For the description and measurement of places, items, or ideas under study, the researchers use various types of variables. It is important for the researchers use the right type during the design of studies, selection of tests, and the interpretation of results. So, it is very important to understand the various types of variables.
Variable
In the field of education, a variable can be defined as anything or any situation that varies from learner to learner.
In the field of mathematics, a variable is defined as a quantity that is changeable in a mathematical problem or experiment.
Simply put, a variable is any quantity that takes a variation of values in a certain problem.
So from the above definitions, we conclude that variables are values that change from situation to situation. For example, age, sex, income, class grades, etc. Before explaining different variables and their 18 types, it is important to understand the concept of variables in research as well.
Variables in research
When any quantity, number, or characteristic takes different values in different situations, it is called a variable. For example, during conducting experiments in the field of education, the teacher researcher might compare the effects of different teaching methods on the learning of the students. In such situations, the variable is the type of teaching method.
Types of variables
The different types of variables are explained below.
Categorical variable
A variable whose categories can be formed is called a categorical variable. For example, the brands of toothpastes
Qualitative variable
The quantitative variable is that which expresses the qualitative attributes such as gender, color, religion, payment method, and so on. It is not a numerical value.
Quantitative variable
Also called a numerical variable, which is measured with numbers, is a quantitative variable. They are usually used in quantitative research. For example, the age of a person, family size, etc. The size of a family is a quantitative variable, as the number of members of a family may be different from the number of members of another family. It is worth noting that they are expressed in numbers. The quantitative variables are again categorized into two types: discrete and continuous.
1. Discrete variables:
Discrete variables adopt certain numbers that cannot be subdivided and result in integers. For example,
- The number of books one checks off the shelf
- The number of heads from throwing a coin a number of times
- The population of a country
- The number of branches of a bank in a particular city
1. Continuous variables
They have infinite values between two numbers. For example,
- date
- time
- blood pressure of a person
- body temperature of a person
The continuous variable assumes countless values in a range as they result from a measurement.
Independent variables
Independent variables are those variables that the researcher is able to manipulate or change in an experiment for the purpose of exploring their effects. Since they are not affected by other variables in the study, they are therefore called independent variables. The other names of this variable are:
Predictor variable: The independent variable is also called a predictor variable because it forecasts the value of the dependent variable.
Explanatory variables:
since they explain an event or the outcome, that is why they are called explanatory variables.
Right-hand side variable(s):
Since they appear on the right side of the regression equation, they are therefore called right-hand-side variables.
Dependent variable
A variable that changes as a result of the change in the independent variable(s) is called the dependent variable. Since dependent variables depend on independent variables, they are called dependent variables.
The dependent variable is also called:
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Response variable:
It is because it responds to changes in the independent variable(s).
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Outcome variable:
It represents the product one wants to measure.
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Left-hand-side variable/s:
The dependent variable is called the left-hand side variable because it appears on the left side of the regression equation.
The dependent variable in research is recorded after the manipulation of the independent variable(s) and is checked to see to what extent the independent variable(s) have influenced the dependent variable.
Background variables
They are also called demographics. They consist of information about the background of the respondents to the research study. For example, name, age, gender, economic status, qualification, experience, etc. They are related to independent variables. They are included depending on the requirements of the research study.
Moderating variable
Those that affect the relationship between independent and dependent variables are called moderating variables.
During the regression analysis, the researchers are interested in finding out how the independent variables affect the dependent variable. But it is possible that another variable, called the moderating variable, may affect the causal relationship between independent and dependent variables as well. For example, if we want to find out the effect of hours spent exercising each week on the resting heart rate of individuals, there is a chance that the results obtained will be affected by the moderating variable of gender or age.
Extraneous variable
Any uncontrollable factor that affects the results of an experiment is referred to as an extraneous variable. So it is any variable other than the independent variable that affects the outcome of the experiment. It can take the form of characteristic of a person such as gender, age or environmental factor such as noise lighting, etc. It is not necessary that they be part of the study.
Types of extraneous variable(s)
There are various types of extraneous variables, which are discussed in the underlying paragraphs.
1. Situational variable
Such types are environmental factors affecting the way a subject is behaving in an experiment. For example, lighting, noise, temperature, etc.
2. Participant variable
It is a situation where the personal feelings of the participants affect the results of the study. For example, physical and mental capacities of the individuals, etc.
3. Experimenter variable
When the behavior of the researcher or experimenter affects the results of the study, it is referred to as experimenter variable. For example, consider the biased attitude of the experimenter or researcher.
4. Demand Characteristic Variable
When clues regarding the outcomes of the study are conveyed to the participant, or he or she comes to know about the results of the study, it is called the demand characteristic variable. For example, the items of the questionnaire or the behavior of the researcher can affect the results of the study.
Confounding variable
The confounding variable is the kind of extraneous variable that is related to the independent and dependent variables of the study. Even after correctly identifying the causal relationship between independent and dependent variables, the confounding variable can overestimate or underestimate the effect. When, due to uncontrolled extraneous variables, the dependent variable is interfered with, it is called a confounding variable. For example, the type of music people listen to has a correlation with their productivity. The type of music here is an independent variable and can be changed easily by the researcher. While the productivity level is the dependent variable, Sometimes they can be interchanged.
Intervening variable
They are the hypothetical constructs that explain the causal relationship between independent and dependent variables. So it is a variable that comes between the independent and dependent variables and explains the variation in the dependent variable due to the independent variable.
Types of intervening variable
There are two types of intervening variables, namely the mediating variable and the moderating variable.
Mediating variable
When a mediating variable occurs, the independent variable affects the mediating variable, and the mediating variable in turn affects the dependent variable. For instance, the quality of sleep (independent variable) affects academic achievement (a dependent variable) through the mediation of alertness.
Moderating variable
Such types of intervening variables influence the direction or strength of the relationship between independent and dependent variables. For example, sleep quality and academic achievement may be moderated by the mental health status of a person.
Suppressor variable
In some cases, the true relationship between the two variables may not be exhibited because of some hidden factors. These hidden factors may suppress the true relationship. So these hidden factors are called suppressor variables. If the researcher is able to control this variable, the true relationship will reappear.
Lurking variable
A variable that, although not included in the analysis, can influence the interpretation of the results is called a lurking variable.
Antecedent variable
Variables that come before the independent variable are called antecedent variables.
Binar variable
A binary variable, also called dichotomous variable, is a variable which takes only two values, usually 0 and 1, yes/no, etc.