1. Introduction
A variable refers to a factor or a trait that has the potential to vary or change during a study or experiment. It’s further classified into dependent and independent variables. A variable is important to the control of our experimental questions, experiments, and interpretation of results in every scientific research.
In this tutorial, we cover the definition of a dependent and independent variable. We also describe the difference between them. Further, we discuss the circumstances under which we can use them in our research.
2. Definition of Dependent and Independent Variables
Let’s start by defining those two terms.
Independent variable refers to the variable that the researcher manipulates. It’s altered to check if a change in its value corresponds to a change in the dependent variable.
Meanwhile, the dependent variable is the variable that the researcher measures or observes. It’s monitored for changes in response to the independent variable. It’s assumed to be the effect or outcome that depends on the other one.
3. Use Case
Let’s move on to an example that combines both concepts. We’ll choose an example directly related to the educational context.
The researchers want to study the impact of learning styles on student performance. Specifically, they want to know if teaching approaches (such as lecture style, flipped classroom, and problem-led learning) correlate with specific student performance outcomes (such as test scores, grade point average, and engagement).
The independent variable is the teaching method.
The dependent variable is the academic performance of the students, which consists of the following measures:
- Test scores: the researchers would measure the student’s performance on standardized tests or assessments
- GPA: They could select the overall grade point average (GPA) as the indicator of academic performance
- Student engagement: the researchers would look at how ‘intensely’ the student is taking part in the learning process
The experiment requires researchers to create first a control group and one or more experimental groups. They’ll provide the control group with a traditional teaching approach that is lecture-based. They’ll provide the experimental groups with an innovative teaching approach, such as flipped classroom and problem-based learning.
The participants were tested on the dependent variable before and after the teaching method intervention. This was to determine whether the manipulations of the independent variable (teaching style) affect the dependent variable.
In doing so, the researchers can better determine the specific effect of the instructional method. For instance, they can determine whether the flipped classroom teaching model leads to higher test scores and engagement compared with the lecture-based method.
In the table below, we can visualize the result of our study. It’s worth noting that the numbers here are just for illustration purposes:
Teaching Method
Test scores
GPA
Engagement
Traditional Lecture
70
3.1
60%
Flipped Classroom
75
3.4
75%
Problem-Based Learning
72
3.3
70%
The table compares the academic performance (test scores and GPA) and student engagement levels across the three different teaching methods being studied.
The above example emphasizes the need to spell out the dependent and independent variables. This is an essential step for researchers to argue for causal inference and structure the experiment properly.
4. Considerations in Identifying Dependent and Independent Variables
The research question sometimes helps us identify the dependent and independent variables. We’ll find it especially helpful if we pay special attention to it. The researchers can ask: Which variable can we change? That question will help us identify the independent variable.
They can also ask: Which factor can we measure or observe for changes? This last question will help us identify the dependent variable.
They must consider if there’s a directional relationship between them, like cause and effect. The independent variable exerts an influence on the dependent one.
In studies with an experimental design, the independent element is the key feature of our group. It’s the factor assigned only to the experimental group. The dependent variable is the outcome that we measure in the control group and the experimental group(s).
They must consider other factors that might influence the relationship between the independent and dependent variables. These factors, known as confounding variables, should be controlled in the research design. For example, participant characteristics (age, gender, socioeconomic status) and environmental factors (time of day, geographic location, distractions) can serve as confounding variables.
It’s also important to define the operational definitions of the independent and dependent variables. The way researchers design their studies depends on these operational definitions.
For example, the operational definitions for the independent variable (teaching method) outline the specific procedures used for each teaching method condition (such as traditional lecture, flipped classroom, or problem-based learning).
The dependent one will have their operational definitions listed out. This includes detailing how each outcome would be measured and quantified (for instance, the test scores, cumulative grade-point averages, and self-report surveys).
It’s important to decide what levels or values of the independent variable the researchers will manipulate and the range of values or measurements for the dependent element.
For example, in a study examining the impact of the teaching method on student academic achievement, the independent variable is the teaching method. It includes levels such as ‘traditional lecture’, ‘flipped classroom’, and ‘problem-based learning’.
By considering the above considerations, researchers can confidently ascertain which variables are independent and dependent. This confidence leads to a better experimental design, more robust data analysis, and insightful conclusions.
5. The Importance of Identifying Dependent and Independent Variables
Accurately identifying the dependent and independent variables in a research study is important for several reasons. We list them in the table below:
Importance
Description
Delineating Causality
By identifying the independent and dependent variable, researchers can understand the causal relationship between. They can conclude how independent variable can impact dependent variable.
Replication
**Identification of these components will help others to replicate the study and check the results
**
Experimental Design
Clarity regarding independent and dependent variable leads the researchers to an appropriate experimental design. They will know the right techniques to use for their methodology
Practical Applications
The ability to recognize the difference between independent and dependent variable assists in making decisions
Data interpretation
Once they are identified, then the researchers can make sense of the data and draw conclusions from them
By identifying the independent and dependent variables, the researcher can control their research findings.
6. Visualizing the Relationship
The image below helps to convey the relations between dependent and independent variables:
In the above diagram, which originates from this website, the independent variable is shown as the influencing factor or the cause, while the dependent variable is shown as the effect. The arrows demonstrate the direction of the relationship. The independent variable is exerting and causing an effect on the dependent variable.
7. Conclusion
Knowing the nature of the dependent and the independent variable is essential for implementing the scientific method and crafting well-designed studies.
Researchers who grasp the details of these two fundamental pieces of the puzzle can more confidently and correctly navigate the complexities of experimental design, data analysis, and interpretation.
Having provided the definition and features and made a practical use case, it is now possible to understand the difference between dependent and independent variables. Being equipped with such an understanding, we can go about our future research with better insight into what is at stake when defining and manipulating our variables.