the explanatory variables in an experiment is

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• Explanatory vs Response Variables | Definitions & Examples

Explanatory vs Response Variables | Definitions & Examples

Published on 4 May 2022 by Pritha Bhandari .

In research, you often investigate causal relationships between variables using experiments or observations. For example, you might test whether caffeine improves speed by providing participants with different doses of caffeine and then comparing their reaction times.

• 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.

Table of contents

Explanatory vs response variables, explanatory vs independent variables, visualising explanatory and response variables, frequently asked questions about explanatory and response variables.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to explanatory variables.

You expect changes in the response variable to happen only after changes in an explanatory variable.

There’s a causal relationship between the variables that may be indirect or direct. In an indirect relationship, an explanatory variable may act on a response variable through a mediator .

If you’re dealing with a purely correlational relationship, there are no explanatory and response variables. Even if changes in one variable are associated with changes in another, both might be caused by a confounding variable .

Examples of explanatory and response variables

In some studies, you’ll have only one explanatory variable and one response variable, but in more complicated research, you may predict one or more response variable(s) using several explanatory variables in a model.

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Explanatory variables and independent variables are very similar, but there are subtle differences between them.

In research contexts, independent variables supposedly aren’t affected by or dependent on any other variable – they’re manipulated or altered only by researchers. For example, if you run a controlled experiment where you can control exactly how much caffeine each participant receives, then caffeine dose is an independent variable.

But sometimes, the term ‘explanatory variable’ is preferred over ‘independent variable’, because in real-world contexts, independent variables are often influenced by other variables. That means they’re not truly independent.

You gather a sample of young adults and ask them to complete a survey in the lab. They report their risk perceptions of different threatening scenarios while you record their stress reactions physiologically.

In your analyses, you find that gender and risk perception are highly correlated with each other. Women are likely to rate situations as riskier than men.

You’ll often see the terms ‘explanatory variable’ and ‘response variable’ used in regression analyses , which focus on predicting or accounting for changes in response variables as a result of explanatory variables.

The easiest way to visualise the relationship between an explanatory variable and a response variable is with a graph.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

When you have only one explanatory variable and one response variable, you’ll collect paired data . This means every response variable measurement is linked to an explanatory variable value for each unit or participant.

• Your explanatory variable is academic motivation at the start of the academic year.
• Your response variable is the grade point average (GPA) at the end of the academic year.

Academic motivation is assessed using an 8-point scale, while GPA can range from 0 to 4. To visualise your data, you plot academic motivation at the start of the year on the x -axis and GPA at the end of the year on the y -axis. Each data point reflects the paired data of one participant.

From the scatterplot, you can see a clear explanatory relationship between academic motivation at the start of the year and GPA at the end of the year.

• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

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Explanatory Variable & Response Variable: Simple Definition and Uses

What is an explanatory variable.

An explanatory variable is a type of independent variable . The two terms are often used interchangeably. But there is a subtle difference between the two. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.

Let’s say you had two variables to explain weight gain: fast food and soda. Although you might think that eating fast food intake and drinking soda are independent of each other, they aren’t really. That’s because fast food places encourage you to buy a soda with your meal. And if you stop somewhere to buy a soda, there’s often a lot of fast food options like nachos or hot dogs. Although these variables aren’t completely independent of each other, they do have an effect on weight gain. They are called explanatory variables because they may offer some explanation for the weight gain.

The line between independent variables and explanatory variables is usually so unimportant that no one ever bothers. That’s unless you’re doing some advanced research involving lots of variables that can interact with each other. It can be very important in clinical research . For most cases, especially in statistics, the two terms are basically the same.

Explanatory Variables vs. Response Variables

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 question is: which procedure prolongs life more? And so survival time is the response variable. The type of therapy given is the explanatory variable; it may or may not affect the response variable. In this example, we have only one explanatory variable: type of treatment. In real life you would have several more explanatory variables, including: age, health, weight and other lifestyle factors.

A scatterplot can help you see trends between paired data . If you have both a response variable and an explanatory variable, the explanatory variable is always plotted on the x-axis (the horizontal axis). The response variable is always plotted on the y-axis (the vertical axis).

Levine, D. (2014). Even You Can Learn Statistics and Analytics: An Easy to Understand Guide to Statistics and Analytics 3rd Edition. Pearson FT Press J Wilson at UGA COE. Assignment 2012.

Explanatory & Response Variables: Definition & Examples

Two of the most important types of variables to understand in statistics are  explanatory variables and response variables .

Explanatory Variable: Sometimes referred to as an  independent variable  or a  predictor variable , this variable explains the variation in the response variable.

Response Variable:  Sometimes referred to as a  dependent variable  or an  outcome variable , the value of this variable responds to changes in the explanatory variable.

In an experimental study, we’re typically interested in how the values of a response variable change as a result of the values of an explanatory variable being changed.

The following examples show different scenarios involving explanatory and response variables.

Example 1: Plant Growth

A botanist wants to compare the effect that two different fertilizers have on plant growth. She randomly selects 20 plants from a field and applies fertilizer A to them for one week. She also randomly selects another 20 plants from the same field and applies fertilizer B to them for one week. After one week she measures the average plant growth for each group.

In this example, we have:

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

A basketball coach wants to compare the effect that three different training programs have on player’s max vertical jump. He randomly assigns 10 players to use training program A for one week, another 10 players to use training program B for one week, and another 10 players to use training program C for one week. At the end of the week he measures the max vertical jump of each player to see if there are significant differences between the groups.

Explanatory Variable:  Type of training program used. This is the variable we change so that we can observe the effect it has on max vertical jump.

Response Variable: Max vertical jump. This is the variable that changes as a result of the training program used by the player.

Fun Fact:  We would use a one-way ANOVA  to perform this experiment.

Example 3: Real Estate Prices

A real estate agent wants to understand the relationship between square footage of a house and selling price. She collects data about square footage and selling price for 100 houses in her city and analyzes the relationship between the two variables.

Explanatory Variable: Square footage. This is the variable that we observe change in so that we can observe the effect it has on selling price.

Response Variable: Selling price. This is the variable that changes as a result of the square footage of the house being changed.

Fun Fact:  We would use simple linear regression  to perform this experiment.

In each of the examples above, we changed the values of some explanatory variable and observed the resulting change in values of some response variable.

Additional Resources

What is a Lurking Variable? What is a Confounding Variable? Independent vs. Dependent Variables: What’s the Difference?

Theoretical Probability: Definition + Examples

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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.

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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.

Response Variable : The response variable is another term for the dependent variable, as it "responds" to changes in the explanatory variable.

Independent Variable : The independent variable is another term for the explanatory variable, as it stands alone and is not influenced by other variables.

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Explanatory & Response Variable in Statistics — A quick guide for early career researchers!

Often researchers have a difficult time choosing the parameters and variables (like explanatory and response variables) that answer their aims and objectives in research study. The primary objective of any study is to determine whether there is a cause-and-effect relationship between variables.

Therefore, in experimental research, a variable is a factor that is not constant. An investigator can modify variables and control variables to determine if one variable has an effect on another variable in an experiment. However, researchers find it difficult to choose factors and parameters for their statistical study due to their lack of understanding of statistical variables.

There are several types of variables, but we will discuss two of the most commonly important variables — explanatory and response. This article will address their distinct attributes and their effects on research.

What Are Variables?

In statistics, a variable is defined as an attribute of an object of study.  It is a characteristic that can assume different values. Furthermore, height, age, income, grades obtained at school and type of housing are all examples of variables. However, in statistical studies there are many types of variables and some variables are used more often than others. For example, continuous variables are used more frequently than dummy variables.

Types of Variables

Variables are majorly categorized into two main categories mentioned below. Furthermore, each category is further classified into subcategories.

Categorical Variable

The categorical variable has limited number of values. Furthermore, this variable is a collection of information divided into groups and gives categorical data.

Categorical variables are of three types: Binary, nominal, and ordinal variables.

Numeric Variable

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.

Most Common Types of Variables

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.

What is Explanatory Variable?

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.

What is Response Variable?

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.

Explanatory Vs. Response 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.

Difference between Explanatory and Response Variables

• Explanatory variables are the variables that can be altered or manipulated in research (for example, a change in dosage) while response variable is the result of manipulation done to the variables (the time it took for the reaction to occur).
• An explanatory variable represents the expected cause that can explain the outcome of the research while response variables represent the effect that is expected as a response to the explanatory variable.
• Changes are visible in response variables only if changes occur in explanatory variables unlike explanatory variables that can change at any point in the test or research.
• Explanatory variables are the independent variables in a research. Meanwhile, response variables are the dependent variables.

Examples of Explanatory and Response Variables

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.

Example 2: Height & Age

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.

Example 3: Panda Fertility Treatments

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.

Example 4: Public Speaking Approaches

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.

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 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.

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1.4 Experimental Design and Ethics

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.

Example 1.19

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.

Try It 1.19

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.

Example 1.20

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.

• Describe the explanatory and response variables in this study.
• What are the treatments?
• Identify any lurking variables that could interfere with this study.
• Is it possible to use blinding in this study?
• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

Try It 1.20

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.

• Describe the explanatory and response variable in this study.

Example 1.21

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.

Try It 1.21

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?

• Describe the explanatory and response variables in the study.
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• How can blinding be used in this study?

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:

• creating datasets, which largely confirmed the prior expectations,
• altering data in existing datasets,
• changing measuring instruments without reporting the change, and
• misrepresenting the number of experimental subjects.

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:

• Risks to participants must be minimized and reasonable with respect to projected benefits.
• Participants must give informed consent . This means that the risks of participation must be clearly explained to the subjects of the study. Subjects must consent in writing, and researchers are required to keep documentation of their consent.
• Data collected from individuals must be guarded carefully to protect their privacy.

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.

Example 1.22

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.

• The researcher selects a block where they are comfortable walking because they know many of the people living on the street.
• No one seems to be home at four houses on the route. They do not record the addresses and do not return at a later time to try to find residents at home.
• The researcher skips four houses on the route because they are running late for an appointment. When they get home, they fill in the forms by selecting random answers from other residents in the neighborhood.
• By selecting a convenient sample, the researcher is intentionally selecting a sample that could be biased. Claiming that this sample represents the community is misleading. The researcher needs to select areas in the community at random.
• Intentionally omitting relevant data will create bias in the sample. Suppose the researcher is gathering information about jobs and child care. By ignoring people who are not home, They may be missing data from working families that are relevant to her study. They need to make every effort to interview all members of the target sample.
• It is never acceptable to fake data. Even though the responses the researcher are “real” responses provided by other participants, the duplication is fraudulent and can create bias in the data. They researcher needs to work diligently to interview everyone on their route.

Try It 1.22

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.

• The survey is commissioned by the seller of a popular brand of apple juice.
• There are only two types of juice included in the study: apple juice and cranberry juice.
• Researchers allow participants to see the brand of juice as samples are poured for a taste test.
• Twenty-five percent of participants prefer Brand X, 33% prefer Brand Y and 42% have no preference between the two brands. Brand X references the study in a commercial saying “Most teens like Brand X as much as or more than Brand Y.”
• 1 McClung, M. Collins, D. “Because I know it will!”: placebo effects of an ergogenic aid on athletic performance. Journal of Sport & Exercise Psychology. 2007 Jun. 29(3):382-94. Web. April 30, 2013.
• 2 Yudhijit Bhattacharjee, “The Mind of a Con Man,” Magazine, New York Times, April 26, 2013. Available online at: http://www.nytimes.com/2013/04/28/magazine/diederik-stapels-audacious-academic-fraud.html?src=dayp&_r=2& (accessed May 1, 2013).
• 3 “Flawed Science: The Fraudulent Research Practices of Social Psychologist Diederik Stapel,” Tillburg University, November 28, 2012, http://www.tilburguniversity.edu/upload/064a10cd-bce5-4385-b9ff-05b840caeae6_120695_Rapp_nov_2012_UK_web.pdf (accessed May 1, 2013).

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• Response vs Explanatory Variables: Definition & Examples

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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What is an Explanatory Variable?

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]

What is a Response Variable

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

Applications of the Explanatory Variable

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.

Applications of the Response Variable

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. 

Explanatory Variables vs. Response Variables

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.

Differences between Explanatory and Response Variables 

• Explanatory variables are the variables that can be altered or manipulated in research ( for example, a change in dosage) while response variables are the results of the manipulation done to the variables. ( The time it took for a reaction to occur)
• An explanatory variable represents the expected cause that can explain the outcome of the research while response variables represent the effect that is expected as a response to the explanatory variable.
• Changes are noticeable in response variables only if changes occurred in explanatory variables unlike e xplanatory variables that can change at any point in the test or research
• Explanatory variables are the independent variables in a research and response variables are the dependent variables. 

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Examples of Explanatory and Response Variables

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.

Visualizing Explanatory and Response Variables

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.

Frequently Asked Questions about Explanatory and Response Variables

• Is age an explanatory variable or a response variable?

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.

• Is time 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.

• Is time response or an explanatory 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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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:

• experimental unit
• explanatory variable
• response (outcome) variable
• confounding variable

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:

• set of twins
• married couple
• plot of land

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:

• type of plant
• type of drug
• type of medical procedure
• teaching method

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:

• temperature
• classification of whether a person is a vegetarian
• classification of symptom severity for an illness

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.

Example 2.3 Section  

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).

The Differences Between Explanatory and Response Variables

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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 .

Definitions of Explanatory and Response

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.

Example One

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.

Example Two

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.

Scatterplots and Variables

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.

Independent and Dependent

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.

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What are Explanatory Variables and Response Variables?

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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.

What is an Explanatory Variable?

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.

How to identify explanatory variables?

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.

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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. 

How to identify response variables?

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. 

Importance of understanding explanatory variables and response variables

Here are some reasons why understanding these variables is crucial in market research: 

• The variables can help you identify and understand the factors that drive consumer behavior. 
• You can make data-driven decisions about marketing strategies, product development, and pricing by analyzing the relationship between key variables. 
• It can also help you identify patterns in consumer behavior to make predictions of market trends and customer preferences.

Explanatory Variables. Response Variable

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,

• Alcohol is your Explanatory Variable 
• Driving Ability is your Response Variable. 

Causal relationship between explanatory and response variables

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.

Practical application of explanatory variables and response variables in market research

Let’s look at some examples of explanatory and response variables in market research. 

1. Market segmentation: 

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. 

2. Product development: 

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. 

3. Marketing communications:

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.

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Visualization of Explanatory and Response Variables in Scatterplot

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. 

• 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.

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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.

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Module 1: Sampling and Data

Components of experimental design, learning outcomes.

• For a given scenario, identify the explanatory variable, response variable, treatments, experimental units, lurking variables and control group
• Explain how blinding could be used in the design of an experiment

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.

• Describe the explanatory and response variables in this study.
• What are the treatments?
• Identify any lurking variables that could interfere with this study.
• Is it possible to use blinding in this study?
• The explanatory variable is scent, and the response variable is the time it takes to complete the maze.
• There are two treatments: a floral-scented mask and an unscented mask.
• All subjects experienced both treatments. The order of treatments was randomly assigned so there were no differences between the treatment groups. Random assignment eliminates the problem of lurking variables.
• Subjects will clearly know whether they can smell flowers or not, so subjects cannot be blinded in this study. Researchers timing the mazes can be blinded, though. The researcher who is observing a subject will not know which mask is being worn.

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?

• Describe the explanatory and response variables in the study.
• What should you consider when selecting participants?
• Your research partner wants to divide participants randomly into two groups: one to drive without distraction and one to text and drive simultaneously. Is this a good idea? Why or why not?
• How can blinding be used in this study?
• Experimental Design and Ethics. Provided by : OpenStax. Located at : https://openstax.org/books/introductory-statistics/pages/1-4-experimental-design-and-ethics . License : CC BY: Attribution . License Terms : Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction
• Introductory Statistics. Authored by : Barbara Illowsky, Susan Dean. Provided by : Open Stax. Located at : https://openstax.org/books/introductory-statistics/pages/1-introduction . License : CC BY: Attribution . License Terms : Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction
• Observational Studies and Experiments. Authored by : ProfessorMcComb. Located at : https://www.youtube.com/watch?v=J_O7ibkX8Ik . License : All Rights Reserved . License Terms : Standard YouTube License

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• Knowledge Base

Methodology

• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

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.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

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.

Table of contents

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:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

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.

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There are two main types of independent variables.

• Experimental independent variables can be directly manipulated by researchers.
• Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental 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 low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

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

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:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

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.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

• Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
• Does this variable come before the other variable in time?
• Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

• Is this variable measured as an outcome of the study?
• Is this variable dependent on another variable in the study?
• Does this variable get measured only after other variables are altered?

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.

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:

• your variable types
• level of measurement
• number of independent variable levels.

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:

• A bar chart is ideal when you have a categorical independent variable.
• A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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 .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

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.

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