The types of variables that exist can be classified according to different criteria that we will discuss in this article. A variable is something whose quality or quantity can vary. For example, temperature (a quantitative variable) or sleep quality (a qualitative variable).
In other words, statistical variables are typologies that can fluctuate or vary; said variation can be measured and observed. Likewise, a variable can be understood as an abstract construction that refers to a property or an element, which can develop a specific role in relation to the object that is being analyzed..
This means that said property or element directly influences the subject or object to be studied. The concept of variable seeks to bring together different modalities or options that must be taken into account to understand the object of study.
Consequently, the values of the variables will be inconsistent or different in the subjects and / or moments to be analyzed. Understanding this concept in the theoretical field can be complex.
However, through concrete examples, the approach can be better understood: a variable can be the sex or age of a person, since these characteristics can affect the object of study if an analysis is to be carried out in patients who suffer from heart disease or other illnesses.
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In addition to the operational variables, there is also a classification according to the relationship that exists between the values of these variables. It is necessary to bear in mind that the role played by each type of variable depends on the function that is being analyzed. In other words, the classification of these variations is influenced by the object of study.
Within this classification there are independent, dependent, moderating, strange, control, situational, participant and confounding variables..
These refer to the variables that are taken into account during the research process and that may be subject to modification by the researcher. In other words, they are those variables from which the analyst starts to contemplate and record the effects that their characteristics produce on the object of study..
An example of an independent variable can be sex and also age if you want to make a record of people with Alzheimer's.
It can be established that the independent variable conditions the dependent one. In addition, the independent can be called experimental or causal, since it is manipulated directly by the researcher. Independent variables are used primarily to describe the factors that are causing the particular problem.
They are those that make direct reference to the element that is modified by the variation produced by the independent variable. This means that the dependent variable is generated from the independent variable.
Examples
For example, if we want to determine depression according to sex, the latter will be the independent variable; modifying this will generate fluctuations in the dependent variable, which in this case is depression.
Another example could be found in the relationship between smoking and lung cancer, since "having lung cancer" in this case would be the dependent variable, while "smoking" is an independent variable, since it can vary depending on the number of packs consumed per day.
These variables alter or modify the relationship that exists between a dependent and an independent variable; hence their name, since they moderate the link between the two above.
For example, study hours are related to academic sequelae; therefore, a moderating variable could be the student's state of mind or the development of their motor skills.
The strange variables receive their name because they were not taken into account for the development of the research but they had a noticeable influence on the final results. They are also known as the intervening or puzzling variables, since they can weaken the relationship between the problem and the possible cause..
Consequently, it is a group of variables that were not controlled during the analysis of the object of study, but can be identified once the research is completed, even in some cases they are identified during the course of said study.
They are similar to the moderators, with the difference that these are taken into account at the time of the investigation. Strange variables can also lead the researcher on the wrong path, so the importance of their presence will depend on the quality of the studies undertaken..
For example, a variable of this type may be the fact that nervous people smoke more and have a greater tendency to develop cancer than those who do not suffer from nervousness; the strange or puzzling variable in this case is nerves.
Control variables are those that a scientist wants to remain constant, and must observe them as carefully as dependent variables..
For example, if a scientist wants to investigate the influence of diet (VI) on health (DV), a control variable could be that the people who are part of the study are non-smokers.
This would be the control variable; it is necessary to control it because the observed differences in health could be due to whether or not people smoke. In any case, in an experiment like this there could be other control variables; being an athlete, having other habits ...
A situational variable is an aspect of the environment that can influence the experiment. For example, air quality in a health-related experiment.
A participant or subject variable is a characteristic of the subjects that are studied in an experiment. For example, the gender of individuals in a health study. Also known as participating variables.
A confounding variable is a variable that influences both the independent variable and the dependent variable. For example, stress can make people smoke more and also directly affect their health..
Statistical and research variables can be classified according to their operability, this category being the best known and most useful. When speaking of operability, allusion is being made to the ability to "number" the values of these variables. Consequently, we can subdivide them into three main types:
Qualitative variables are those variations that allow establishing the identification of a specific element, but that cannot be quantified. This means that these variables can inform about the existence of a characteristic but it cannot be valued numerically..
Consequently, these are variations that establish whether there is equality or inequality, as occurs with sex or nationality. Although they cannot be quantified, these variables can contribute forcefulness to the research.
An example of a qualitative variable would be the motivation that students have during the learning process; this variable can be identified but cannot be numbered.
In addition, these can be subdivided into other categories, such as dichotomous qualitative variables and polytomous qualitative variables..
These variables can only be considered or analyzed from only two options; hence the word "dichotomy" is present in its name, since it indicates a division present in two aspects that are usually contrary to each other..
A precise example would be the variable of being alive or dead, since it only allows two possible options and the presence of one of these immediately denies the other..
These statistical variables are the opposite of dichotomous variables, since they allow the existence of three or more values. However, in many cases this prevents them from being sorted, since they only establish the identification of a value.
A precise example is the color variable since, although it allows to identify, it declares that there is only one possible characteristic or element assignable to this variable.
These variables are characterized by making it impossible to carry out any mathematical operation; however, they are more advanced than those that are solely qualitative.
This is because quasi-quantitative ones allow establishing a hierarchy or a kind of order, although they cannot be quantified..
For example, the level of studies of a group of people can be a variable of this type, since the completion of a postgraduate degree is located in a higher hierarchy than the completion of an undergraduate degree..
These variables, as their name implies, allow the performance of mathematical operations within their values; therefore, the different elements of these variables can be assigned numbers (that is, they can be quantified).
Some examples of this type of variable include the following:
-Age, since it can be expressed in years.
-The weight, which can be specified in pounds or kilograms.
-The distance between a given place and the place of origin, which can be expressed in kilometers or minutes.
-Monthly income, which can be expressed in dollars, euros, pesos, soles, among other types of currencies.
In turn, these types of variables can be subdivided into two groups: discrete quantitative variables and continuous quantitative variables..
These refer to quantitative variables that cannot have intermediate values - they do not admit decimals within their number. In other words, they must be numbered through a complete number.
A precise example consists of the impossibility of having 1.5 children; it is only possible to have one or two children. This means that the unit of measurement cannot be fractioned..
On the contrary to the discrete ones, continuous variables can have decimals, so their values can be intermediate.
These variables are measured by the interval scales. In other words, continuous quantitative variables can be fractionated.
For example, measuring the weight or height of a group of people.
In addition to the previous classifications, statistical variables can be cataloged taking into account the function of their scales and the measures that are used to calculate them; However, when talking about these variables, greater emphasis is being placed on the scale than on the variable itself..
In turn, the scales used for the variables may undergo modifications depending on the level of operability, since the latter allows the incorporation of other possibilities within the range of scales..
Despite this, four main types of variables can be established according to scale; These are the following: the nominal variable, the ordinal variable, interval, ratio and continuous.
This type of variables refers to those whose values only allow distinguishing a single specific quality without introducing mathematical operations on them. In this sense, nominal variables are equivalent to qualitative variables.
As an example of the nominal variable, gender can be found, since it is divided into masculine or feminine; as well as the marital status, which can be single, married, widowed or divorced.
These variables are essentially qualitative since they do not allow the performance of mathematical operations; however, ordinal variables do allow establishing certain hierarchical relationships in their values.
An example of a nominal variable can be a person's educational level or economic status. Another example can be the ranking of academic performance by the following adjectives: excellent, good or bad.
Variables of this type are used to classify subjects, events or phenomena in a hierarchical way, considering specific characteristics.
The variables that have scale in interval allow the realization of numerical relations between them, although they can be limited by the proportionality relations. This is because within this range there are no "zero points" or "absolute zeros" that can be fully identified..
This results in the impossibility of carrying out transformations directly in the other values. Therefore, the interval variables, rather than measuring specific values, measure the ranges; This complicates operations somewhat but encourages coverage of a large number of securities..
Interval variables can be presented in degrees, magnitudes, or any other expression that symbolizes quantities. Likewise, they allow to classify and order categories, as well as to indicate the degrees of distance that exist between them..
Within this classification can be found the temperature or the IQ.
This type of variable is measured by a scale that operates in a total way, which does allow the direct transformation of the results that were obtained..
In addition, it also encourages the performance of complex number operations. In these variables there is an initiation point that implies the complete absence of what was measured.
Consequently, the ratio variables do have an absolute zero and the distance between two points is always the same, although they also have the characteristics of the previous variables.
For example, age, weight and height are ratio variables.
A variable with an infinite number of values, such as "time" or "weight".
Categorical variables are those whose values can be expressed through a series of categories that define them..
A good example of a categorical variable corresponds to the consequences of a given disease, which can be broken down into recovery, chronic illness, or death..
A variable that is manipulated by the researcher.
A variable that can only take two values, usually 0/1. It could also be yes / no, high / short or some other combination of two variables.
Similar to an independent variable, it has an effect on the dependent variable, but it is generally not the variable of interest..
Another name for a dependent variable, when the variable is used in non-experimental situations.
Similar to dependent variables, they are affected by other variables within a system. Used almost exclusively in econometrics.
Variables that affect others, and that come from outside a system.
Variables used to uniquely identify situations.
A variable that is used to explain the relationship between variables.
A hidden variable that cannot be directly measured or observed.
A variable that can be directly observed or measured.
Variables that explain how the relationship between variables happens.
Changes the intensity of an effect between independent and dependent variables. For example, psychotherapy can reduce stress levels in women more than in men, so sex moderates the effect between psychotherapy and stress levels.
Variables that can have more than two values.
Similar in meaning to the independent variable, but used in regression and in non-experimental studies.
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