25 Dichotomous Variables Examples

dichotomous variable examples and definition, explained below

Dichotomous variables are just what they sound like: variables with two, and only two, distinct categories or options.

Distinct primarily in the world of statistics, these variables provide binary alternatives, such as yes or no, present or absent, success or failure (Hunter & Schmidt, 2004). In quantitative research, they serve an instrumental role in a variety of analytical methods that range from cross-tab comparisons to logistic regression (Adams & McGuire, 2022).

chrisMy Simple Definition: “A dichotomous variable is a type of categorical variable that has two categories or response options.”

Dichotomous Variables Examples

1. Gender
In traditional demographic surveys, gender is often considered a dichotomous variable, with the categories of Male and Female.

2. Alive or Dead
In biological or medical research, the central variable can be whether a subject is alive or dead.

3. Employed or Unemployed
In labor market studies, the employment status of the population can be a critical dichotomous variable.

4. Pass or Fail
In educational evaluations, the students’ performance can be measured as a dichotomous variable: pass or fail.

5. Positive or Negative
Medically, test results can present dichotomous outcomes, such as positive or negative in a pregnancy test.

6. Buy or Not Buy
In market research, consumer behavior towards a product can be perceived as a dichotomous variable.

7. Married or Unmarried
In social science research, the marital status can be considered a dichotomous variable.

8. Presence or Absence of Disease
In health-based studies, having or not having a particular disease is a dichotomous variable.

9. Natural or Artificial
In research about materials, the origin of the material—natural or artificial—serves as a dichotomous variable.

10. Smoking or Non-Smoking
In wellness promotions, people’s smoking habits are referenced dichotomously: you either smoke or you do not.

11. Default or Not Default
In finance, whether someone will default or not default on a loan is a common dichotomous variable.

12. Graduate or Undergraduate
In educational research, the level of study for students can be used as a dichotomous variable: graduate or undergraduate.

13. E-commerce or In-store
In retail studies, purchasing methods (online vs in-store) can serve as an example of a dichotomous variable.

14. Yes Vote or No Vote
In political science, the type of vote submitted can serve as a dichotomous variable.

15. Sick or Not Sick
In medical research, patient status can be divided into dichotomous categories: sick or not sick.

16. Success or Failure
In project management studies, the project outcome could be a dichotomous variable in the form of success or failure.

17. Innocent or Guilty
In legal statutes, judgments and rulings, the verdict is a clear dichotomy: you’re either innocent or guilty, and nothing in between.

18. Public Sector or Private Sector
In economics research, the sector a firm operates in can serve as a dichotomous variable.

19. Beneficiary or Non-beneficiary
In policy and program evaluations, whether someone benefits from the program or not is a dichotomous variable.

20. Renting or Owning
In housing studies, whether a person rents or owns a home is a dichotomous variable.

21. Migration or Non-migration
In urban development research, whether people migrate or not is a dichotomous variable.

22. Membership or Non-membership
In social network studies, belonging or not belonging to a group can serve as a dichotomous variable.

23. Use or Non-use of Service
In service sector studies, whether a customer uses a service or not is a dichotomous variable.

24. Compliance or Non-compliance
In regulatory investigations, whether a firm complies or does not comply with the laws can serve as a dichotomous variable.

25. Remote job or Office job
In studies about work environments, the type of employment – remote or office-based— could be the dichotomous variable examined in the study.


Is a Dichotomous Variable Qualitative or Quantitative?

Dichotomous variables are generally seen as qualitative or categorical variables, but may be quantitative in some statistical studies.

They are qualitative in the sense that they reflect a characteristic or quality (for example, presence or absence of a certain condition or trait).

However, they can also be discrete variables within quantitative research. Unlike other qualitative variables, some well-defined dichotomous variables can be quantitatively analyzed because they represent two distinct numerical values (ordinarily coded as 0 and 1).

In a sense, dichotomous variables can bridge the gap between qualitative and quantitative variables (Weinberg & Abramowitz, 2008).

Dichotomous Variables vs Ordinal Variables

Dichotomous and ordinal variables are both types of categorical variables. Yet, they differ fundamentally.

A dichotomous variable has only two categories. On the other hand, ordinal variables have two or more (usually more) categories that follow a specific order or rank (Coolidge, 2012).

Examples of ordinal variables include ratings (poor, okay, good, excellent) or class rank (freshman, sophomore, junior, senior).

With ordinal variables, you do not just have discrete categories, but these categories possess a clear, logical progression (De Vaus, 2002).


Dichotomous variables form the backbone of many research designs in various fields. They simplify complex scenarios into two options, assisting analysts in making sense of large volumes of data. Experts use dichotomous variables to highlight stark contrasts and demonstrate clear binary relationships. As crucial tools in research, understanding their role, and being able to identify them in studies is integral for robust data analysis practices.


Adams, K. A., & McGuire, E. K. (2022). Research Methods, Statistics, and Applications. SAGE Publications.

Coolidge, F. L. (2012). Statistics: A Gentle Introduction (3rd ed.). SAGE Publications.

De Vaus, D. A. (2002). Analyzing Social Science Data: 50 Key Problems in Data Analysis. SAGE Publications.

Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings. SAGE Publications.

Weinberg, S. L., & Abramowitz, S. K. (2008). Statistics Using SPSS: An Integrative Approach. Cambridge University Press.

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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]

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