: employed or unemployed. This division isn’t based on a numeral amount or quantity but rather on the characteristic of employment.

6. Ethnicity (Nominal)
Ethnicity is a qualitative nominal variable that classifies individuals based on their ethnic group. These categories, such as Caucasian, African, Asian, Hispanic, etc., are not numeric or hierarchically ordered.

7. Age Group (Ordinal)
Age group classifications such as children, teenagers, adults, and elderly are ordinal, qualitative variables. They depict progressions in age, which are not numerical measures. Each of these categories demonstrates a particular stage of age but doesn’t exhibit a specific numeric value or difference.

8. Marriage Status (Dichotomous)
Marital status, as being either married or not married, is a dichotomous, qualitative variable. This classification does not rely on numerical measures or quantities but on the intrinsic characteristic of being married.

9. Movie Genres (Nominal)
Movie genres like Comedy, Action, Drama, and Horror demonstrate qualitative, nominal variables. They categorize movies based on the genre, not by any numerical measure or hierarchical order.

10. Social Class (Ordinal)
Social class, with categories such as lower-class, middle-class, and upper-class, is ordinal and qualitative. Its nature is not numerical. Although it identifies an inherent hierarchical order, this order doesn’t equate to specific numerical differences.

11. Marital Status (Nominal)
The marital status of individuals—categorized as single, married, divorced, or widowed—is a qualitative, nominal variable. These categories don’t possess a numerical value or a specific order.

12. Performance Rating (Ordinal)
Performance ratings—ranging from Poor, Average, Good, Excellent—represent ordinal, qualitative variables. They capture the ordered level of performance, yet these levels are not quantifiable and don’t possess specific numerical differences.

13. Outcome of a Game (Dichotomous)
The outcome of a game, defined as either won or lost, is a dichotomous, qualitative variable. Its classification is based on the characteristic of the game’s outcome, not a numerical value or quantity.

14. Newspaper Section (Nominal)
Newspaper sections like News, Sports, Entertainment, Business are examples of qualitative, nominal variables. They separate news into categories depending on the type of content, without assigning any numerical attributes or hierarchical order.

15. Severity of an Illness (Ordinal)
The severity of an illness—categorized as mild, moderate, or severe—represents qualitative, ordinal data. These categories indicate a progression in severity, but they aren’t based on a standard numerical scale or quantifiable differences.

16. Smoking Status (Dichotomous)
A person’s smoking status, classified as a smoker or a non-smoker, is a dichotomous, qualitative variable. It groups individuals not based on any numeral quantity but on the presence or absence of the habit.

The type of business such as Retail, Manufacturing, or Services, represents a nominal, qualitative variable. Different business models form distinct categories, which don’t possess any numerical attributes or prevailing hierarchy.

18. Credit Rating (Ordinal)
Credit ratings—classified as Excellent, Good, Fair, Poor—illustrate ordinal, qualitative variables. They segment financial creditworthiness into ranked categories that, while ordered, do not provide a specific numerical value or consistent scale.

19. Language Spoken (Nominal)
Language Spoken, whether it be English, Spanish, French, etc., serves as a qualitative, nominal variable. It categorizes the medium of communication, without any numerical valuables or inherent order between the languages.

20. Household Size (Ordinal)
Household size, designed as Single, Small (2-3 members), Medium (4-5 members), Large (6+ members), are ordinal, qualitative variables. These categories depict the size and composition of a household, but they don’t denote precise numerical measures or quantifiable differences.

21. Season (Nominal)
Season data like Spring, Summer, Autumn, and Winter manifest nominal, qualitative variables. Seasons categorize temporal data, which does not involve numerical measures or an order of progression.

22. Pollution Level (Ordinal)
Pollution level—categorized into low, moderate, high, and hazardous—represents ordinal, qualitative data. These differing classifications translate to various states of pollution but don’t correspond to exact numerical measurements.

23. Migration Status (Dichotomous)
Migration status, whether a person is native or a migrant, aligns with a dichotomous, qualitative variable. Such categorization is not founded on numbers but on the characteristic of migration itself.

24. Religion (Nominal)
Religion, with examples including Christianity, Islam, Hinduism, Buddhism, etc., represents a nominal, qualitative variable. It demarcates individuals based on their religious beliefs, not involving any numerical measurements or an inherent order among the religions.

25. Transport Preference (Nominal)
Transport preferences, such as by car, bike, public transit, or walking, are examples of nominal, qualitative variables. These preferences distinguish individuals according to their travel mode, absent of numerical metrics or order of preference.

Types of Qualitative Variables

There are three primary types of qualitative variables: nominal, ordinal, and dichotomous (Wilson & Joye, 2016).

• Nominal Variables categorize data without imposing an order or hierarchy among the categories. Nationality, hair color, and marital status are examples of nominal variables wherein the categories exclusively separate the data without indicating any order or precedence among them (Norman & Streiner, 2008, p. 4).
• Ordinal Variables preserve the categorizing feature of nominal variables but introduce an element of order among the categories (De Vaus, 2001). Educational level (primary, secondary, tertiary) and satisfaction rating (dissatisfied, neutral, satisfied) are examples of ordinal variables, where the categories are ranked based on their inherent order.
• Dichotomous Variables, a type of nominal variable, only have two categories or levels (Stockemer, 2018; Katz, 2006a). Gender (male, female) and a true or false test result are examples highlighting the binary aspect of dichotomous variables.

Conclusion

Understanding qualitative variables is essential for successful data analysis and research. They are fundamental in identifying and grouping data based on categorized features or characteristics. While their non-numeric nature requires specific analytical techniques, they offer a valuable lens through which to discern patterns, draw comparisons, and analyze results. By wisely choosing and leveraging the appropriate form of qualitative variable – nominal, ordinal, or dichotomous – researchers can effectively classify and organize their data to align with the nature of their examination.

References

Babbie, E., Halley, F., & Zaino, J. (2007). Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows (6th ed.). New York: SAGE Publications.

De Vaus, D. A. (2001). Research Design in Social Research. New York: SAGE Publications.

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

Katz, M. H. (2006b). Multivariable analysis: A practical guide for clinicians. Cambridge: Cambridge University Press.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (Eds.). (2004). The SAGE Encyclopedia of Social Science Research Methods (Vol. 1). London: SAGE Publications.

Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials. New York: B.C. Decker.

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.

Siegel, A. F. (2011). Practical Business Statistics (6th ed.). Elsevier Science.

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

Wilson, J. H., & Joye, S. W. (2016). Research Methods and Statistics: An Integrated Approach. New York: SAGE Publications.

Wang, Y. (2009). Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection. Information Science Reference.

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]