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A variable in the field of research is an object, idea, or any other characteristic which can take any value that you are trying to measure. A variable can be age, blood pressure, height, exam score, sea level, time, etc.
There are primarily two types of variables used in an experiment – Independent Variables and Dependent Variables.
As per the name, an independent variable (IV) stands alone. The value does not change due to the impact of any other variable. The researcher manipulates or changes the independent variable to measure its impact on other variables.
Independent variables, in some cases, can already exist, like age, but it is not dependent on any other variable. The variable plays a significant role in research by assisting you in examining causal relationships and testing hypotheses. By manipulating this variable, you can determine its impact on DV and establish a cause-and-effect relationship.
Similarly, a dependent variable (DV) as the name suggests, depends on other variables. It is the variable that is being tested in the experiment. A researcher measures the outcome of the experiment to see how other variables cause changes in the value of a dependent variable.
The causal relationship between dependent & independent variables in research studies helps you assess the effects, associations, and correlations.
The IV is the variable you can manipulate or control in research. You can deliberately change is to observe its impact on the dependent variable. Whereas DV is the variable you observe/ analyze. You examine the changes in the dependent variable in response to the changes in the independent variable.
The most common independent and dependent variable identifier is:
“Which variable is being intentionally manipulated?”. This question can help you identify the independent variable.
“What is the response being observed?” This will help you identify the dependent variable.
Let’s use “Test Scores” as an example of independent and dependent variables in research studies.
You want to see the effect of studying or sleeping on a test score. In the example, “test score” is the dependent variable. “Studying” or “sleeping” is the independent variable because these factors impact how much a student scores on the test.
So, in the experiment, you are trying to determine if and how one variable affects the other. Here you can manipulate the independent variable (time of studying) to see if the dependent variable (test score) changes or not.
Another independent and dependent variable identifier is the characteristics of both variables.
IV possesses the following characteristics as the presumed causes of the outcome you investigate in the research.
DV possesses the following characteristics as the outcomes that researchers observe to assess the impact of IV.
These two methods are some commonly used independent and dependent variable identifiers.
The easiest way to identify which variable in your experiment is the Independent Variable (IV) and which one is the Dependent Variable (DV) is by putting both variables in the sentence below in a way that makes sense.
“The IV causes a change in the DV. It is not possible that DV could cause any change in IV.”
In an experiment, when you make changes in the Independent Variables, your aim is to measure the changes it causes to the Dependent Variables.
Remember that the dependent variable is affected by the changes you make in the independent variable.
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Let’s explore some examples of dependent and independent variables to understand their properties better.
Here are more examples of independent and dependent variables in research studies in various fields.
In social science research, you can use IV and DV to understand and assess various aspects of human behavior, social phenomena, etc.
Here are some IV and DV examples in social surveys.
In medical/healthcare research, you can use IV and DV to evaluate the effectiveness of medical interventions and treatments.
Here are some IV and DV examples in medical surveys.
In the field of MR, you can use IV and DV to study consumer behavior and market trends.
Here are some IV and DV examples in market research.
B. Examining consumer perception (dependent variable) in response to the change in price (independent variable) of the product.
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In an Experiment, the independent variable is the characteristic manipulated by the researcher to gauge the effect of the changes on the dependent variable.
Note that the resulting change in the dependent variable is always measured by altering the independent variable.
Here we will look into several dependent and independent variables in research examples.
You want to determine the effect of exposure to classical music on the test scores on math.
To see the changes in the test score, divide students into two groups.
After two months, students of both groups were given a math test. It was seen that Group A performed better than Group B.
In the experiment, test score in math exam was the dependent variable, and the exposure or lack thereof to classical music was the independent variable.
In an Experiment, 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.
For example, you run an experiment to learn how playing video games impacts a teenager’s memory as well as their mood. In the experiment, while playing video games is your independent variable, the teenager’s memory and mood are your two dependent variables.
For example, you want to see how a healthy diet can help with weight loss. So, you will look for several types of a healthy diet and their impact on weight. In this case, different types of diet would be your different levels of the independent variable, while weight loss is the outcome that makes it the dependent variable.
In this example, you want to observe if there is any cause-and-effect relationship between age and weight gain.
The dependent and independent variables in this research example differ slightly. Here, you cannot control the age of the people you are studying to understand its impact on weight gain.
So, you compare the factors that had an effect on weight and the factors that did not. Comparing the difference in the other factors, you can learn the changes in a person’s weight caused by their age.
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Aside from dependent and independent variables, you must be aware of other variables that may influence the result of your experiment.
Extraneous Variables: may influence the relationships between the Independent and the Dependent Variable. Researchers try to identify these variables in order to control them.
Confounding Variables: are those variables that cannot be controlled in research. In non-experimental research, there may be other variables that you have not identified. These variables may be influencing changes in the outcome.
In this blog, we have shared many examples of independent and dependent variables in research studies and highlighted their characteristics.
Understanding independent and dependent variables is significant in research as it ensures the validity and reliability of the research. The two variables help you establish cause-and-effect relationships and draw meaningful conclusions.
In the blog, we have also mentioned the independent and dependent variable identifiers so you can delve deeper into the world of research.
The main difference between Independent & Dependent Variables is in the definition.
The best way to identify dependent and independent variables in research is by putting the variables in the sentence “The Independent Variables cause a change in Dependent Variable”.
Variables are characteristics that take on different values. In experimental research, there are three types of Variables: Independent, Dependent, and Controlled Variables.
When you try to boil a potato, as the temperature of water rises the potato boils faster. Here, the temperature of the water is an Independent Variable and Potato is the Dependent Variable.
An independent variable is often denoted by “x.” It is a variable whose value does not depend on another variable.
An independent variable is the factors or conditions that you can manipulate in an experiment. This variable can directly affect the value of a dependent variable.
A Dependent variable is denoted by “y.” It is a variable whose value depends on that of an independent variable.
In an experiment, a dependent variable is a factor that is observed or measured. When you vary an independent variable, you observe the change in your dependent variable.
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