When you conduct research, you frequently explore cause-and-effect relationships between variables using experiments or observational studies. In this context, the variable that you manipulate or observe for changes is called the explanatory variable. On the other hand, the variable that changes in response to the explanatory variable is called the response variable. In this case, the reaction times of the participants would be the response variable.
Example:
When you’re conducting a study, you might be interested in how the amount of time spent exercising affects weight loss. In this case, you would set up an experiment where you ask participants to exercise for different durations each week, and then you would measure their weight loss at the end of the study period. In this example, the explanatory variable is the amount of time spent exercising per week. This is the variable that you are manipulating or changing to see its effect on the response variable. You might have different groups of participants exercising for 30 minutes, 60 minutes, and 90 minutes per week. The response variable in this study is the amount of weight lost by the participants. This is the variable that you are measuring to see how it changes in response to the different levels of the explanatory variable. You would record each participant’s weight before and after the study period to calculate their weight loss.
It’s important to note that the terms “explanatory variable” and “response variable” are often used synonymously with other terms in research:
- Explanatory variables are also known as independent variables, predictor variables, or input variables.
- Response variables are also referred to as dependent variables, outcome variables, or output variables.
These terms all describe the same fundamental concept: the variable that is being manipulated or observed (explanatory variable) is thought to influence or predict changes in the variable that is being measured or recorded (response variable).
Explanatory vs. response variables
In statistical modeling and data analysis, the terms “explanatory variable” and “response variable” are often used to describe the relationship between two variables.
- Explanatory variables, also known as independent or predictor variables, are the variables that are used to explain or predict changes in the response variable. They are the variables that are manipulated, controlled, or observed to investigate their effect on the response variable.
- Response variables, also known as dependent or outcome variables, are the variables that are being explained or predicted by the explanatory variables. They are the variables that are measured or observed to determine the effect of the explanatory variables.
Examples of explanatory and response variables
Some studies have just one explanatory variable and one response variable. However, in more complex research, you may use multiple explanatory variables to predict one or more response variables within a single model.
| Research question | Explanatory variables | Response variable |
| How does the amount of fertilizer affect crop yield? | Type of fertilizerAmount of fertilizer applied | Crop yield (tons per acre) |
| What factors influence customer satisfaction in a restaurant? | Wait timeFood qualityService qualityAmbiance | Customer satisfaction rating (1-5 scale) |
| How do different teaching methods impact student performance? | Teaching method (e.g., lecture, discussion, hands-on)Teacher experience (years) | Student test scores |
| Does the type of packaging material affect product shelf life? | Packaging material (e.g., plastic, glass, cardboard) | Product shelf life (days) |
| How do various demographic factors relate to voting behavior? | AgeGenderEducation level Income | Voting decision (e.g., Democrat, Republican) |
| What is the relationship between employee motivation and job performance? | Intrinsic motivation scoreExtrinsic motivation scoreJob satisfaction score | Job performance rating (by supervisor) |
Explanatory vs independent variables
The terms “explanatory variable” and “independent variable” are often used interchangeably, but there is a subtle difference between them. An explanatory variable is specifically used to explain or predict changes in the response variable, while an independent variable is any variable that is manipulated or controlled by the researcher, regardless of its role in the study.
Example: Explanatory versus independent variables
In a study investigating the effect of temperature (explanatory variable) on plant growth (response variable), temperature is an explanatory variable because it is used to explain changes in plant growth.
In the same study, if the researcher also controls the amount of water given to the plants (independent variable), water would be an independent variable but not an explanatory variable, as it is not the main focus of the study and is not used to explain changes in plant growth.
Visualizing explanatory and response variables
When presenting the results of a study involving explanatory and response variables, it is common to use graphs or charts to visualize the relationship between the variables. The explanatory variable is typically placed on the x-axis (horizontal axis), while the response variable is placed on the y-axis (vertical axis).
When visualizing the relationship between explanatory and response variables, the type of graph you choose depends on the nature of your variables:
- For quantitative variables, a scatterplot or a line graph is most appropriate.
- If your response variable is categorical, you can still use a scatterplot or a line graph.
- When your explanatory variable is categorical, a bar graph is the best choice.
In studies with only one explanatory variable and one response variable, you’ll collect paired data. Paired data means that each measurement of the response variable is directly linked to a specific value of the explanatory variable for each unit or participant in your study.
Example: Explanatory and response variables
Suppose you are a researcher interested in how the number of hours spent studying affects students’ exam scores. You collect data from a group of students, recording the number of hours each student spent studying for an exam and their corresponding exam score.
In this example:
- The explanatory variable (x-axis) is the number of hours spent studying. This is the variable you believe might influence or predict the response variable.
- The response variable (y-axis) is the exam score. This is the variable you are measuring to see how it changes in relation to the explanatory variable.
To visualize this relationship, you can create a scatterplot with the number of hours studied on the x-axis and the exam score on the y-axis. Each data point on the scatter plot represents a single student’s studying time and exam score.
