Response vs Explanatory Variables: Definition & Examples
By busayo.longe
| Last updated:
Jul 27
|
10 min read
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The primary objective of any study is to determine whether there is a cause-and-effect relationship between the variables. Hence in experimental research, a variable is known as a factor that is not constant. It can be changed and it can also change on its own. An investigator can modify variables and control variables to determine if one variable has an effect on another variable in an experiment.
There are several types of variables, but the two which we will discuss are explanatory and response variables. We will examine their distinct attributes and their effects on research.
First, let’s start with the explanatory variable.
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An explanatory variable is known as the factor in an experiment that has been altered by the investigator or the researcher.
The researcher uses this variable to determine whether a change has occurred in the intervention group (Response variables). An explanatory variable is also known as a predictor variable or independent variable.
Although explanatory variables are often used interchangeably as independent variables, there are still some slight differences between the two.
An independent variable refers to when a variable is not affected by other variables. This means that the characteristics of other variables do not have an effect on that variable. While the variable is said to be explanatory when it is not totally independent.
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For example, let’s assume a random person is given two variables to analyze and interpret the concept of weight gain. The two variables given are Soda (Pepsi, Coke) and Fast food ( Burger, Pizza).
A person may opine that the consumption of fast food and soda are not related because fast food and soda are independent variables that don’t depend on each other. However, that may not be correct because the seller of fast food also encourages their customers to purchase soda along with their meal. Same also if you stop to buy soda in a place there are possibilities that there will be fast food available like burgers or hot dogs.
Now both variables ( fast food and soda) do have a contribution to weight gain. This is why they are called explanatory variables because these two variables can provide an explanation for the weight gain.
Oftentimes the lines between an explanatory variable and an independent variable are ignored especially in statistical research both the explanatory variable and independent variable mean the same.
Read: Research Questions: Definitions, Types + [Examples]
Response Variable is the outcome of a study in which the explanatory variable is altered. This means that the variation of a response variable gets to be explained by other factors.
Response variable is not independent because its result depends on the effects of other factors. It is also known as the dependent variable or outcome variable.
For example, if you want to determine whether alcohol reduces the chance of safe driving, the alcohol consumed by a subject would determine the effects on the subject’s driving performance.
This means that the consumed alcohol would provide an explanation for the subject driving performance. Here the driving skills is the response variable which the alcohol would explain. So the alcohol is the explanatory variable.
Read: What are Cross-Sectional Studies: Examples, Definition, Types
In some research experiments or studies, you can use one variable to explain or even predict the changes in other variables. In those types of studies, the explanatory variable explains the changes or differences that are observed in the response variable. Therefore the explanatory variable is the variable that the researcher or investigator can manipulate or alter in an experimental study.
The response variable is used to understand the outcome of experiments. This is because it is the response variable that shows the effects of the treatment item which is then explained by the explanatory variable.
For example, a teacher developed a new lesson outline to replace the old lesson outline which she believed can decrease anxiety in a student when it comes to public speaking. To test if a new lesson outline works better than the formal lesson she planned an experiment.
In the experiments, the selected students are randomly given either the new lesson or the old lesson.
Then their level of anxiety was measured during a series of public speaking experiences.
In this example, the explanatory variable is the lesson the student received while the response variable is their level of anxiety.
Read: Nominal, Ordinal, Interval & Ratio Variable + [Examples]
Let’s look at another example: some middle school students want to know whether height can be used to determine the age. So they conducted a random sampling of 30 students and teachers in their school.
Each of their sample group’s individual height and age was recorded as the study is an observational study. Because the student wants to use this process to predict the age of the people in their school the explanatory variable here will be the height while the response variable is the age.
To understand the relationship between explanatory variables and response variables, it is best to first understand the variables individually.
The first thing to keep in mind is that you can alter or manipulate the value of explanatory variables so as to evaluate their effect on response variables. So while the explanatory variables explain the changes that occur in the variables, the response variable is the actual focus of the study. It represents the questions of the studies that need to be answered.
Also, an explanatory variable explains the variation that occurs in the response variable. This is because there is a causal relationship between the explanatory variable and the response variable. Depending on the study questions, there can be an even distribution of variables in the explanatory variables and the response variables.
The response variable is what all the questions in research are based on. This is because it shows the change that occurs when a treatment has been administered.
For example, when trying to decide the best procedure for a patient with breast cancer between chemotherapy and anti-estrogen treatment, the question to be answered is which of these two procedures will prolong your patient’s life more?
The explanatory variable here will be the type of procedure administered while the response variable will be the survival time.
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Let us consider these examples of explanatory variables and response variables to better understand the concept.
Examples 1
If you as a researcher want to observe whether fruit smoothies help in losing weight. The aim of the study will be to determine whether the change in your subjects’ or participants’ weight is caused by the intake of fruit smoothies.
The explanatory variable in this study will be the fruit smoothie while the response variable is the weight of your participants.
Example 2
If a teacher wants to determine whether the amount of time her students spend on playing video games has an impact on the performance and score earned by the students in their exams. The aim of the study will be to observe the impact of video games on exam performance.
In this case, the explanatory variable is the amount of time the students spend playing video games and the response variable will be their exam results.
Example 3
A nutritionist may want to observe the effects of diet on her participants’ skin and hair health.
The aim of the experiment will be to determine how the participants’ diet can cause changes in their hair and their skin’s health.
In this study, the explanatory variable will be the participant’s diet while the response variable will be the health of the participants’ hair and skin.
To visualize explanatory and response variables, the easiest method is to use a graph.
The explanatory variable is placed on the x-axis on the graph while the response variable is placed on the y-axis.
Use a bar graph if the explanatory variable is categorical.
Use a line graph if the response variable is categorical. You can also use the scatter plot.
You’ll get paired data if you have a single explanatory variable and a single response variable. This implies that the measurement of each response variable is connected to the value of an explanatory variable in each subject.
Let’s use Example 2 in the above-listed examples, if the teacher wants to determine whether there is any cause-and-effect relationship between the number of hours the students spent playing video games and their exam performance, she can conduct a test on 100 students in the school.
The explanatory variables in this study are the number of hours the students spent playing video games and the response variable is the exam score of the 100 selected students.
The teacher can further represent the results in a graph. A scatter plot is best for this. The hours spent on playing video games will be plotted on the X-axis and the exam score of the 100 students plotted on the Y-axis. The data point in the scatterplot graph will represent the paired data of each of the students.
There is no definite answer to this however, we can use this example to determine whether age is an explanatory variable or a response variable.
If you want to determine an individual’s cost of living, some of the factors that will be analyzed are the individual’s age, the salary, and the individual’s marital status. In this case, these listed factors are the explanatory variables while the individual’s cost of living is the response variable because the level of the person’s cost of living is dependent on these factors.
From this example, age is an explanatory variable.
Let us consider this example, if a researcher wants to predict the possible value of a commodity In the market, the determinant factor will be other factors.
Let’s assume the commodity in question is gold, to determine the futuristic price of gold, other factors such as mining sites, and demand and supply will be considered.
The explanatory variable, in this case, will be demand and supply, and the mining sites while the response variable will be the forecasted price of gold in the future.
We can deduce from this example that time is a response variable.
Time is a response variable and not an explanatory variable. For example,
If you conduct a test to determine whether drinking coffee keeps a student awake for a longer time, give the student coffee in different measures.
Then compare the student’s reaction time to determine the effect of the treatment item.
The explanatory variable here will be the coffee drink given to the students while the response variable will be the student’s reaction time.
We have been able to discuss the relationship between explanatory variables and response variables. If you need to understand the cause of a reaction in an experiment, study the explanatory variable, they will provide the solution to the research problems.
It is also important for all researchers to note that there can be more than one explanatory variable in research. Such as age, temperature e.t.c
Also if there is no causal relationship in the data of a study, there may be no response variable.
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