por The research variables They are the different characteristics or properties of living beings, objects or phenomena that have the particularity of undergoing changes and that can be observed, measured, analyzed and controlled during the research process.
Some examples of variables are the socioeconomic condition of a population, place of residence, political preferences, level of education, gender, age, radiation level, ambient temperatures, or levels of polluting gases.
A variable is a property of the object of study that can assume two or more values (that is, it can change). So, if this does not happen, the observed characteristic is not a variable but a constant.
For example, in an investigation you want to know how the levels of solar radiation (independent variable) affect the growth of a plant (dependent variable). As you can see, both variables can have two or more values, and it is expected that as one changes (solar radiation), the other (plant growth) changes.
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The definition of variables
One of the most important steps in scientific research is the definition of variables. This happens because it is not possible to carry out an investigation without specifying and strictly defining the variables that are going to be studied.
The definition of variables in scientific research is one of the most complicated tasks that the researcher must carry out. This process must be carried out very strictly, because only in this way will the researcher achieve the objectives set.
The researcher specifies the study variable and determines how it will be measured or evaluated. Once this process is completed, the data collection instruments are prepared.
Some examples of variable definitions:
-ANDdad: time in years elapsed from birth to the date of the interview.
–Origin: place where the interviewee or patient resides.
–Fever: finding of body temperature (axillary), greater than or equal to 38 degrees Celsius (ºC).
-Degree of dehydration of an individual: refers to fluid loss, which according to the clinical scale can be mild, moderate or severe.
Classification of research variables
– Types of variables according to their nature
Depending on their nature, variables are classified as qualitative or quantitative.
quantitative
They are those variables that can be measured or subjected to counting. For example, the number of inhabitants in a region or the number of people in a theater.
In addition, quantitative variables are classified as continuous and discrete.
continuous variables: are those that can take fractional or decimal values. For example, the degree of temperature of the human body, which can be 37°C or 37.5°C.
Discrete variables: are those that take integer values. For example: the number of people in a theater can be 100, but it will never be 100.5 people.
qualitative
They are the variables that represent an attribute of the individual or the object in question, so their representation is not numerical. For example: the gender or type of diet of a group of children.
Qualitative variables differentiate two or more aspects of the object of study and can be dichotomous and polychotomous.
Dichotomous variables: are those that express two possibilities for the characteristic studied. Examples: gender (female or male), types of schools (public or private).
Polychotomous variables: display more than two characteristics. Example: the socioeconomic stratum of a population, which can range from class 1 to class 5.
– Types of variables according to its complexity
Depending on the degree of complexity, the variables are classified as simple or complex:
simple
They are the variables that are expressed directly through a number or a quality. For example, gender manifests itself in two modes: masculine or feminine; the age is expressed in the years that have been completed.
complex
These variables are initially broken down or divided into several generalities, because they cannot be studied as a whole, therefore each part is defined individually. These will be exemplified in the examples section.
– Types of variablesaccording to their role or relationship
Depending on their relationship with other characteristics of the object of study, the variables can be independent, dependent, intervening or confusing.
independent
They are those that cause changes in other variables. Independent variables are used or manipulated by the researcher to explain the observed phenomenon. Example: the type of exercises to which a therapist subjects patients to see their impact on obesity.
dependents
They are the variables modified by the action of the independent variable. They are the ones that are measured and originate the results of the investigation. Example: the body weight of patients after performing the indicated exercises for a certain time.
Intervening or mediators
These variables stand between the independent and dependent variables, being able to intervene in the response of the latter. They must be identified and controlled so that the results obtained come from the independent variable. For example: the type of food that patients who perform the exercises eat.
confusing or strange
These variables affect both the dependent variable and the independent ones. For example: hereditary factors that affect the body weight of people who perform the exercises.
– Types of variablesaccording to the level of measurement
Variables in this category are classified as ordinal, nominal, interval, and ratio.
ordinals
In these variables an order is established in the values or characteristic that they define. Example: the grades or grades of a student body, which are established from lowest to highest score; or the degree of schooling, which can be established from basic education to university.
As can be seen, in this type of variables the values or properties indicate hierarchies. So when numbers are used, the values are not arbitrary but represent the order of the observed attribute.
interval
In these variables, the elements that compose them are classified into categories that follow an order or degree. In this way, the differences between two consecutive values do not vary, that is, they are established at equal intervals.
Likewise, the zero value in this case is considered a reference value, but it does not indicate the absence of the attribute.
For example, the height of the mountains having the sea level as a reference. In this case the zero value assigned to the sea is arbitrary.
Of reason
These variables have the properties of ordinals and intervals. But in this category the zero value is real and represents the absence of the feature. For example, the number of children in a family. In this case, the value “zero children” would indicate the absence of children.
Examples of research variables
–continuous quantitative: Measurements of people’s weights in kilos, which can be a whole number like 50 kilos or a fraction like 55.5 kilos.
–Discrete quantitative: The number of students in a class, which will always be a whole number like 50 or 100.
–dichotomous qualitative: the types of vehicles. This variable can be divided, for example, into two varieties: racing cars and passenger cars.
–Polychotomous qualitative: the degree of dehydration of a person, which can be mild, moderate or severe.
-Simple: the color of the eyes (black, blue, brown) or the favorite flavor of ice cream (strawberry, vanilla, ice cream).
-Complex: An example of the use of these variables is the evaluation of the quality of service provided by a place that sells food and has a small restaurant.
In this case, the variable is the quality of service throughout the premises. But since it is very broad, it is broken down according to the main areas that pay attention to the public.
In this example you can establish the divisions of the variable and the ways in which they will be measured:
-Quality of service in the sweets and ice cream sales area: responsibility and courteous treatment will be evaluated.
-Quality of service in the restaurant area: the quality of the food and the speed of the service will be evaluated.
-Quality of service in the delicatessen sales area: cleanliness and friendly treatment will be evaluated.
-Independent, dependent, intervening or confounding variables
A teacher applies a new mathematics learning methodology to a group of students in order to increase their interest in this science.
In this example, the independent variable (VI) is the learning technique applied and the dependent variable (VD) is the increase in student interest in mathematics; while the intervening variable could be excessive homework in other subjects or the possible existence of cognition factors that impair learning in certain students.
-Ordinals: Examples of this variable are the different ranks of university professors or the degrees of the military career. In both cases an order is established.
-Of intervals: An example of this variable is the measurement of the ambient temperature in ºC. 0ºC is included in this measurement scale, which does not indicate the absence of temperature, since this value is considered one more reference value.
The values in this example can go from positive to negative, such as: 24ºC can go through the value 0ºC and reach negative values such as -20 ºC.
-Of reason: Examples of these variables are the measurements of income or productions. A family group can make an investment of 400,000 monetary units and have an income of 450,000, which would imply a profit of 50,000 monetary units.
In addition, in these variables there is an absolute zero, since a family can also have an income equal to the investment, with the profit equal to zero monetary units.
Themes of interest
Dependent and independent variable.
Scientific method.
Types of research.
References
Glasser, (2008). Research Methodology for Studies of Diagnostic Tests. Retrieved on May 6, 2020 from: researchgate.net
Coldit, G. (2002). Improving standards of medical and public health research. Retrieved on May 6, 2020 from: jech.bmj.com
Mousali, (2015). Quantitative Research Methods and Designs. Retrieved on May 7, 2020 from: researchgate.net
Wolff, B., Mahoney, F., Lohiniva, A., Corkum, M. (2018). Collecting and Analyzing Qualitative Data. Retrieved on May 8, 2020 from: cdc.gov
Coronado, J. (2007). Measurement scales. Retrieved on May 7, 2020 from: dialnet.unirioja.es
Orlandoni, G. (2010). Measurement scales in statistics. Retrieved on May 6, 2020 from: researchhgate.net