Understanding the terms 'Dependent' and 'Independent' variables is essential in research and data analysis. These variables help us know more about the relationships and trends among our study data. However, knowing the difference between them can sometimes be confusing. This post will explain these terms, show their differences, and provide a quick reference summary.
A dependent variable is what you are trying to explain or predict. It is the variable whose values are dependent on one or more other variables in your study. In a research study, the dependent variable is the outcome or the phenomenon under investigation.
On the other hand, an independent variable is what you suspect might influence the dependent variable. It is the variable you manipulate or change to see how it affects the dependent variable.
Both dependent and independent variables go by various names, often used interchangeably in different fields.
- Outcome Variable
- Response Variable
- Explained Variable
- Measured Variable
- Predicted Variable
- Predictor Variable
- Explanatory Variable
- Manipulated Variable
- Feature (common in machine learning)
In The Context of Research
In a research context, let's say you are investigating the impact of studying hours on exam scores among students. Here:
- Dependent Variable: Exam Scores (as they are expected to change based on the studying hours)
- Independent Variable: Studying Hours (as they are the presumed influencers of exam scores)
|Aspect||Dependent Variable||Independent Variable|
|Definition||Variable being predicted or explained.||Variable presumed to cause changes or influence the dependent variable.|
|Alternate Names||Outcome, Response, Explained, Measured, Predicted Variable||Predictor, Explanatory, Manipulated, Feature|
|Example (In Context preparing for an exam)||Exam Scores||Studying Hours|