The 11 types of variables used in research

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

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

    What is a variable?

    Before proceeding to the observation of the different types of variables, it may be desirable to give a brief reminder of what we consider to be such in order to facilitate their identification and take into account their importance.

    By variable is meant an abstract construction which refers to a property, characteristic or studied element which may or may not have a specific role in what is analyzed and which is presented in such a way that it may have different values. These values ​​can therefore vary in different sizes depending on both the variable and the situation analyzed or the limits that the researchers want to take into account.

    We are therefore faced with a concept which brings together the different options or modalities that can be taken into account in relation to a characteristic in question, these values ​​being inconsistent 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 consider 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 different conditions (eg, in diabetes or heart disease).

    Variables can be classified very differently and on the basis of many differentiated criteria, such as their level of operability, their relationship with other variables, or the scale at which they are measured. It is important to note that the same element can have several roles and be classified as different types of variables according to its role in a given situation or an experimental context.

    Types of variables according to their operation

    We must not forget that scientific research always requires to simplify more or less the elements of what we want to study. Identifying what is important to focus on, leaving everything else unclear, is a prerequisite, because otherwise we wouldn’t be able to analyze anything without knowing what kind of data to start with.

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

    As we have mentioned, one of the best known and classic ways of dividing and classifying different variables is in relation to their operability, i.e. the possibility of digitizing their values ​​and working with them. Considering this aspect, 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 does not allow it to be quantified. This type of variable would only inform us of the existence or not of this feature. or the presence of alternatives. They are simply nominal, expressing equality and / or inequality. Gender or nationality would be examples. However, this does not mean that they cannot be observed or that they are not highly relevant in research.

    In the qualitative variables we can find different types.

    Dichotomous qualitative variables

    These are variables in which only two possible options exist or are being considered. Being alive or dead is an example: it is not possible to be alive at the same time, so the presence of one of the values ​​negates the other.

    Polytomous qualitative variables

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

    2. Quasi-quantitative variables

    It is these variables with which it is not possible to perform mathematical operations, but which are more advanced than simply 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 education, being able to determine if someone has more or less of that quality.

    However, there is no consistency in the differences between a category and what precedes it and what follows it (A person with a postgraduate degree knows only one with a bachelor’s degree in the same way that a person with a secondary education knows more than another with only a primary degree).

    3. Quantitative variables

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

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

    Discrete quantitative variables

    These are the set of quantitative variables whose values ​​do not support intermediate values, and it is not possible to get decimals in their measurement (although later averages can be made to include them). For example, it is not possible to have 2.5 children. They usually refer to variables that use reason scales.

    Continuous quantitative variables

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

      According to its relation to other variables

      It is also possible to determine different types of variables based on the relationship between their values ​​and those of others. In this sense, several types stand out, the first two being particularly relevant. It is important to note that the same item can be one type of variable and another depending on the type of relationship that is being measured and what is being changed. In addition, we must keep in mind that the role and type of variable in question depend on what we are analyzing, whatever role the variable actually plays in the situation studied.

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

      1. Independent variables

      By independent variables we mean the variables which are taken into account at the time of the research and which may or may not be modifiable by the experimenter. This is the variable from which to start observing the effects that determine the quality, The characteristic or situation may have on different elements. Examples of independent variables are gender, age, or baseline anxiety level.

      2. Dependent variables

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

      3. Moderation variables

      By moderate variables, we mean the set of variables that they modify the relationship between the dependent and independent variable. An example of this is given if we relate study hours to academic performance, the moderate variables being emotional state or intellectual ability.

      4. Strange variables

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

      It is therefore all this set of uncontrolled variables and taken into account in the situation studied, although it is possible to identify oneself after it or even during an experience or a researched context. They differ from moderators in that foreigners are not taken into account, and moderators are not.

      In other words, strange variables are those that can lead us to erroneous conclusions when interpreting the results of a research, and the impact of their presence depends on the quality of the design of the studies conducted by investigating. on something.

      Types of variables by scale

      Another possible classification of the variables can be made according to the scales and measures used. However, it should be noted that more than the variable, we would speak of the scale in question as a distinguishing feature. It should also be borne in mind that, as the operating level of the scales used is increasing, new possibilities are added to those of the previous scales. Thus, a ratio variable also has the properties of nominal, ordinal and interval. In this sense, we can find the following types.

      1. Nominal variable

      We speak of nominal variables when the values ​​that this variable can reach only allow us to distinguish the existence of a concrete quality, without allowing these values ​​to perform an order or a mathematical operation 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 relations between its values. These are fundamentally qualitative variables. Examples are socio-economic status or level of education.

      3. Interval variable

      In addition to the above functionality, interval scale variables allow build digital relationships between variables, although generally these relationships are limited to proportionality. There is no absolute zero or fully identifiable zero point, which does not allow direct transformations from values ​​to others. They measure ranges, rather than specific values, which complicates their operation but allows a large number of values ​​to be covered.

      4. Reason variable

      The variables of reason are measured in a scale such that its total operationalization is possible, being able to carry out several transformations to the results obtained and establishing complex numerical relations between them. There is a point of origin which assumes the total absence of what is being measured.

      Different ways of analyzing reality

      We must not forget that the different types of variables are always a simplification of reality, a way to break it down into simple, easy-to-measure parameters isolate them from other components of nature or society.

      Therefore, we cannot limit ourselves to believing that knowing these variables is fully understanding what is going on. A critical look at the results obtained from variable studies is necessary in order not to reach erroneous conclusions and not to be closed to more complete and realistic explanations of what is happening around us.

      Bibliographical references:

      • Barnes, B. (1985): On science, Barcelona: Work.
      • 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: University of the Alliance.
      • Sullivan, M. (1998). Trigonometry and analytical geometry. Barcelona: Pearson Education.

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