In the field of research, a variable is an object, idea, or any other characteristic that can take any value that you are attempting to measure. Age, blood pressure, height, exam score, sea level, time, and so on are all variables.
In an experiment, there are two types of variables: independent variables and dependent variables.
Definition of an independent variable
An independent variable (IV) is one that stands on its own. The impact of any other variable has no effect on the value. The independent variable is manipulated or changed by the researcher in order to assess its impact on other variables.
Age is an example of an independent variable that is not dependent on any other variable.
Definition of dependent variable
Similarly, as the name implies, a dependent variable (DV) is dependent on other variables. The variable is the one being tested in the experiment. A researcher evaluates the experiment's results to determine how other variables influence the value of a dependent variable.
Take "Test Scores" as an example.
You want to see how studying or sleeping affects a test score. The dependent variable in this example is "test score." The independent variable is "studying" or "sleeping" because these factors influence how well a student performs on the test.
So, in the experiment, you're attempting to determine whether and how one variable influences the other. You can change the independent variable (studying time) to see if the dependent variable (test score) changes.
Difference between Independent and Dependent Variable
The simplest way to determine which variable in your experiment is the Independent Variable (IV) and which is the Dependent Variable (DV) is to put both variables in the sentence below in the correct order.
"A change in the DV is caused by the IV." It is impossible for DV to cause any change in IV."
When you change the Independent Variables in an experiment, your goal is to measure the changes in the Dependent Variables.
Remember that changes in the independent variable have an effect on the dependent variable.
Examples of Independent and Dependent Variables
Let's look at some examples of dependent and independent variables to better understand their characteristics.
How does the amount of sleep affect test results?
1. Independent Variable: Sleeping time prior to the exam
2. Test Score is the Dependent Variable
What effect does fast food have on blood pressure?
1. Independent variable: Fast food consumption
Blood Pressure is the dependent variable.
What effect does caffeine have on sleep?
1. Independent variable: caffeine consumption
2. Dependent Variable: Sleep
In Experiment – Independent and Dependent Variables
The independent variable in an experiment is the characteristic that the researcher manipulates to determine the effect of the changes on the dependent variable.
It is important to note that changing the independent variable always results in a change in the dependent variable.
For example, you might want to look into the impact of listening to classical music on math test scores.
To observe changes in test scores, students are divided into two groups. For two months, students in Group A listened to classical music for an hour every day. Group B students were not instructed to listen to classical music.
After two months, students from both groups took a math test. It was discovered that Group A outperformed Group B.
The dependent variable in the experiment was the math exam test score, and the independent variable was exposure to classical music.
While the most common study has one independent variable and one dependent variable, it is also possible to have a different level of each variable in an Experiment.
As a researcher, you may be interested in learning how a single Independent Variable can influence two distinct dependent variables.
For example, you might conduct an experiment to learn how video games affect a teenager's memory and mood. While playing video games is the independent variable in the experiment, the teenager's memory and mood are the two dependent variables.
Independent variables can also have different levels. In some experiments, multiple independent variables may be required to examine the various effects they may have on a single dependent variable.
For example, you might be interested in learning how a healthy diet can aid in weight loss. As a result, you will investigate various types of healthy diets and their effects on weight.
Different types of diet are your different levels of the independent variable in this case, while weight loss is the outcome that makes it the dependent variable.
Applying two levels of IV can reveal whether or not it has an effect on the DV.
Using multiple levels of IV can demonstrate how it affects the outcome of DV.
It is not always possible to change the independent variable in experimental research.
For example, you might want to investigate whether age influences weight gain.
Your Independent Variable is your age.
Your dependent variable is weight gain.
You can't change the age of the people you're studying to figure out how it affects weight gain. So you compare the factors that affected weight and those that did not.
By comparing the differences in the other factors, you can learn about a person's weight changes caused by their age.
Risks to Be Aware Of
Other variables that may influence the outcome of your experiment, in addition to dependent and independent variables, must be considered.
Extraneous variables can have an impact on the relationships between Independent and Dependent variables. Researchers try to identify these variables so that they can be controlled.
Confounding variables are those in research that cannot be controlled. Other variables may exist in non-experimental research that you have not identified. These variables may have an impact on the outcome.