What are Independent and Dependent Variables?
In research, varying parameters like age, height, sex, hours of sleep etc., are termed as variables.
Scientific research aims to establish the influence of one variable on another. For instance, researchers investigated a correlation between extended periods of studying and test scores among students.
The variables under research are the independent and dependent variables that demonstrate a cause-and-effect link.
- The independent variable describes the cause. The value of this type of variable remains unchanged by other variables in your study.
- The dependent variable describes the effect. The value of this variable is determined by the fluctuations induced by the researchers in the independent variable.
Independent and dependent variables example
|
Research questions |
Independent variable |
Dependent variable |
|
Is incandescent, fluorescent, or natural light the fastest way to grow tomatoes? |
The sort of lighting that the plant receives |
The plant’s rate of development |
|
What effect do regular and diet soda have on blood sugar levels? |
What type of soda is being consumed? (Either diet or regular) |
Blood sugar level |
|
What is the effect of screentime on quality of sleep at night |
The amount of time spent on the smartphone before heading to sleep |
Sleep hours Sleep quality |
|
What is the tolerance of various species of plants to saltwater? |
The plants are given water mixed with a certain proportion of salt. |
Plant development Wilting rate of plants The survival rate of plants |
The researcher changes or adjusts the independent variable during the scientific investigation to observe the independent variable’s impact on the dependent variable and then assesses the result.
As an example
You’re looking into the effectiveness of a new medication to treat hypertension in patients with blood pressure levels.
The patients are placed into two groups to see if the therapy is working. Then, the new medication is administered to one group, while the other group is given a sugar pill called a placebo.
- The type of medication that the patient gets is the independent variable, which you adjust across groups.
- The blood pressure measurements of the individual, which you are monitoring, are your dependent variable.
The independent variable is frequently utilised at multiple stages to examine how the result fluctuates.
To determine whether or not the independent variable has an influence, merely two stages can be used (for instance, the new treatment and the placebo pill).
You can also try several levels (for instance, three separate and distinct dosages of the new treatment) to see how the independent variable influences the research’s dependent variable.
Different kinds of research variables
It is not possible for researchers to directly manipulate the independent variable under study outside of an experimental context.
Instead, researchers should look for existing occurrences of the independent variable and analyse how these changes impact the dependent variable.
Illustration
You’re looking to understand how raising the minimum wage influences employment rates. Unfortunately, as a researcher, you have no control over the minimum wage. So instead, you make your case by contrasting a state that previously increased its minimum wage with one that did not increase the minimum wage.
- Independent variable in the above example is the workers’ minimum wage.
- Dependent variable in the above example is the employment rate.
You can see if a change in the minimum wage affects employment rates among workers by looking at the differences in outcome measures between the two states.
In the case of non-experimental research, establishing a clear cause-and-effect link can be much more complicated since other variables that weren’t considered might influence the study’s outcomes. Such types of factors are referred to as confounding variables.
In many forms of study, you could use alternate terminology for independent and dependent variables whenever the precise link between variables is less specific.
Other terms denote independent variables.
Other variables may impact the variable you think to be the source. One of the following terms is better acceptable in this situation:
- Explanatory variables (exemplify an event or condition)
- Predictor variables (The value of the dependent variable can be predicted with the help of predictor variable)
- Right-hand-side variables (these are found on the right-hand side of linear regression).
Other terms to denote dependent variables
These expressions can also be used to express dependent variables in research:
- Outcome variables: These types of variables represent the result you are looking to quantify.
- Response variables: These types of variables are used to respond to a modification in another variable
- Left-hand-side variables (these types of variables are found on the regression model’s left-hand side)
Making a graphical representation of independent and dependent variables
Researchers usually present their findings in the form of charts or graphs. When representing a graph, the “X” or horizontal axis is used to plot the independent variable, while the “Y” or vertical axis is reserved for the dependent variable.
Frequently Asked Questions
Is it possible for a variable to be both dependent and independent simultaneously?
No. A variable can’t be both dependent and independent simultaneously since the values of an independent variable influence the attributes of a dependent variable. So it can’t be both at the same time!
What is the significance of independent and dependent variables?
Unravelling and identifying the cause and effect link among variables in research is perhaps the most essential aspect of any scientific research. It is critical to comprehend both the cause, i.e. independent variable and the impact, i.e. dependent variable.
Is it possible to have more than a single independent or dependent variable?
Yes, however, having more than one necessitates the development of several research questions.
What is a confounding factor?
A confounder is a variable that affects both the independent and dependent variables, resulting in a misleading correlation.
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