The 11 Types Of Variables Used In Research

A review of the main classes of variables used in science for research.

Types of variables

Age. Sex. Weight. Height. Occupation. Socioeconomic status. Anxiety level. These and other elements must be taken into account when trying to explain some type of hypothesis regarding the human being or some type of problem.

And it is that in everything that exists and happens around us, innumerable types of variables participate that can have a more or less relevant role in the different phenomena that occur. It will be necessary to analyze and take into account which variables influence and how they do it if we want to obtain a generalizable explanation. It is something that all those engaged in scientific research take into account, both in psychology and in other sciences. In this article we are going to review which are the main types of variables that exist.

What is a variable?

Before going on to observe the different variable types, it may be convenient to carry out a brief review of what we consider to be such in order to facilitate their identification and take into account their importance.

A variable is understood to be an abstract construct that refers to a property, characteristic or element studied that may or may not have a specific role on what is being analyzed and that is presented in such a way that it can have different values. These values, then, can vary in different measures depending on both the variable and the situation being analyzed or the limits that the researchers want to take into account.

We are therefore faced with a concept that brings together the different options or modalities that can be taken into account with respect to a characteristic in question, said values ​​being variable and different at different times and / or subjects.

The concept in question may seem complex to understand theoretically, but it is much more understandable if we think that some variables may be those mentioned in the introduction: the weight or sex of a person would be simple examples of variables that may or may not affect in different conditions (for example, in diabetes or heart disease).

Variables can be classified in very different ways and based on numerous different criteria, such as their level of operability, their relationship with other variables or even the scale on which they are measured. It is important to bear in mind that the same element can have different roles and be classified as different types of variable depending on its role in a given situation or experimental context.

Types of variables according to their operability

Do not forget that scientific research always requires simplifying to a greater or lesser extent elements of what you want to study. Identifying the important elements to focus on, leaving everything else out of focus, is an indispensable requirement, because otherwise we would not be able to analyze anything because we do not know what type of data to start with.

Thus, the different types of variables account for the diversity of elements in which we can look to study patches of reality. Of course, this diversity makes it essential to choose the variables well to be able to focus on what allows us to reach valid conclusions about our object of study.

As we have mentioned, one of the best-known and classic ways of dividing and classifying the different variables is in relation to their operability, that is, to the possibility of numbering their values ​​and operating with them. Taking this aspect into account we can find three main types of variables.

1. Qualitative variables

A qualitative variable is considered to be any variable that allows the expression and identification of a specific characteristic, but that does not allow them to be quantified. This type of variable would only inform us of the existence or non-existence of said characteristic or the presence of alternatives. They are merely nominal, expressing equality and / or inequality. Sex or nationality would be examples of this. However, this does not mean that they cannot be observed or that elements are not highly relevant in the investigation.

Within the qualitative variables we can find different types.

Dichotomous qualitative variables

These are variables in which only two possible options exist or are contemplated. Being alive or dead is an example of this: it is not possible to be alive at the same time, in such a way that the presence of one of the values ​​negates the other.

Qualitative polytomous variables

Those variables that admit the existence of multiple values, which as in the previous case only allow an identification of a value and this excludes the rest without being able to order or operate with said value. Color is an example.

2. Quasi-quantitative variables

These are those variables with which it is not possible to perform mathematical operations, but which are more advanced than the merely qualitative ones. They express a quality and at the same time allow to organize it and establish an order or hierarchy, although not exactly.

An example of this is the level of studies, being able to determine if someone has more or less of this quality.

However, there is no evidence of the differences between a category and the one that precedes it and the one that follows it (a person who has postgraduate studies does not know more than one with a bachelor’s degree in the same way that a person with secondary studies knows more than another that only has elementary school).

3. Quantitative variables

The quantitative variables are all those that, this time, allow the operationalization of their values. It is possible to assign different numbers to the values ​​of the variable, being able to perform different mathematical procedures with them in such a way that different relationships between their values ​​can be established.

In this type of variables we can find two large groups of great relevance, continuous and discrete variables.

Discrete quantitative variables

This is the set of quantitative variables whose values ​​do not admit intermediate values, and it is not possible to obtain decimals in their measurement (although then averages can be made that do include them). For example, it is not possible to have 2.5 children. They usually refer to variables that use ratio scales.

Continuous quantitative variables

We speak of this type of variables when their values ​​are part of a continuum in which between two specific values ​​we can find various intermediate values. More frequently, we speak of variables that are measured on an interval scale.

According to its relationship with other variables

It is also possible to determine different types of variables based on how their values ​​are related to those of others. In this sense, several types stand out, the first two being especially relevant. It is important to bear in mind that the same element can be one type of variable and another depending on the type of relationship being measured and what is being modified. Furthermore, it must be taken into account that the role and type of variable in question depends on what we are analyzing, regardless of the role that the variable actually occupies in the situation studied.

For example, if we are investigating the role of age in Alzheimer’s, the age of the subject will be an independent variable while the presence or absence of tau protein and beta-amyloid plaques will be a dependent variable in our research (regardless of the role that have each variable in the disease).

1. Independent variables

Independent variables are understood to be those variables that are taken into account at the time of the investigation and that may or may not be possible to modify by the experimenter. It is the variable from which one starts to observe the effects that determine quality, characteristic or situation can have on different elements. Gender, age, or baseline anxiety level are examples of an independent variable.

2. Dependent variables

The dependent variable refers to the element that is modified by the existing variation in the independent variable. In the research, the dependent variable will be chosen and generated from the independent one. For example, if we measure the level of anxiety according to sex, sex will be an independent variable whose modification will generate alterations in the dependent, in this case anxiety.

3. Moderator variables

We understand by moderating variables the set of variables that alter the existing relationship between the dependent and independent variables. An example of this is given if we relate hours of study with academic results, with moderating variables being emotional state or intellectual capacity.

4. Strange variables

This label refers to all those variables that have not been taken into account but that have an effect on the results obtained.

Thus, they are all that set of variables not controlled and taken into account in the studied situation, although it is possible to identify them after it or even during an experiment or context investigated. They differ from moderators in the fact that strangers are not taken into account, this is not the case for moderators.

In other words, strange variables are those that can lead us to erroneous conclusions when interpreting the results of an investigation, and the impact of their presence depends on the quality of the design of the studies carried out to investigate something.

Types of variables according to scale

Another possible classification of variables can be made according to the scales and measures used. However, it must be taken into account that more than the variable, we would be talking about the scale in question as a distinctive element. It should also be borne in mind that as the level of operability of the scales used increases, new possibilities are incorporated in addition to those of the previous scales. Thus, a ratio variable also has the properties of the nominal, the ordinal, and the interval. In this sense we can find the following types.

1. Nominal variable

We speak of nominal variables when the values ​​that said variable can reach only allow us to distinguish the existence of a specific quality, without allowing these values ​​to carry out an ordering or mathematical operations with them. It is a type of qualitative variable.

2. Ordinal variable

Although it is not possible to operate with them, it is possible to establish an order between the different values. However, this order does not allow the establishment of mathematical relationships between their values. These are fundamentally qualitative variables. Examples are socioeconomic status or educational level.

3. Interval variable

In addition to the previous characteristics, the variables on an interval scale allow establishing numerical relationships between the variables, although generally these relationships are limited to proportionality. There is no absolute zero or totally identifiable zero point, something that does not allow direct transformations of the values ​​into others. They measure ranges, rather than specific values, something that complicates their operation but helps to cover a large number of values.

4. Ratio variable

The ratio variables are measured on a scale such that they can be fully operationalized, and various transformations can be made to the results obtained and establishing complex numerical relationships between them. There is a point of origin that supposes the total absence of what is measured.

Different ways of analyzing reality

It should not be forgotten that the different types of variables are always a simplification of reality, a way of dividing it into simple and easy-to-measure parameters, isolating them from the rest of the components of nature or society.

Therefore, we cannot limit ourselves to believing that knowing these variables is fully understanding what is happening. Taking a critical look at the results obtained from the studies of variables is necessary to avoid reaching erroneous conclusions and not closing ourselves to more complete and realistic explanations of what is happening around us.

Bibliographic references:

  • Barnes, B. (1985): On science, Barcelona: Labor.
  • Fraleigh, JB (1989). A First Course in Abstract Algebra. New York: Addison-Wesley
  • Latour, B. and Woolgar S. (1979/1986): Life in the laboratory. The construction of scientific facts, Madrid: Alianza Universidad.
  • Sullivan, M. (1998). Trigonometry and analytical geometry. Barcelona: Pearson Education.

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