If you’re about to conduct an experiment or write a research paper, chances are, you may need to familiarize yourself with the terminology and research basics that come as part of the scientific package.
And the difference between dependent and independent variables is one of those.
While not every academic paper includes variables – for example, a literature-based analysis looks quite different from that of a physicist – it’s common to orient one’s work around variables in scientific and statistical fields.
Here’s everything you need to know about dependent vs independent variables!
Dependent VS Independent Variables: Overview
The main purpose of variables in research is to explore a correlation and/or potential causation between two different things, which can be anything from height or gender to temperature, cortisol levels, or income.
For example, let’s say you want to research how stress impacts memory. You’ve got two variables here: stress on the one hand and memory on the other.
Stress is your independent variable. This means that it’s independent of other variables in your study. It’s the cause.
Memory is your dependent variable. Its value is reliant on the changes to the independent variable. If you’re severely stressed, your memory becomes impaired. This means that the dependent variable is the effect.
RELATED READ: How to write a strong hypothesis
Independent Variables
In order to make research as clear as possible, independent variables (sometimes referred to as predictor variables or factors) are further sorted into two different categories.
Experimental (or manipulated) variables can be directly manipulated by the researchers.
For instance, a study can measure if music tuned to 440 Hz has different health effects on the body than music tuned to 432 Hz.
The music is an experimental independent variable as it can be manipulated in order to see different results (health effects).
Subject variables, on the other hand, cannot be manipulated by researchers because they are fixed. This includes variables like socioeconomic status or gender.
Studies of this kind can look at a subject independent variable – such as socioeconomic status – and research how it impacts the dependent variable – such as academic performance.
Dependent Variables
Dependent variables are extremely important as they are essentially the results you’re trying to obtain in your study. Based on the changes or different characteristics of the independent variable, you are measuring the changes to the dependent variable.
Dependent variables can go by different names:
- Outcome variables
- Response variables
The dependent variable can also serve as a good starting point for brainstorming potential research ideas. Zero in on a subject you’re interested in, such as sleep quality or the mental well-being of students, and then ask yourself:
“What could impact sleep quality/the mental well-being of students? Is there a potential correlation between this dependent variable and another factor? Are there studies on the topic I can read?”
The more you dig into the specific subject, the more you’re likely to uncover potential gaps in existing knowledge and different cause-and-effect relationships.
RELATED READ: How to Write a Research Outline
Dependent VS Independent Variables: How to Identify Them
When you’re looking at a specific study, there’s a high likelihood you probably won’t see the words “dependent and independent variable”.
However, the relationship between the different factors being measured should give you a good indication of what each variable is.
To identify an independent variable, ask yourself:
- Does this variable come before the other variable (as cause comes before effect)?
- Is the variable changed in any way in order to determine changes in the second variable (as is the case with the study on music and health)?
- Does the variable serve as a grouping mechanism to determine the second variable (as is the case with the study on socioeconomic status and academic performance)?
To recognize a dependent variable, ask yourself:
- Does this variable follow after the other variable?
- Is this variable considered the outcome of the study?
- Is this variable altered as a result of changes to another variable?
Examples
Let’s quickly run through a few more examples.
1. You’re researching the impact of remote work on carbon emissions. Remote work is an independent variable. Carbon emissions are a dependent variable.
2. You’re measuring the effect of daylight on serotonin levels in humans. Daylight is an independent variable. Serotonin levels are a dependent variable.
3. You want to know how long a nap should be in order to boost alertness. The length of the nap is an independent variable. Alertness is a dependent variable.
6 Important Tips to Keep in Mind
1. Remember that variables are often referred to by different names. No matter the name, it is the relationship between the variables that is of the utmost importance.
2. It is essential to keep in mind that a statistical relationship does not automatically mean there is a cause-and-effect relationship. Sometimes, there may be a hidden variable you’re missing that’s affecting the study or the two variables may correlate without one directly causing the other.
3. Usually, the independent variable changes first, and the dependent variable changes second.
4. To better visualize your variables, turn to graphs and charts for help. It is common to place the independent variable on the x-axis and the dependent variable on the y-axis.
5. A variable can never be both dependent and independent. It is one or the other.
6. It is possible to include many different dependent variables, but remember that if you decide to do so, you may need to ask more research questions and expand the scope of your paper. For instance, you can measure the impact of social media usage on sleep, diet, and subjective well-being, which will allow you to conduct much more thorough research on social media in general.
Conclusion
As you can see, dependent vs independent variables actually sound much more complicated than they are in practice. Once you unpack the relationship between the two kinds of variables, every research paper becomes easier to understand and digest properly.
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