Before starting to write a manuscript or scientific article, it is important to be clear about certain concepts that will allow us to understand how to analyze the information we have collected. To this end, we will first review the definition of a variable, its types, and the importance of understanding this concept

The definition of what a variable is varies depending on the author consulted.¹˒² However, in this document we will define variables as a measurable and interpretable characteristic or attribute that changes across individuals or units of analysis. These variables may change from one subject to another or within the same subject over time. An example of a variable that does not change over time is biological sex, since even if this variable is measured in a subject during childhood, adolescence, or adulthood, it will not change. On the other hand, there are variables that do change over time; an example of this is the number of children a woman has throughout her life.

It should always be remembered that variables also encompass a “concept.” An example of this is biological sex, sexual orientation, and gender identity—each of which represents a different aspect of an individual’s sexuality. Confusing their definitions could lead to incorrect interpretations in a study. Therefore, whenever we use a variable, we must have a clear understanding of what we are measuring and exactly what we are referring to

Variables can be classified in many ways; one of them is based on whether the variable can be measured numerically. Those that can be measured numerically are quantitative variables, while those that cannot are called qualitative variables. An example of a quantitative variable is weight, which can be measured in kilograms (1.5 kg, 3.8 kg, or 5 kg). On the other hand, a qualitative variable would be the level of satisfaction a patient feels when receiving care (poor, fair, or good). In turn, quantitative variables can be divided into discrete or continuous, and qualitative variables can be divided into nominal or ordinal.

Another way to classify variables is based on their relationship to the study hypothesis. In this case, we have independent and dependent variables. Dependent variables are those whose value is secondary to another variable, while independent variables are those that contribute to or influence the occurrence of the dependent variable. An example of a dependent variable would be the presence or absence of lung cancer, and an independent variable could be cigarette smoking. It should be remembered that the same variable can be dependent or independent depending on the hypothesis that has been proposed

But why is it important to understand this? This is where we must recognize that the variables we use in our study require an analytical process, and this analysis will depend on the type of variable involved. If, during our analysis, we are dealing with a quantitative variable, we can consider the possibility of adding, subtracting, or dividing the variable. However, with qualitative variables (which cannot be measured numerically), this type of analysis is not possible, and attempting to do so would be a major error; for qualitative variables, the appropriate analyses are absolute and relative frequencies

• There are many definitions of what a variable is.
• Variables can be defined as a measurable and interpretable characteristic or attribute that varies across individuals or units of analysis.
• Variables can be classified in many ways (quantitative vs. qualitative or independent vs. dependent).
• Understanding the different types of variables allows us to determine which analyses can be performed with the collected information.

1. Arias Gonzáles, J. L. (2021). Guía para elaborar la operacionalización de variables. Espacio I+D: Innovación más Desarrollo, 10(28). https://doi.org/10.31644/IMASD.28.2021.a02
2. Andrade C. A Student’s Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 1. Indian J Psychol Med. 2021;43(2):177–179.

Gerhard M Acero
ESP epidemiology
Publication Date: 28/01/2026

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