In order to know independent and dependent variables, one should know what variables are. Variables are properties or characteristics of some event, object, or person that can take on different values or amounts. When researchers are conducting research, they often manipulate variables.
Now, let us discuss independent and dependent variables in detail:
Independent variable(s) are the variables or alternatives that are manipulated (i.e. the level of these variables are changed by a researcher) and whose effects are measured and compared. They are also called Predictor(s), as they predict the values of the dependent variable or predicted variables in the model. In layman’s language, the independent variable is a variable that stands alone and is not changed by the other variable one is trying to measure. For example, while looking at someone’s age, variables like what a person eats, how much he watches television etc... do not change the person’s age. That is why they are called the other variables. In fact, when one is looking for some kind of a relationship between the variables, then one is trying to see if the independent variable causes some kind of change in the other variables.
The other variable(s) can also be called dependent variable(s). As the name suggests, they are the variables that measure the effect of the independent variable(s) on the test units. In layman’s language, the dependent variables are the variables which are completely dependent on the independent variable(s). They are also called Predicted variable(s) as they are the values to be predicted or assumed by the predictor / independent variables. For example, a student’s score could be a dependent variable because it could change depending on several factors such as how much he studied, how much sleep he got the night before he took the test, or even how hungry he was when he took the test. Usually, when one is looking for a relationship between two things, one is trying to find out what makes the dependent variable change the way it does.
Independent variables are also called “regressors,” “controlled variable,” “manipulated variable,” “explanatory variable,” “exposure variable,” and/or “input variable.” Similarly, dependent variables are also called "response variable," "regressand," "measured variable," "observed variable," "responding variable," "explained variable," "outcome variable," "experimental variable," and/or "output variable."
A few examples can highlight the importance and usage of dependent and independent variables in a broader sense:
If one wants to measure the influence of different quantities of nutrient intake on the growth of an infant, then the amount of nutrient intake can be the independent variable, while the dependent variable can be the growth of an infant measured by height, weight or other factor(s) as per the requirement of the experiment.
If one wants to estimate the cost of living for an individual, then factors such as salary, age, marital status etc. are independent variables. The cost of living for a person is highly dependent on such factors, hence can be designated as the dependent variable.
In the case of the time series analysis, forecasting a price value of a particular commodity is again dependent on various factors as per the study. Suppose we want to forecast the value of gold, for example. In such an instance, the seasonal factor can be an independent variable on which the price value of gold will depend.
In the case of a poor performance of a student in an examination, the independent variables can be the factors like the student not attending classes regularly, the student having poor memory etc., which can reflect the grade of the student. Here, the dependent variable is the test score of the student.
