Win or lose in a debate
Rating scale responses in a survey.
The numeric variable is a quantifiable characteristic whose values are numbers (except for numbers which are codes for other categories). Moreover, numeric variables are either continuous or discrete.
Discrete | Counts individual items or values | Number of students in a class Number of trees in a particular forest |
Continuous | Measurement of continuous or non-finite values | Distance Volume |
Apart from the above mentioned types, variables are classified into many common and less common types. Here, we will discuss two of the most important variables that help investigate causal relationship between variables using experiments or observations in a research.
An explanatory variable is a type of independent variable. It is what a researcher manipulates or observes changes in. In other words, an explanatory variable is the expected cause, and it explains the results.
A response variable is a type of dependent variable. It is the one that changes the results. Furthermore, a response variable is the expected effect, and it responds to explanatory variables.
These two variables are related, wherein the change in the response variable happens only after changes in an explanatory variable.
There is a causal relationship between these variables either directly or indirectly. Furthermore, in an indirect relationship, an explanatory variable may act on a response variable through a mediator.
However, if a researcher is dealing with a correlational relationship, there will be no explanatory and response variables. The changes in one variable brings changes in another. This type of variable is confounding variable, another common type of variable.
Example 1: cancer treatment.
In a research study, where you are trying to figure out if chemo or anti-estrogen treatment is a better procedure for breast cancer patients, the question to be addressed is — which procedure prolongs life more? And thus, survival time is the response variable. Meanwhile, the type of therapy given is the explanatory variable, which may or may not affect the response variable.
In this example, explanatory variable is type of treatment. However, in real life there could be several more explanatory variables, including: age, health, weight, and other lifestyle factors.
A group of middle school students want to know if they can use height to predict age. They take a random sample of 50 people at their school, both students and teachers, and record each individual’s height and age. This is an observational study. Additionally, the students want to use height to predict age.
In this example, explanatory variable is height and response variable is age.
A team of veterinarians want to compare the effectiveness of two fertility treatments for pandas in captivity. The two treatments are in-vitro fertilization and male fertility medications.
This experiment has one explanatory variable: type of fertility treatment. Meanwhile, the response variable is a measure of fertility rate.
A public speaking teacher developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. Furthermore, she designed an experiment to test if her new lesson works better than the old lesson. Public speaking students are randomly assigned to receive either the new or old lesson; their anxiety levels during a variety of public speaking experiences are measured.
In this experiment, explanatory variable is the lesson received. While the response variable is the anxiety level.
The easiest way to visualize explanatory and response variables is from a graphical representation . Moreover on a graph, the explanatory variable is conventionally placed on the x-axis, meanwhile the response variable is placed on the y-axis.
For quantitative variables, use a scatterplot or a line graph.
If the response variable is categorical, use scatterplot or a line graph.
If the explanatory variable is categorical, use a bar graph.
However, when you have only one explanatory and one response variable , you will acquire paired data. Furthermore, this means every response variable measurement is linked to an explanatory variable value for each participating unit.
Have you used these variables in research? Tell us or write to us if you have used explanatory or response variables in your research study.
Great Useful
Rate this article Cancel Reply
Your email address will not be published.
Research Interviews: An effective and insightful way of data collection
Research interviews play a pivotal role in collecting data for various academic, scientific, and professional…
Planning Your Data Collection: Designing methods for effective research
Planning your research is very important to obtain desirable results. In research, the relevance of…
A research study includes the collection and analysis of data. In quantitative research, the data…
You know what is tragic? Having the potential to complete the research study but not…
What Is Statistical Validity? -Understanding Trends in Validating Research Data
With an aim to understand, analyze, and draw conclusions based on the enormous data often…
Effective Use of Statistics in Research – Methods and Tools for Data Analysis
Sign-up to read more
Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:
We hate spam too. We promise to protect your privacy and never spam you.
I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:
Which among these features would you prefer the most in a peer review assistant?
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.
The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments . An experimental unit is a single object or individual to be measured.
You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.
Additional variables that can cloud a study are called lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.
The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:
Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. 1
When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment–a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor. Blinding or masking in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, they do not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.
Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred people between the ages of 50 and 84 are recruited as participants. The people are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each person takes one pill each day for three years, but they don't know whether they are taking aspirin or the placebo. At the end of the study, researchers count the number of people in each group who have had heart attacks.
Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.
The population is people aged 50 to 84. The sample is the 400 people who participated. The experimental units are the individual people in the study. The explanatory variable is oral medication. The treatments are aspirin and a placebo. The response variable is whether a subject had a heart attack.
A study needs to be conducted of the effect of three medicines A, B, and C on the height of adults aged 30 to 45. 90 adults were selected randomly and divided into three equal groups. The first group was asked to take medicine A for 6 months. The second group was asked to take medicine B for 6 months. The third group was asked to take medicine C for 6 months. The average change in height in each group is calculated at the end of the study.
Identify the following values for this study: population, sample, experimental units, explanatory variables, response variable, treatments.
The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.
The Placebo Research Group conducted a study to find the extent of placebo effects. A group of men randomly selected were asked to take a test before and after taking a pill that induces a mild headache. The pill in half of the randomly selected men was replaced with a similar pill that has no effect. For each trial, researchers recorded the change in time men took to complete the tests before and after taking the pill.
A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?
The explanatory variable is birth order. You cannot randomly assign a person’s birth order. Random assignment eliminates the impact of lurking variables. When you cannot assign subjects to treatment groups at random, there will be differences between the groups other than the explanatory variable.
You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?
The widespread misuse and misrepresentation of statistical information often gives the field a bad name. Some say that “numbers don’t lie,” but the people who use numbers to support their claims often do.
An investigation of famous social psychologist, Diederik Stapel, has led to the retraction of his articles from some of the world’s top journals including Journal of Experimental Social Psychology, Social Psychology, Basic and Applied Social Psychology, British Journal of Social Psychology, and the magazine Science . Diederik Stapel is a former professor at Tilburg University in the Netherlands. An extensive investigation involving three universities where Stapel has worked concluded that the psychologist is guilty of fraud on a colossal scale. Falsified data taints over 55 papers he authored and 10 Ph.D. dissertations that he supervised.
Stapel did not deny that his deceit was driven by ambition. But it was more complicated than that, he told me. He insisted that he loved social psychology but had been frustrated by the messiness of experimental data, which rarely led to clear conclusions. His lifelong obsession with elegance and order, he said, led him to concoct sexy results that journals found attractive. “It was a quest for aesthetics, for beauty—instead of the truth,” he said. He described his behavior as an addiction that drove him to carry out acts of increasingly daring fraud, like a junkie seeking a bigger and better high. 2
The committee investigating Stapel concluded that he is guilty of several practices including:
Clearly, it is never acceptable to falsify data the way this researcher did. Sometimes, however, violations of ethics are not as easy to spot.
Researchers have a responsibility to verify that proper methods are being followed. The report describing the investigation of Stapel’s fraud states that, “statistical flaws frequently revealed a lack of familiarity with elementary statistics.” 3 Many of Stapel’s co-authors should have spotted irregularities in his data. Unfortunately, they did not know very much about statistical analysis, and they simply trusted that he was collecting and reporting data properly.
Many types of statistical fraud are difficult to spot. Some researchers simply stop collecting data once they have just enough to prove what they had hoped to prove. They don’t want to take the chance that a more extensive study would complicate their lives by producing data contradicting their hypothesis.
Professional organizations, like the American Statistical Association, clearly define expectations for researchers. There are even laws in the federal code about the use of research data.
When a statistical study uses human participants, as in medical studies, both ethics and the law dictate that researchers should be mindful of the safety of their research subjects. The U.S. Department of Health and Human Services oversees federal regulations of research studies with the aim of protecting participants. When a university or other research institution engages in research, it must ensure the safety of all human subjects. For this reason, research institutions establish oversight committees known as Institutional Review Boards (IRB) . All planned studies must be approved in advance by the IRB. Key protections that are mandated by law include the following:
These ideas may seem fundamental, but they can be very difficult to verify in practice. Is removing a participant’s name from the data record sufficient to protect privacy? Perhaps the person’s identity could be discovered from the data that remains. What happens if the study does not proceed as planned and risks arise that were not anticipated? When is informed consent really necessary? Suppose your doctor wants a blood sample to check your cholesterol level. Once the sample has been tested, you expect the lab to dispose of the remaining blood. At that point the blood becomes biological waste. Does a researcher have the right to take it for use in a study?
It is important that students of statistics take time to consider the ethical questions that arise in statistical studies. How prevalent is fraud in statistical studies? You might be surprised—and disappointed. There is a website dedicated to cataloging retractions of study articles that have been proven fraudulent. A quick glance will show that the misuse of statistics is a bigger problem than most people realize.
Vigilance against fraud requires knowledge. Learning the basic theory of statistics will empower you to analyze statistical studies critically.
Describe the unethical behavior in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.
A researcher is collecting data in a community.
Describe the unethical behavior, if any, in each example and describe how it could impact the reliability of the resulting data. Explain how the problem should be corrected.
A study is commissioned to determine the favorite brand of fruit juice among teens in California.
This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.
Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.
Access for free at https://openstax.org/books/introductory-statistics-2e/pages/1-introduction
© Jul 18, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.
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.
For you: Empirical Research Survey Template
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.
Explore: Dependent vs Independent Variables: 11 Key Differences
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.
Read: Open vs Close-Ended Question: 13 Key Differences
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.
Free to use: Feasibility Study Template
Let us consider these examples of explanatory variables and response variables to better understand the concept.
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.
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.
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 b ar 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.
Connect to Formplus, Get Started Now - It's Free!
You may also like:
Differences between experimental and non experimental research on definitions, types, examples, data collection tools, uses, advantages etc.
In this article, we will look into the concept of experimental bias and how it can be identified in your research
In this article, we are going to look at Simpson’s Paradox from its historical point and later, we’ll consider its effect in...
In this article, we’ll discuss what a lurking variable means, the several types available, its effects along with some real-life examples
Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..
Content preview.
Arcu felis bibendum ut tristique et egestas quis:
2.5 - defining a common language for comparative studies, overview: section .
We've learned some of the very basics about research studies that compare two or more samples of one variable. Now we will explore this topic in more detail. We first need to learn a few terms. These include:
The experimental unit is the smallest basic object to which one can assign different conditions (treatments.) In research studies, the experimental unit does not always have to be a person. In fact, the statistical terminology that is associated with research studies actually came from studies done in agriculture. Examples of an experimental unit include:
The explanatory variable is the variable used to form or define the different samples. In randomized experiments, one explanatory variable is the variable that is used to explain differences in the groups. In this instance, the explanatory variable can also be called a treatment when each experimental unit is randomly assigned a certain condition. Examples of explanatory variables include:
You should note that gender and type of plant cannot be called treatments because one cannot randomly assign gender or type of plant.
The response (outcome) variable is the outcome of the study that is either measured or counted. We have seen the response (outcome) variable in previous lessons. Examples of response variables include:
Of course, some variables may play different roles in different studies. For example, in an experiment to see whether a new diet might be held in reducing your weight; weight is the response variable and whether you used the new diet or not would be the explanatory variable. On the other hand, in an observational study to examine how a person's weight might affect their heart rate; weight would play the role of an explanatory variable and heart rate would be the response variable.
A confounding variable is a variable that affects the response variable and is also related to the explanatory variable. The effect of a confounding variable on the response variable cannot be separated from the effect of the explanatory variable. Therefore, we cannot clearly determine that the explanatory variable is solely responsible for any effect on the response or outcome variable when a confounding variable is present. Confounding variables are problematic in observational studies.
Laboratory experiments conducted in the 1980s showed that pregnant mice exposed to high does of ultrasound gave birth to lower weight infant mice than unexposed mice (in fact the higher the dose the greater the effect on birthweights). This worried obstetricians who feared that sonograms given to women during pregnancy might cause lower weights in their children. Researchers at Johns Hopkins University Hospital then examined the birthweights of infants of mothers who had sonograms versus those whose mothers had no such exposure. They found that the 1598 infants who had been exposed averaged a couple of ounces lower in weight than the 944 infants whose mothers did not have a sonogram. However, the women who got sonograms were more likely to have had twins in the past and were more likely to be over 40 years old. Having twins or being over 40 are examples of confounding variables in this study since they provide an alternate explanation for the data. You can not tell whether it was the sonogram that caused the lower birth weights or just the confounding medical reasons for getting the sonogram in the first place. Later experimental evidence in humans did not show sonograms to have any effect (see Abramowicz et al, 2008 for a review).
One of the many ways that variables in statistics can be classified is to consider the differences between explanatory and response variables. Although these variables are related, there are important distinctions between them. After defining these types of variables, we will see that the correct identification of these variables has a direct influence on other aspects of statistics, such as the construction of a scatterplot and the slope of a regression line .
We begin by looking at the definitions of these types of variables. A response variable is a particular quantity that we ask a question about in our study. An explanatory variable is any factor that can influence the response variable. While there can be many explanatory variables, we will primarily concern ourselves with a single explanatory variable.
A response variable may not be present in a study. The naming of this type of variable depends upon the questions that are being asked by a researcher. The conducting of an observational study would be an example of an instance when there is not a response variable. An experiment will have a response variable. The careful design of an experiment tries to establish that the changes in a response variable are directly caused by changes in the explanatory variables.
To explore these concepts we will examine a few examples. For the first example, suppose that a researcher is interested in studying the mood and attitudes of a group of first-year college students. All first-year students are given a series of questions. These questions are designed to assess the degree of homesickness of a student. Students also indicate on the survey how far their college is from home.
One researcher who examines this data may just be interested in the types of student responses. Perhaps the reason for this is to have an overall sense about the composition of a new freshman. In this case, there is not a response variable. This is because no one is seeing if the value of one variable influences the value of another.
Another researcher could use the same data to attempt to answer if students who came from further away had a greater degree of homesickness. In this case, the data pertaining to the homesickness questions are the values of a response variable, and the data that indicates the distance from home forms the explanatory variable.
For the second example we might be curious if number of hours spent doing homework has an effect on the grade a student earns on an exam. In this case, because we are showing that the value of one variable changes the value of another, there is an explanatory and a response variable. The number of hours studied is the explanatory variable and the score on the test is the response variable.
When we are working with paired quantitative data , it is appropriate to use a scatterplot. The purpose of this kind of graph is to demonstrate relationships and trends within the paired data. We do not need to have both an explanatory and response variable. If this is the case, then either variable can plotted along either axis. However, in the event that there is a response and explanatory variable, then the explanatory variable is always plotted along the x or horizontal axis of a Cartesian coordinate system. The response variable is then plotted along the y axis.
The distinction between explanatory and response variables is similar to another classification. Sometimes we refer to variables as being independent or dependent . The value of a dependent variable relies upon that of an independent variable . Thus a response variable corresponds to a dependent variable while an explanatory variable corresponds to an independent variable. This terminology is typically not used in statistics because the explanatory variable is not truly independent. Instead the variable only takes on the values that are observed. We may have no control over the values of an explanatory variable.
Take a peek at our powerful survey features to design surveys that scale discoveries.
Download feature sheet.
Explore Voxco
Need to map Voxco’s features & offerings? We can help!
Watch a Demo
Download Brochures
Get a Quote
Get exclusive insights into research trends and best practices from top experts! Access Voxco’s ‘State of Research Report 2024 edition’ .
We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
Find the best survey software for you! (Along with a checklist to compare platforms)
Get Buyer’s Guide
Explore Voxco
Watch a Demo
Download Brochures
Find the best customer experience platform
Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.
Get the Guide Now
VP Innovation & Strategic Partnerships, The Logit Group
SHARE THE ARTICLE ON
In market research, explanatory variables refer to the characteristics that influence consumer behavior. Response variables, on the other hand, are the outcomes of interest that are measured in response to the changes in the explanatory variables.
In experimental research, a variable is a factor that can change and can be changed. These factors can be altered and controlled for an experiment to measure the effect of one variable on the other.
The experiment includes different types of variables. It aims to determine the causal relationships between two or more variables. Among many variables, two of which we will discuss are Explicative Variables and Response Variables.
An explanation variable is a factor that you can manipulate in an experiment to determine the change caused by the response variable. It is often referred to as an Independent Variable.
In market research, these variables can include demographic, socioeconomic, geographic, and psychographic factors. For example;
1. Demographic: Age, gender, education level, occupation, marital status, etc.
2. Socioeconomic: Social class, household size, household income, employment status, etc.
3. Geographic: Location, urban/rural classification, etc.
4. Psychographic: Personality traits, values, lifestyle, interests, attitudes, etc.
This type of variable plays a key role in market research to help you identify patterns and relationships between consumer characteristics and their resulting behavior. By understanding the causal relationship between these variables you can tailor the marketing strategies to target specific customer segments.
Two simple ways to identify explanatory variables include:
1. Surveys – Leverage online survey tools to gather data directly from customers through structured surveys across multiple accessible channels.
2. Interviews – Utilize phone surveys or mobile- offline survey tools to engage consumers in person and gather insights into consumer attitudes and motivations to identify relevant explanatory variables.
What is a response variable.
Response variable is the result of the experiment where the explanatory variable is manipulated. It is a factor whose variation is explained by the other factors. Response Variable is often referred to as the Dependent Variable.
In market research, this variable represents the key metric businesses seek to understand about their consumers and influence through their marketing initiatives. Examples of response variables in terms of market research include;
1. Purchase behavior: The frequency, volume, and types of products or services purchased by consumers.
2. Customer satisfaction: The level of satisfaction or dissatisfaction experienced by customers with a product, service, or brand.
3. Brand loyalty: Repeat purchase patterns and likelihood to recommend brands/products.
This type of variable provides you with a measurable indicator of consumer behavior and attitude. It helps gauge the impact of your marketing initiatives, allowing for a timely adjustment to those strategies.
Three ways you can identify response variables are:
1. Surveys – Gather direct feedback from consumers to identify customer satisfaction, brand loyalty, and purchase behavior.
2. Observation – Observe customer behavior in natural or simulated environments.
3. Experimentation – Conduct controlled experiments by manipulating variables and measuring the impact on response variables.
Here are some reasons why understanding these variables is crucial in market research:
The best way to identify the two variables separately and understand the difference is to remember that You change the value of explanatory variables to observe the impact it has and how it influence the response variable.
The explanatory variable explains the variation caused by the response variable. There is a cause-and-effect relationship between the two variables. The number of variables in each type may be more than one, depending on the research question.
For Example,
You want to find out if alcohol decreases the ability to drive safely. The alcohol a participant consumes determines its effect on their driving performance. In the experiment, the amount of alcohol consumed gives an explanation for the driving skill.
Therefore, in the experiment,
Identifying and understanding the causal relationship between explanatory and response variables enables you to interpret market research outcomes and make insightful and informed decisions. Explanatory variables influence or cause changes in response variables. However, a causal relationship requires careful analysis and consideration.
Statistical data analysis methods such as correlation and regression analysis can help explore, identify, and validate any causal relationship between variables in market research.
Let’s look at some examples of explanatory and response variables in market research.
You can identify distinct marge segments with unique preferences and needs by understanding the relationship between explanatory variables like demographic or geographic factors and response variables like purchase behavior and brand loyalty.
This enables you to customize market strategies to the specific preferences of each segment, leading to more targeted customer retention and acquisition.
The insight into the causal relationship between consumer preferences and purchase behavior can inform product development efforts. By identifying features that are valued by target consumers, you can design products that align with customer needs and preferences.
Exploring the causal relationship between explanatory variables and response variables within your target market can help you tailor marketing communications that resonate with the target audience.
Get our survey software buyer’s guide to find the best fit for you!
When you have paired data, you may use Scatterplot to demonstrate the causal relationship between the Explanatory and Response Variables.
A paired data implies that you have one variable for each type. This means that the outcome of every response variable for each participant is linked with every explanatory variable.
In such a case, in a scatterplot, the explanatory variable is plotted along the X-axis, which is horizontal, and the response variable is plotted along the Y-axis, which is vertical in a Cartesian coordinate system.
Let’s say you want to observe if there is any causal relationship between the number of hours spent studying and the performance on the test. You experimented with 100 students in a school.
You can demonstrate the result in a scatter plot by plotting the hours spent studying on the X-axis and the test score on the Y-axis. Each data point in the scatterplot is the paired data of each student.
Trusted by 450+ brands and top 50 MR firms in 40+ countries to gather, measure, uncover, and act on data.
In conclusion, understanding the relationship between explanatory and response variables is essential to conducting meaningful market research and making insightful business decisions. An understanding of the causal relationship between the two variables enables you to leverage insights into various business functions and make decisions that meet the target market’s needs.
Explore Voxco Survey Software
+ Omnichannel Survey Software
+ Online Survey Software
+ CATI Survey Software
+ IVR Survey Software
+ Market Research Tool
+ Customer Experience Tool
+ Product Experience Software
+ Enterprise Survey Software
Regression coefficient SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents What is regression coefficient? Regression coefficient in
Exploring Top 10 Customer Experience Survey Software Customer Experience Ensuring an excellent customer experience can be tricky but an effective guide can help. Download Now
How To Choose The Right Dialer Software? SHARE THE ARTICLE ON Table of Contents Choosing the right dialer for your organization’s needs is a critical
Unveiling The Factors That Mold Your Brand Perception SHARE THE ARTICLE ON Table of Contents It takes an average of 10 seconds for consumers to
Using a t-test SHARE THE ARTICLE ON Share on facebook Share on twitter Share on linkedin Table of Contents A t-test is a statistical technique
Unlocking Insights: A Comprehensive Guide to Primary Market Research Try a free Voxco Online sample survey! Unlock your Sample Survey SHARE THE ARTICLE ON In
We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Read More
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
hubspotutk | www.voxco.com | HubSpot functional cookie. | 1 year | HTTP |
lhc_dir_locale | amplifyreach.com | --- | 52 years | --- |
lhc_dirclass | amplifyreach.com | --- | 52 years | --- |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_fbp | www.voxco.com | Facebook Pixel advertising first-party cookie | 3 months | HTTP |
__hstc | www.voxco.com | Hubspot marketing platform cookie. | 1 year | HTTP |
__hssrc | www.voxco.com | Hubspot marketing platform cookie. | 52 years | HTTP |
__hssc | www.voxco.com | Hubspot marketing platform cookie. | Session | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_gid | www.voxco.com | Google Universal Analytics short-time unique user tracking identifier. | 1 days | HTTP |
MUID | bing.com | Microsoft User Identifier tracking cookie used by Bing Ads. | 1 year | HTTP |
MR | bat.bing.com | Microsoft User Identifier tracking cookie used by Bing Ads. | 7 days | HTTP |
IDE | doubleclick.net | Google advertising cookie used for user tracking and ad targeting purposes. | 2 years | HTTP |
_vwo_uuid_v2 | www.voxco.com | Generic Visual Website Optimizer (VWO) user tracking cookie. | 1 year | HTTP |
_vis_opt_s | www.voxco.com | Generic Visual Website Optimizer (VWO) user tracking cookie that detects if the user is new or returning to a particular campaign. | 3 months | HTTP |
_vis_opt_test_cookie | www.voxco.com | A session (temporary) cookie used by Generic Visual Website Optimizer (VWO) to detect if the cookies are enabled on the browser of the user or not. | 52 years | HTTP |
_ga | www.voxco.com | Google Universal Analytics long-time unique user tracking identifier. | 2 years | HTTP |
_uetsid | www.voxco.com | Microsoft Bing Ads Universal Event Tracking (UET) tracking cookie. | 1 days | HTTP |
vuid | vimeo.com | Vimeo tracking cookie | 2 years | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
__cf_bm | hubspot.com | Generic CloudFlare functional cookie. | Session | HTTP |
Name | Domain | Purpose | Expiry | Type |
---|---|---|---|---|
_gcl_au | www.voxco.com | --- | 3 months | --- |
_gat_gtag_UA_3262734_1 | www.voxco.com | --- | Session | --- |
_clck | www.voxco.com | --- | 1 year | --- |
_ga_HNFQQ528PZ | www.voxco.com | --- | 2 years | --- |
_clsk | www.voxco.com | --- | 1 days | --- |
visitor_id18452 | pardot.com | --- | 10 years | --- |
visitor_id18452-hash | pardot.com | --- | 10 years | --- |
lpv18452 | pi.pardot.com | --- | Session | --- |
lhc_per | www.voxco.com | --- | 6 months | --- |
_uetvid | www.voxco.com | --- | 1 year | --- |
Components of experimental design, learning outcomes.
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments. In this module, you will learn important aspects of experimental design. Proper study design ensures the production of reliable, accurate data.
The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory variable . The affected variable is called the response variable . In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments . An experimental unit is a single object or individual to be measured.
The following video explains the difference between collecting data from observations and collecting data from experiments.
Let’s say you want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.
Additional variables that can cloud a study are called lurking variables . In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point the only difference between groups is the one imposed by the researcher. Different outcomes measured in the response variable, therefore, must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.
The power of suggestion can have an important influence on the outcome of an experiment. Studies have shown that the expectation of the study participant can be as important as the actual medication. In one study of performance-enhancing drugs, researchers noted:
Results showed that believing one had taken the substance resulted in [ performance ] times almost as fast as those associated with consuming the drug itself. In contrast, taking the drug without knowledge yielded no significant performance increment. 1
When participation in a study prompts a physical response from a participant, it is difficult to isolate the effects of the explanatory variable. To counter the power of suggestion, researchers set aside one treatment group as a control group . This group is given a placebo treatment — a treatment that cannot influence the response variable. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments. Of course, if you are participating in a study and you know that you are receiving a pill which contains no actual medication, then the power of suggestion is no longer a factor.
Blinding in a randomized experiment preserves the power of suggestion. When a person involved in a research study is blinded, he does not know who is receiving the active treatment(s) and who is receiving the placebo treatment. A double-blind experiment is one in which both the subjects and the researchers involved with the subjects are blinded.
Researchers want to investigate whether taking aspirin regularly reduces the risk of heart attack. Four hundred men between the ages of 50 and 84 are recruited as participants. The men are divided randomly into two groups: one group will take aspirin, and the other group will take a placebo. Each man takes one pill each day for three years, but he does not know whether he is taking aspirin or the placebo. At the end of the study, researchers count the number of men in each group who have had heart attacks.
Identify the following values for this study: population, sample, experimental units, explanatory variable, response variable, treatments.
The Smell & Taste Treatment and Research Foundation conducted a study to investigate whether smell can affect learning. Subjects completed mazes multiple times while wearing masks. They completed the pencil and paper mazes three times wearing floral-scented masks, and three times with unscented masks. Participants were assigned at random to wear the floral mask during the first three trials or during the last three trials. For each trial, researchers recorded the time it took to complete the maze and the subject’s impression of the mask’s scent: positive, negative, or neutral.
A researcher wants to study the effects of birth order on personality. Explain why this study could not be conducted as a randomized experiment. What is the main problem in a study that cannot be designed as a randomized experiment?
You are concerned about the effects of texting on driving performance. Design a study to test the response time of drivers while texting and while driving only. How many seconds does it take for a driver to respond when a leading car hits the brakes?
Privacy Policy
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Methodology
Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.
An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
Professional editors proofread and edit your paper by focusing on:
See an example
There are two main types of independent variables.
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
You have three independent variable levels, and each group gets a different level of treatment.
You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .
Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.
Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.
In statistics , dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .
A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .
Here are some tips for identifying each variable type.
Use this list of questions to check whether you’re dealing with an independent variable:
Check whether you’re dealing with a dependent variable:
Independent and dependent variables are generally used in experimental and quasi-experimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question | Independent variable | Dependent variable(s) |
---|---|---|
Do tomatoes grow fastest under fluorescent, incandescent, or natural light? | ||
What is the effect of intermittent fasting on blood sugar levels? | ||
Is medical marijuana effective for pain reduction in people with chronic pain? | ||
To what extent does remote working increase job satisfaction? |
For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .
The type of test is determined by:
You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.
In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).
The type of visualization you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.
You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bhandari, P. (2023, June 22). Independent vs. Dependent Variables | Definition & Examples. Scribbr. Retrieved September 18, 2024, from https://www.scribbr.com/methodology/independent-and-dependent-variables/
Other students also liked, guide to experimental design | overview, steps, & examples, explanatory and response variables | definitions & examples, confounding variables | definition, examples & controls, "i thought ai proofreading was useless but..".
I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”
IMAGES
VIDEO
COMMENTS
An explanatory variable is what you manipulate or observe changes in (e.g., caffeine dose), while a response variable is what changes as a result (e.g., reaction times). The words "explanatory variable" and "response variable" are often interchangeable with other terms used in research. Cause (what changes)
This experiment has one explanatory variable: type of fertility treatment. The response variable is a measure of fertility rate. Example: Public Speaking Approaches Section . A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. She designs an ...
Explanatory Variable: Type of fertilizer. This is the variable we change so that we can observe the effect it has on plant growth. Response Variable: Plant growth. This is the variable that changes as a result of the fertilizer being applied to it. Fun Fact: We would use a two sample t-test to perform this experiment. Example 2: Max Vertical Jump
This experiment has one explanatory variable: type of fertility treatment. The response variable is a measure of fertility rate. Example: Public Speaking Approaches A public speaking teacher has developed a new lesson that she believes decreases student anxiety in public speaking situations more than the old lesson. She designs an experiment to ...
An explanatory variable is what you manipulate or observe changes in (e.g., caffeine dose). A response variable is what changes as a result (e.g., reaction times). The words 'explanatory variable' and 'response variable' are often interchangeable with other terms used in research. Cause (what changes) Effect (what's measured ...
The response variable is the focus of a question in a study or experiment. An explanatory variable is one that explains changes in that variable. It can be anything that might affect the response variable. Let's say you're trying to figure out if chemo or anti-estrogen treatment is better procedure for breast cancer patients.
The purpose of an experiment is to investigate the relationship between two variables. In an experiment, there is the explanatory variable which affects the response variable. In a randomized experiment, the researcher manipulates the explanatory variable and then observes the response variable.
Explanatory Variable: Type of fertilizer. This is the variable we change so that we can observe the effect it has on plant growth. Response Variable: Plant growth. This is the variable that changes as a result of the fertilizer being applied to it. Fun Fact: We would use a two sample t-test to perform this experiment. Example 2: Max Vertical Jump
1.1.4 - Variables. There may be many variables in a study. The variables may play different roles in the study. Variables can be classified as either explanatory or response variables. A variable is any characteristic, number, or quantity that can be measured, counted, or observed for record. Variable that about which the researcher is posing ...
An explanatory variable is a variable that is manipulated or controlled by the researcher in an experiment to determine its effect on the dependent variable. Related terms Dependent Variable : The dependent variable is the outcome or result that is measured or observed in response to changes in the explanatory variable.
In this experiment, explanatory variable is the lesson received. While the response variable is the anxiety level. Visualization of Explanatory and Response Variables. The easiest way to visualize explanatory and response variables is from a graphical representation. Moreover on a graph, the explanatory variable is conventionally placed on the ...
The affected variable is called the response variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments. An experimental unit is a single object or individual to be measured.
Factors are explanatory variables to be studied in an investigation. Examples: 1. In a study of the effects of colors and prices on sales of cars, the factors being studied are color (qualitative variable) and price (quantitative variable). ... Factor levels are the "values" of that factor in an experiment. For example, in the study involving ...
This is an example where the experiment has two explanatory variables and a response variable. In order to set up such an experiment, there has to be one treatment group for every combination of categories of the two explanatory variables. Thus, in this case there are 3 * 3 = 9 combinations of the two variables to which the subjects are assigned.
Revision notes on Explanatory & Response Variables for the College Board AP® Statistics syllabus, written by the Statistics experts at Save My Exams.
Select the correct answer below: a. the independent variable in an experiment. b. a variable that has an effect on a study even though it is neither an independent nor a dependent variable. c. the dependent variable in an experiment. d. a value or component of the independent variable applied in an experiment.
The affected variable is called the response variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments. An experimental unit is a single object or individual to be measured.
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.
The explanatory variable is the variable used to form or define the different samples. ... For example, in an experiment to see whether a new diet might be held in reducing your weight; weight is the response variable and whether you used the new diet or not would be the explanatory variable. On the other hand, in an observational study to ...
The distinction between explanatory and response variables is similar to another classification. Sometimes we refer to variables as being independent or dependent. The value of a dependent variable relies upon that of an independent variable. Thus a response variable corresponds to a dependent variable while an explanatory variable corresponds ...
Explanatory research helps you analyze these patterns, formulating hypotheses that can guide future endeavors. If you are seeking a more complete understanding of a relationship between variables, explanatory research is a great place to start. However, keep in mind that it will likely not yield conclusive results.
Explanatory variables for this experiment are the number of hours spent studying; The response variable is the test score of 100 students; You can demonstrate the result in a scatter plot by plotting the hours spent studying on the X-axis and the test score on the Y-axis. Each data point in the scatterplot is the paired data of each student.
The affected variable is called the response variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments. An experimental unit is a single object or individual to be measured.
Explanatory variables (they explain an event or outcome) Predictor variables (they can be used to predict the value of a dependent variable) ... Experimental design is the process of planning an experiment to test a hypothesis. The choices you make affect the validity of your results. 1503.