Types of Variables

Variables represent information about something that can change. The design of the measurement scales, or of the methods for obtaining information, will determine the data gathered and the characteristics of that data. As a result, a variable can be qualitative or quantitative, and may be further classified into subgroups. Every possible way to express the value of a variable, or the range of the variable, is called a domain.

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A variable is a placeholder for a value that may be represented in a qualitative or quantitative manner. A function is a relationship between multiple variables.

2 major classes of variables:

  • Qualitative: represent categories
    • Nominal
    • Ordinal
  • Quantitative: represent a measurable quantity 
    • Continuous
    • Discrete

Within epidemiology:

In epidemiology, a variable is understood as a set of attributes.

  • Independent variable = exposure, what is manipulated 
  • Dependent variable = outcome, what is measured
Interpretations of variables

The distinct interpretations of the independent and dependent variables in mathematics and epidemiology:
This example illustrates a function commonly utilized to estimate the target exercise heart rate (HR) for a patient based on age. The patient’s age (x) can be changed, and is thus the independent variable (or exposure). HR represents the patient’s target exercise heart rate, and is calculated based on the dependent variable. Because of this, HR considered the dependent variable (or outcome).

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Qualitative Variables


  • Refer to the name given to each category, which don’t have a number or rank in a scale
  • E.g., surgical sutures, blood groups, occupations, food groups

Ordinal or ranked:

  • Refer to the degrees of a measuring scale, which can’t be summed or averaged
  • E.g., degrees of burn injury, extent of pitting edema in lower limb, intensity of heart murmur

Quantitative Variables


  • There is value in between their values; they are thus infinite.
  • E.g., age, height, blood pressure, serum glucose (one can measure a person’s height as 2 m, 2.1 m, 2.2 m, etc.)


  • There is no value between them.
  • E.g., amount of people (it is not possible to have 1.5 people)
  • Categorization:
    • A continuous data series is divided into categories.
    • E.g., religion: People can describe themselves as Christian, Muslim, Jewish, etc. 
  • Dichotomous, or binary, variables:
    • A categorical variable with only 2 values is referred to as a dichotomous or binary variable.
    • Example: sick or healthy, alive or deceased, normal or abnormal

Example of the process of dichotomization:
“Dichotomize” means to convert a nondichotomous variable to a dichotomous one.

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  1. Katz DL, Elmore JG, Wild DMG, Lucan SC. (2014). Describing variation in data. In Katz DL, Elmore JG, Wild DMG, Lucan SC (Eds.), Jekel’s Epidemiology, Biostatistics, Preventive medicine, and Public Health, pp. 105–118.
  2. Babbie ER. (2009). The Practice of Social Research. 12th edition. Wadsworth Publishing, pp. 14–18.

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