por The variable types that exist can be classified according to different criteria. 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, variables are typologies that can fluctuate or vary, and that can be measured and observed. Many variables change over time, and their type depends on the data they represent. For example, weight is a quantitative variable (something weighs so many grams, kilos, tons, etc.), but when talking about «heavy» or «light», it will be qualitative.
Types of variables according to the relationship with other variables
There is a classification according to the relationship that exists between the values of these variables. It is necessary to take into account that the role that each type of variable plays 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, moderator, extraneous, control, situational, participant, and confounding variables.
Independent variables
They are variables that do not depend on another variable. They are represented on the abscissa axis (x-axis).
Example
An example of an independent variable can be sex or age, if you want to make a record of people with Alzheimer’s.
It can be established that the independent variable conditions the dependent. In addition, the independent can be experimental or causal, since it is manipulated directly by the researcher. Independent variables are mainly used to describe the factors that are the cause of the particular problem.
dependent variables
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
If you want to determine depression according to sex, the latter will be the independent variable; modifying it 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 cigarettes consumed per day.
moderator variables
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 previous ones.
Example
Study hours are related to academic sequelae, therefore, a moderating variable could be the student’s mood or the development of their motor skills.
foreign variables
Strange variables are those variables that were not taken into account for the development of the research but had a noticeable influence on the final results. They are also known as intervening or confounding variables, as they can weaken the relationship between the problem and the possible cause.
It is a group of variables that were not controlled during the analysis of the object of study, but can be identified once the investigation is finished, even in some cases they are identified during the course of the study.
They are similar to the moderators, with the difference that the latter are taken into account at the time of the investigation. Extraneous variables can also lead the researcher down the wrong path, so the importance of their presence will depend on the quality of the studies undertaken.
Example
For example, such a variable may be the fact that nervous people smoke more and are more likely to get cancer than people who do not suffer from nervousness. The strange or puzzling variable in this case is nerves.
control variables
Control variables are those that a scientist wants to remain constant, and he must watch them just as carefully as the dependent variables.
For example, if a scientist wants to investigate the influence of diet (VI) on health (VD), a control variable could be that the people who are part of the study are non-smokers.
This would be the control variable; it needs to be controlled for 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, etc.
situational variables
A situational variable is an aspect of the environment that can influence the experiment. For example, the air quality in an experiment related to health.
participating variables
A participant or subject variable is a characteristic of the subjects being studied in an experiment. For example, the gender of individuals in a health study.
confounding variable
A confounding variable is a variable that influences both the independent variable and the dependent variable. For example, stress can cause people to smoke more and also directly affect their health.
Types of variables according to the operation
Statistical and research variables can be classified according to their operability, this category being the best known and most useful. When speaking of operability, reference is made to the ability to «numeralize» the values of these variables. They can be divided into three main types:
qualitative variables
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 cannot be valued numerically.
Therefore, these are variations that establish whether there is equality or inequality, as occurs with sex or nationality. Although they cannot be quantified, they can provide forcefulness to the investigation.
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.
Dichotomous qualitative variables
These variables can only be contemplated or analyzed only from two options, usually contrary to each other.
Example
A precise example would be the variable of being alive or dead, since it only allows two possible options and the presence of one immediately negates the other.
Polytomous qualitative variables
These statistical variables are the opposite of dummy variables, since they allow the existence of three or more values.
Example
Examples are the results of an exam: outstanding, pass, fair, fail. Position achieved in a competition: 1, 2, 3, etc.
Quasi-quantitative variables
These variables are characterized by making it impossible to carry out any mathematical operation. However, they are more advanced than those solely qualitative.
This is because the quasi-quantitative ones allow establishing a hierarchy or a kind of order, even though they cannot be quantified.
Example
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 rank of hierarchy than the completion of an undergraduate degree.
Quantitative variables
These variables allow the performance of mathematical operations within their values, so numbers can be assigned to the different elements of these variables (that is, they can be quantified).
Some examples of this type of variable include the following:
The age, since it can be expressed in years.
The weight, which can be quantified in pounds or kilograms.
The distance between one place and another, 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, this type of variables can be subdivided into two groups: discrete quantitative variables and continuous quantitative variables.
Discrete quantitative variables
They refer to quantitative variables that cannot have intermediate values —they do not admit decimals within their number.
Example
A precise example is 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 divided.
Continuous quantitative variables
Continuous variables can have decimals, so their values can be intermediate. These variables are measured by interval scales. In other words, continuous quantitative variables can be fractionalized.
Example
For example, the measurement of the weight or height of a group of people.
Variables according to their scale
Statistical variables can also be categorized taking into account the function of their scales and the measures used to calculate them. However, when talking about these variables, greater emphasis is placed on the scale than on the variable itself.
The scales used for the variables can undergo modifications, depending on the level of operation, 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: the nominal variable, the ordinal variable, the interval variable, the ratio variable, and the continuous variable.
nominal variable
They are the variables whose values only allow distinguishing a single specific quality without introducing the performance of mathematical operations on them. In this sense, nominal variables are equivalent to qualitative variables.
Example
Marital status, which can be single, married, widowed or divorced.
ordinal variable
These variables are essentially qualitative since they do not allow the performance of mathematical operations; however, ordinal variables do allow certain hierarchical relationships to be established in their values.
Example
An example can be the level of studies of a person or their economic status. Another example can be the ranking of academic performance using the following adjectives: excellent, good or bad.
interval variable
Variables that have an interval scale allow the realization of numerical relationships among themselves, although they may be limited by proportionality relationships. 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 performing transformations directly on the other values. Therefore, interval variables, rather than measuring concrete values, measure ranges. This somewhat complicates the operation but encourages…