control group with an experimental group

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Related Posts

Control Group Design: The Cornerstone of True Experimental Research

Table of Contents

Have you ever wondered how scientists determine the effectiveness of a new medication or therapeutic technique? The answer lies within a cornerstone of psychological research: the control group design . This powerful tool allows researchers to uncover the true effects of an intervention by comparing outcomes between treated and untreated groups. So, let’s dive into the intricacies of this design and uncover why it’s so pivotal in the scientific quest for knowledge.

What is control group design?

Control group design is a methodological approach where one group receives the experimental treatment, while a separate ‘control’ group does not. The control group serves as a benchmark to measure the effect of the variable being tested. This comparison can reveal whether changes in the experimental group are indeed due to the treatment or if they could be attributed to other factors.

The different forms of control group design

Though the concept might seem straightforward, control group design is nuanced and can be executed in various forms, each tailored to address specific research questions and concerns.

Post\-test only control group design

This form involves two groups: one receiving the treatment and the other not. Both groups are measured after the treatment period, providing data on the effect of the treatment. This design is particularly useful when pretesting might influence the participants’ responses to the treatment.

Pretest\-posttest control group design

In this approach, both the experimental and control groups are measured before and after the treatment. The pretest ensures that any changes observed in the post-test can be attributed to the treatment rather than to pre-existing differences between the groups.

Addressing validity concerns with control group design

Control group design doesn’t just sort groups and compare outcomes; it’s a sophisticated strategy to bolster the study’s validity. Let’s delve into how it safeguards against threats to experimental validity.

Internal validity

Internal validity refers to the degree to which we can be confident that the change in the dependent variable was indeed caused by the independent variable, and not by other factors. Control groups help to rule out alternative explanations by ensuring that the only difference between groups is the treatment variable.

External validity

External validity is about the generalizability of the findings. By using control groups that closely resemble the target population, researchers can make stronger claims about how their findings might apply in real-world settings.

The Solomon Four Group Design

What if you’re concerned about both pretesting effects and external validity? Enter the Solomon Four Group Design. This robust method combines both post-test only and pretest-posttest configurations across four different groups, providing a comprehensive safeguard against validity threats.

How it works

The Solomon Four Group Design involves four groups, where two receive the treatment and two serve as controls. One treated and one control group are pretested, while the others are not. This design helps identify any pretesting effects and further isolates the treatment’s impact, offering a fuller picture of the treatment’s effectiveness.

Ensuring research integrity with control group design

Control group design is more than just a way to compare outcomes. It’s a fundamental approach to ensuring that research findings are accurate, reliable, and applicable. By methodically controlling for extraneous variables and threats to validity, researchers can draw more definitive conclusions about the effects of their treatments.

Minimizing biases

By randomly assigning participants to the experimental or control groups, control group design minimizes selection biases, ensuring that the groups are comparable at the start of the experiment.

Enhancing replicability

Control group design also enhances the replicability of research. By providing a clear structure for the experiment, other researchers can replicate the study to confirm its findings, which is a fundamental aspect of the scientific method.

The control group design is a testament to the meticulous nature of scientific inquiry. By thoughtfully comparing treated and untreated groups, researchers can illuminate the true effects of a variable, paving the way for discoveries that can enhance our understanding of human behavior and improve psychological treatments. It’s a method that epitomizes the rigor and integrity of experimental research in psychology.

What do you think? How might the control group design be applied to current issues in psychology? Can you think of a situation where a control group design might not be the best approach for a study?

How useful was this post?

Click on a star to rate it!

Average rating 0 / 5. Vote count: 0

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Submit Comment

Research Methods in Psychology

1 Introduction to Psychological Research – Objectives and Goals, Problems, Hypothesis and Variables

• Nature of Psychological Research
• The Context of Discovery
• Context of Justification
• Characteristics of Psychological Research
• Goals and Objectives of Psychological Research

2 Introduction to Psychological Experiments and Tests

• Independent and Dependent Variables
• Extraneous Variables
• Experimental and Control Groups
• Introduction of Test
• Types of Psychological Test
• Uses of Psychological Tests

3 Steps in Research

• Research Process
• Identification of the Problem
• Review of Literature
• Formulating a Hypothesis
• Identifying Manipulating and Controlling Variables
• Formulating a Research Design
• Constructing Devices for Observation and Measurement
• Sample Selection and Data Collection
• Data Analysis and Interpretation
• Hypothesis Testing
• Drawing Conclusion

4 Types of Research and Methods of Research

• Historical Research
• Descriptive Research
• Correlational Research
• Qualitative Research
• Ex-Post Facto Research
• True Experimental Research
• Quasi-Experimental Research

5 Definition and Description Research Design, Quality of Research Design

• Research Design
• Purpose of Research Design
• Design Selection
• Criteria of Research Design
• Qualities of Research Design

6 Experimental Design (Control Group Design and Two Factor Design)

• Experimental Design
• Control Group Design
• Two Factor Design

7 Survey Design

• Survey Research Designs
• Steps in Survey Design
• Structuring and Designing the Questionnaire
• Interviewing Methodology
• Data Analysis
• Final Report

8 Single Subject Design

• Single Subject Design: Definition and Meaning
• Phases Within Single Subject Design
• Requirements of Single Subject Design
• Characteristics of Single Subject Design
• Types of Single Subject Design
• Advantages of Single Subject Design
• Disadvantages of Single Subject Design

9 Observation Method

• Definition and Meaning of Observation
• Characteristics of Observation
• Types of Observation
• Advantages and Disadvantages of Observation
• Guides for Observation Method

10 Interview and Interviewing

• Definition of Interview
• Types of Interview
• Aspects of Qualitative Research Interviews
• Interview Questions
• Convergent Interviewing as Action Research
• Research Team

11 Questionnaire Method

• Definition and Description of Questionnaires
• Types of Questionnaires
• Purpose of Questionnaire Studies
• Designing Research Questionnaires
• The Methods to Make a Questionnaire Efficient
• The Types of Questionnaire to be Included in the Questionnaire
• Advantages and Disadvantages of Questionnaire
• When to Use a Questionnaire?

12 Case Study

• Definition and Description of Case Study Method
• Historical Account of Case Study Method
• Designing Case Study
• Requirements for Case Studies
• Guideline to Follow in Case Study Method
• Other Important Measures in Case Study Method
• Case Reports

13 Report Writing

• Purpose of a Report
• Writing Style of the Report
• Report Writing – the Do’s and the Don’ts
• Format for Report in Psychology Area
• Major Sections in a Report

14 Review of Literature

• Purposes of Review of Literature
• Sources of Review of Literature
• Types of Literature
• Writing Process of the Review of Literature
• Preparation of Index Card for Reviewing and Abstracting

15 Methodology

• Definition and Purpose of Methodology
• Participants (Sample)
• Apparatus and Materials

16 Result, Analysis and Discussion of the Data

• Definition and Description of Results
• Statistical Presentation
• Tables and Figures

17 Summary and Conclusion

• Summary Definition and Description
• Guidelines for Writing a Summary
• Writing the Summary and Choosing Words
• A Process for Paraphrasing and Summarising
• Summary of a Report
• Writing Conclusions

18 References in Research Report

• Reference List (the Format)
• References (Process of Writing)
• Reference List and Print Sources
• Electronic Sources
• Book on CD Tape and Movie
• Reference Specifications
• General Guidelines to Write References

Share on Mastodon

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center
• When did science begin?
• Where was science invented?

control group

Our editors will review what you’ve submitted and determine whether to revise the article.

• Verywell Mind - What Is a Control Group?
• National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Control Group in an Experiment

By Jim Frost 3 Comments

A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.

Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.

By serving as a basis for comparison, the control group reveals the treatment’s effect.

Related post : Effect Sizes in Statistics

Using Control Groups in Experiments

Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.

Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .

Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.

For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .

Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .

Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!

Related posts : When to Use Regression Analysis

Example of a Control Group

Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:

• Control group : Does not consume vitamin supplements
• Treatment group : Regularly consumes vitamin supplements.

In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.

After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!

Types of Control Groups

Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.

Negative Control Group

The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.

Positive Control Group

Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.

For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.

In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.

Placebo Control Group

Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.

Learn more about the Placebo Effect .

Blinded Control Groups

If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.

Waitlist Control Group

When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.

Historical Control Group

When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.

Share this:

Reader Interactions

December 19, 2021 at 9:17 am

Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur

December 17, 2021 at 4:46 pm

Thank you very much Jim, very interesting article.

Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur

December 18, 2021 at 1:51 am

You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.

If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?

If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.

If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.

So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!

Best of luck with your research!

Comments and Questions Cancel reply

Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

Qualitative data analysis starts with ATLAS.ti

Turn data into rich insights with our powerful data analysis software. Get started with a free trial.

Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

Analyze data and generate rich results with ATLAS.ti

Try out a free trial of ATLAS.ti to see how you can make the most of your qualitative data.

Control Group vs. Experimental Group: Everything You Need To Know About The Difference Between Control Group And Experimental Group

As someone who is deeply interested in the field of research, you may have heard the terms control group and experimental group thrown around a lot. If you’re not very familiar with these terms, it can be daunting to determine the role they play in research and why they are so important. In layman’s terms, a control group is a group that does not receive any experimental treatment and is used as a benchmark for the group that does receive the treatment. Meanwhile, the experimental group is a group that receives the treatment and is compared to the control group that does not receive the treatment. To put it simply, the main difference between a control group and an experimental group is whether or not they receive the experimental treatment.

Table of Contents

What Is Control Group?

A control group is a group in an experiment that does not receive the experimental treatment and is used as a comparison for the group that does receive the treatment. It is a critical aspect of experimental research to determine whether the treatment caused the outcome rather than another factor. The control group ensures that any observed effects can be attributed to the treatment and not a result of other variables. The quality of the control group can affect the validity of the experiment. Therefore, researchers must carefully design and select participants for the control group to ensure that it accurately represents the population and provides meaningful results. Overall, control groups are essential to gain accurate and reliable results in experimental research.

What Is Experimental Group?

Key differences between control group and experimental group, control group vs. experimental group similarities.

The control group and experimental group are two essential components of any research study. The main similarity between these groups is that they are both used to assess the effects of a treatment or intervention. The control group is intended to provide a baseline measurement of the outcomes that are expected in the absence of the intervention. In contrast, the experimental group is exposed to the intervention or treatment and is observed for any changes or improvements in outcomes. In summary, both groups serve as comparisons for one another, and their use increases the credibility and validity of research findings.

Control Group vs. Experimental Group Pros and Cons

Control group pros & cons, control group pros, control group cons, experimental group pros & cons, experimental group pros.

The Experimental Group, in scientific studies and experimentation, is a group that receives the experimental treatment and is compared to a control group that does not receive the treatment. There are several advantages or pros of this group. First, the experimental group allows researchers to determine the effectiveness of a new treatment or procedure. Second, it helps in identifying side effects of the treatment on the subjects. Third, it provides clear evidence regarding the cause and effect relationships between variables. Additionally, the experimental group enables researchers to validate their findings and test the hypothesis. These benefits make the Experimental Group essential in accurately assessing the effectiveness of new treatments or procedures.

Experimental Group Cons

Comparison table: 5 key differences between control group and experimental group.

Comparison Chart

Comparison video, conclusion: what is the difference between control group and experimental group.

Federation vs. Confederation: Everything You Need To Know About The Difference Between Federation And Confederation

Farthest vs. Furthest: Everything You Need To Know About The Difference Between Farthest And Furthest

Miralax vs. Colace: Everything You Need To Know About The Difference Between Miralax And Colace

Dulcolax vs. Miralax: Everything You Need To Know About The Difference Between Dulcolax And Miralax

Adh vs. Aldosterone: Everything You Need To Know About The Difference Between Adh And Aldosterone

Chromebook vs. Laptop: Everything You Need To Know About The Difference Between Chromebook And Laptop

Leave a reply cancel reply, add difference 101 to your homescreen.

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Understanding Experimental Groups

• Chemical Laws
• Periodic Table
• Projects & Experiments
• Scientific Method
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate

Scientific experiments often include two groups: the experimental group and the control group . Here's a closer look at the experimental group and how to distinguish it from the experimental group.

Key Takeaways: Experimental Group

• The experimental group is the set of subjects exposed to a change in the independent variable. While it's technically possible to have a single subject for an experimental group, the statistical validity of the experiment will be vastly improved by increasing the sample size.
• In contrast, the control group is identical in every way to the experimental group, except the independent variable is held constant. It's best to have a large sample size for the control group, too.
• It's possible for an experiment to contain more than one experimental group. However, in the cleanest experiments, only one variable is changed.

Experimental Group Definition

An experimental group in a scientific experiment is the group on which the experimental procedure is performed. The independent variable is changed for the group and the response or change in the dependent variable is recorded. In contrast, the group that does not receive the treatment or in which the independent variable is held constant is called the control group .

The purpose of having experimental and control groups is to have sufficient data to be reasonably sure the relationship between the independent and dependent variable is not due to chance. If you perform an experiment on only one subject (with and without treatment) or on one experimental subject and one control subject you have limited confidence in the outcome. The larger the sample size, the more probable the results represent a real correlation .

Example of an Experimental Group

You may be asked to identify the experimental group in an experiment as well as the control group. Here's an example of an experiment and how to tell these two key groups apart .

Let's say you want to see whether a nutritional supplement helps people lose weight. You want to design an experiment to test the effect. A poor experiment would be to take a supplement and see whether or not you lose weight. Why is it bad? You only have one data point! If you lose weight, it could be due to some other factor. A better experiment (though still pretty bad) would be to take the supplement, see if you lose weight, stop taking the supplement and see if the weight loss stops, then take it again and see if weight loss resumes. In this "experiment" you are the control group when you are not taking the supplement and the experimental group when you are taking it.

It's a terrible experiment for a number of reasons. One problem is that the same subject is being used as both the control group and the experimental group. You don't know, when you stop taking treatment, that is doesn't have a lasting effect. A solution is to design an experiment with truly separate control and experimental groups.

If you have a group of people who take the supplement and a group of people who do not, the ones exposed to the treatment (taking the supplement) are the experimental group. The ones not-taking it are the control group.

How to Tell Control and Experimental Group Apart

In an ideal situation, every factor that affects a member of both the control group and experimental group is exactly the same except for one -- the independent variable . In a basic experiment, this could be whether something is present or not. Present = experimental; absent = control.

Sometimes, it's more complicated and the control is "normal" and the experimental group is "not normal". For example, if you want to see whether or not darkness has an effect on plant growth. Your control group might be plants grown under ordinary day/night conditions. You could have a couple of experimental groups. One set of plants might be exposed to perpetual daylight, while another might be exposed to perpetual darkness. Here, any group where the variable is changed from normal is an experimental group. Both the all-light and all-dark groups are types of experimental groups.

Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.

Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.

• The Difference Between Control Group and Experimental Group
• What Is a Control Group?
• What Is an Experiment? Definition and Design
• How to Calculate Experimental Error in Chemistry
• Scientific Method Lesson Plan
• 10 Things You Need To Know About Chemistry
• Hydroxyl Group Definition in Chemistry
• Chemistry 101 - Introduction & Index of Topics
• Can You Really Turn Lead Into Gold?
• Molecular Mass Definition
• How to Write a Lab Report
• Platinum Group Metals (PGMs)
• Defining Prepregs
• Periodic Table Study Guide - Introduction & History
• Catalysts Definition and How They Work
• Activation Energy Definition in Chemistry

Frequently asked questions

What’s the difference between a control group and an experimental group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

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 cola and regular cola, so you conduct an experiment .

• The type of cola – 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 cola.

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.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

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 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.

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.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

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.

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)

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:

• 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)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Ask our team

Want to contact us directly? No problem. We are always here for you.

• Chat with us
• Email [email protected]
• Call +44 (0)20 3917 4242
• WhatsApp +31 20 261 6040

Our support team is here to help you daily via chat, WhatsApp, email, or phone between 9:00 a.m. to 11:00 p.m. CET.

Our APA experts default to APA 7 for editing and formatting. For the Citation Editing Service you are able to choose between APA 6 and 7.

Yes, if your document is longer than 20,000 words, you will get a sample of approximately 2,000 words. This sample edit gives you a first impression of the editor’s editing style and a chance to ask questions and give feedback.

How does the sample edit work?

You will receive the sample edit within 24 hours after placing your order. You then have 24 hours to let us know if you’re happy with the sample or if there’s something you would like the editor to do differently.

Read more about how the sample edit works

Yes, you can upload your document in sections.

We try our best to ensure that the same editor checks all the different sections of your document. When you upload a new file, our system recognizes you as a returning customer, and we immediately contact the editor who helped you before.

However, we cannot guarantee that the same editor will be available. Your chances are higher if

• You send us your text as soon as possible and
• You can be flexible about the deadline.

Please note that the shorter your deadline is, the lower the chance that your previous editor is not available.

If your previous editor isn’t available, then we will inform you immediately and look for another qualified editor. Fear not! Every Scribbr editor follows the  Scribbr Improvement Model  and will deliver high-quality work.

Yes, our editors also work during the weekends and holidays.

Because we have many editors available, we can check your document 24 hours per day and 7 days per week, all year round.

If you choose a 72 hour deadline and upload your document on a Thursday evening, you’ll have your thesis back by Sunday evening!

Yes! Our editors are all native speakers, and they have lots of experience editing texts written by ESL students. They will make sure your grammar is perfect and point out any sentences that are difficult to understand. They’ll also notice your most common mistakes, and give you personal feedback to improve your writing in English.

Every Scribbr order comes with our award-winning Proofreading & Editing service , which combines two important stages of the revision process.

For a more comprehensive edit, you can add a Structure Check or Clarity Check to your order. With these building blocks, you can customize the kind of feedback you receive.

You might be familiar with a different set of editing terms. To help you understand what you can expect at Scribbr, we created this table:

View an example

When you place an order, you can specify your field of study and we’ll match you with an editor who has familiarity with this area.

However, our editors are language specialists, not academic experts in your field. Your editor’s job is not to comment on the content of your dissertation, but to improve your language and help you express your ideas as clearly and fluently as possible.

This means that your editor will understand your text well enough to give feedback on its clarity, logic and structure, but not on the accuracy or originality of its content.

Good academic writing should be understandable to a non-expert reader, and we believe that academic editing is a discipline in itself. The research, ideas and arguments are all yours – we’re here to make sure they shine!

After your document has been edited, you will receive an email with a link to download the document.

The editor has made changes to your document using ‘Track Changes’ in Word. This means that you only have to accept or ignore the changes that are made in the text one by one.

It is also possible to accept all changes at once. However, we strongly advise you not to do so for the following reasons:

• You can learn a lot by looking at the mistakes you made.
• The editors don’t only change the text – they also place comments when sentences or sometimes even entire paragraphs are unclear. You should read through these comments and take into account your editor’s tips and suggestions.
• With a final read-through, you can make sure you’re 100% happy with your text before you submit!

You choose the turnaround time when ordering. We can return your dissertation within 24 hours , 3 days or 1 week . These timescales include weekends and holidays. As soon as you’ve paid, the deadline is set, and we guarantee to meet it! We’ll notify you by text and email when your editor has completed the job.

Very large orders might not be possible to complete in 24 hours. On average, our editors can complete around 13,000 words in a day while maintaining our high quality standards. If your order is longer than this and urgent, contact us to discuss possibilities.

Always leave yourself enough time to check through the document and accept the changes before your submission deadline.

Scribbr is specialised in editing study related documents. We check:

• Graduation projects
• Dissertations
• Admissions essays
• College essays
• Application essays
• Personal statements
• Process reports
• Reflections
• Internship reports
• Academic papers
• Research proposals
• Prospectuses

Calculate the costs

The fastest turnaround time is 24 hours.

You can upload your document at any time and choose between four deadlines:

At Scribbr, we promise to make every customer 100% happy with the service we offer. Our philosophy: Your complaint is always justified – no denial, no doubts.

Our customer support team is here to find the solution that helps you the most, whether that’s a free new edit or a refund for the service.

Yes, in the order process you can indicate your preference for American, British, or Australian English .

If you don’t choose one, your editor will follow the style of English you currently use. If your editor has any questions about this, we will contact you.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Experimental Group in Psychology Experiments

In a randomized and controlled psychology experiment , the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.

The participants who are in the experimental condition are those who receive the treatment or intervention of interest. The data from their outcomes are collected and compared to the data from a group that did not receive the experimental treatment. The control group may have received no treatment at all, or they may have received a placebo treatment or the standard treatment in current practice.

Comparing the experimental group to the control group allows researchers to see how much of an impact the intervention had on the participants.

A Closer Look at Experimental Groups

Imagine that you want to do an experiment to determine if listening to music while working out can lead to greater weight loss. After getting together a group of participants, you randomly assign them to one of three groups. One group listens to upbeat music while working out, one group listens to relaxing music, and the third group listens to no music at all. All of the participants work out for the same amount of time and the same number of days each week.

In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups.   They each receive some level of the independent variable, which in this case is listening to music while working out.

In this experiment, you find that the participants who listened to upbeat music experienced the greatest weight loss result, largely because those who listened to this type of music exercised with greater intensity than those in the other two groups. By comparing the results from your experimental groups with the results of the control group, you can more clearly see the impact of the independent variable.  

Some Things to Know

When it comes to using experimental groups in a psychology experiment, there are a few important things to know:

• In order to determine the impact of an independent variable, it is important to have at least two different treatment conditions. This usually involves using a control group that receives no treatment against an experimental group that receives the treatment. However, there can also be a number of different experimental groups in the same experiment.
• Care must be taken when assigning participants to groups. So how do researchers determine who is in the control group and who is in the experimental group? In an ideal situation, the researchers would use random assignment to place participants in groups. In random assignment, each individual stands an equal shot at being assigned to either group. Participants might be randomly assigned using methods such as a coin flip or a number draw. By using random assignment, researchers can help ensure that the groups are not unfairly stacked with people who share characteristics that might unfairly skew the results.
• Variables must be well-defined. Before you begin manipulating things in an experiment, you need to have very clear operational definitions in place. These definitions clearly explain what your variables are, including exactly how you are manipulating the independent variable and exactly how you are measuring the outcomes.

A Word From Verywell

Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group. The goal of this experimental manipulation is to gain a better understanding of the different factors that may have an impact on how people think, feel, and act.

Byrd-Bredbenner C, Wu F, Spaccarotella K, Quick V, Martin-Biggers J, Zhang Y. Systematic review of control groups in nutrition education intervention research . Int J Behav Nutr Phys Act. 2017;14(1):91. doi:10.1186/s12966-017-0546-3

Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging. 2015;10:1189-1200. doi:10.2147/CIA.S81868

Oberste M, Hartig P, Bloch W, et al. Control group paradigms in studies investigating acute effects of exercise on cognitive performance—An experiment on expectation-driven placebo effects . Front Hum Neurosci. 2017;11:600. doi:10.3389/fnhum.2017.00600

Kim H. Statistical notes for clinical researchers: Analysis of covariance (ANCOVA) . Restor Dent Endod . 2018;43(4):e43. doi:10.5395/rde.2018.43.e43

Bate S, Karp NA. A common control group — Optimising the experiment design to maximise sensitivity . PLoS ONE. 2014;9(12):e114872. doi:10.1371/journal.pone.0114872

Myers A, Hansen C. Experimental Psychology . 7th Ed. Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
• measuring variables to statistically control for them in your analyses.
• balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal,
• Study environment (e.g., temperature or lighting),
• Participant’s frequency of buying fast food,
• Participant’s familiarity with the specific fast food brand,
• Participant’s socioeconomic status.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• A control group that’s presented with red advertisements for a fast food meal.
• An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

Cite this Scribbr article

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). What Is a Controlled Experiment? | Definitions & Examples. Scribbr. Retrieved August 5, 2024, from https://www.scribbr.com/methodology/controlled-experiment/

Is this article helpful?

Pritha Bhandari

Other students also liked, extraneous variables | examples, types & controls, guide to experimental design | overview, steps, & examples, how to write a lab report, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

✂️ The Future of Marketing Is Personal: Personalize Experiences at Scale with Ninetailed AI Platform Ninetailed AI →

Control Group

What is a control group in an experiment.

A control group is a set of subjects in an experiment who are not exposed to the independent variable. The purpose of a control group is to serve as a baseline for comparison. By having a group that is not exposed to the treatment, researchers can compare the results of the experimental group and determine whether the independent variable had an impact.

In some cases, there may be more than one control group. This is often done when there are multiple treatments or when researchers want to compare different groups of subjects. Having multiple control groups allows researchers to isolate the effect of each treatment and better understand how each one works.

Control groups are an important part of any experiment, as they help ensure that the results are accurate and reliable. Without a control group, it would be difficult to determine whether the results of an experiment are due to the independent variable or other factors.

When designing an experiment, it is important to carefully consider what kind of control group you will need. There are many different ways to set up a control group, and the best approach will depend on the specific goals of your research.

Control Group vs. Experimental Group

A control group is a group in an experiment that does not receive the experimental treatment. The purpose of a control group is to provide a baseline against which to compare the experimental group results.

An experimental group is a group in an experiment that receives the experimental treatment. The purpose of an experimental group is to test whether or not the experimental treatment has an effect.

The differences between control and experimental groups are important to consider when designing an experiment. The most important difference is that the control group provides a comparison for the results of the experimental group. This comparison is essential in order to determine whether or not the experimental treatment had an effect. Without a control group, it would be impossible to know if the results of the experiment are due to the treatment or not.

Another important difference between a control group and an experimental group is that the experimental group is the only group that receives the experimental treatment. This is necessary in order to ensure that any results seen in the experimental group can be attributed to the treatment and not to other factors.

Control groups and experimental groups are both essential parts of experiments. Without a control group, it would be impossible to know if the results of an experiment are due to the treatment or not. Without an experimental group, it would be impossible to test whether or not a treatment has an effect.

What Is the Purpose of a Control Group

The purpose of a control group is to serve as a baseline for comparison. By having a group that is not exposed to the treatment, researchers can compare the results of the experimental group and determine whether the independent variable had an impact.

Why Is a Control Group Important in an Experiment

A control group is an essential part of any experiment. It is a group of subjects who are not exposed to the independent variable being tested. The purpose of a control group is to provide a baseline against which the results from the treatment group can be compared.

Without a control group, it would be impossible to determine whether the results of an experiment are due to the treatment or some other factor. For example, imagine you are testing the effects of a new drug on patients with high blood pressure. If you did not have a control group, you would not know if the decrease in blood pressure was due to the drug or something else, such as the placebo effect.

A control group must be carefully designed to match the treatment group in all important respects, except for the one factor that is being tested. This ensures that any differences in the results can be attributed to the independent variable and not to other factors.

Get a weekly roundup of Ninetailed updates, curated posts, and helpful insights about the digital experience, MACH, composable, and more right into your inbox

Keep Reading on This Topic

In this blog post, we will explore nine of the most common personalization challenges and discuss how to overcome them.

In this post, we will discuss some of the best practices and tips for using website content personalization to delight your customers and enhance user experiences.

• Skip to primary navigation
• Skip to main content
• Skip to footer

Understanding Science

How science REALLY works...

Frequently asked questions about how science works

The Understanding Science site is assembling an expanded list of FAQs for the site and you can contribute. Have a question about how science works, what science is, or what it’s like to be a scientist? Send it to  [email protected] !

Expand the individual panels to reveal the answers or Expand all | Collapse all

What is the scientific method?

The “scientific method” is traditionally presented in the first chapter of science textbooks as a simple, linear, five- or six-step procedure for performing scientific investigations. Although the Scientific Method captures the core logic of science (testing ideas with evidence), it misrepresents many other aspects of the true process of science — the dynamic, nonlinear, and creative ways in which science is actually done. In fact, the Scientific Method more accurately describes how science is summarized  after the fact  — in textbooks and journal articles — than how scientific research is actually performed. Teachers may ask that students use the format of the scientific method to write up the results of their investigations (e.g., by reporting their  question, background information, hypothesis, study design, data analysis,  and  conclusion ), even though the process that students went through in their investigations may have involved many iterations of questioning, background research, data collection, and data analysis and even though the students’ “conclusions” will always be tentative ones. To learn more about how science really works and to see a more accurate representation of this process, visit  The  real  process of science .

Why do scientists often seem tentative about their explanations?

Scientists often seem tentative about their explanations because they are aware that those explanations could change if new evidence or perspectives come to light. When scientists write about their ideas in journal articles, they are expected to carefully analyze the evidence for and against their ideas and to be explicit about alternative explanations for what they are observing. Because they are trained to do this for their scientific writing, scientist often do the same thing when talking to the press or a broader audience about their ideas. Unfortunately, this means that they are sometimes misinterpreted as being wishy-washy or unsure of their ideas. Even worse, ideas supported by masses of evidence are sometimes discounted by the public or the press because scientists talk about those ideas in tentative terms. It’s important for the public to recognize that, while provisionality is a fundamental characteristic of scientific knowledge, scientific ideas supported by evidence are trustworthy. To learn more about provisionality in science, visit our page describing  how science builds knowledge . To learn more about how this provisionality can be misinterpreted, visit a section of the  Science toolkit .

Why is peer review useful?

Peer review helps assure the quality of published scientific work: that the authors haven’t ignored key ideas or lines of evidence, that the study was fairly-designed, that the authors were objective in their assessment of their results, etc. This means that even if you are unfamiliar with the research presented in a particular peer-reviewed study, you can trust it to meet certain standards of scientific quality. This also saves scientists time in keeping up-to-date with advances in their fields by weeding out untrustworthy studies. Peer-reviewed work isn’t necessarily correct or conclusive, but it does meet the standards of science. To learn more, visit  Scrutinizing science .

What is the difference between independent and dependent variables?

In an experiment, the independent variables are the factors that the experimenter manipulates. The dependent variable is the outcome of interest—the outcome that depends on the experimental set-up. Experiments are set-up to learn more about how the independent variable does or does not affect the dependent variable. So, for example, if you were testing a new drug to treat Alzheimer’s disease, the independent variable might be whether or not the patient received the new drug, and the dependent variable might be how well participants perform on memory tests. On the other hand, to study how the temperature, volume, and pressure of a gas are related, you might set up an experiment in which you change the volume of a gas, while keeping the temperature constant, and see how this affects the gas’s pressure. In this case, the independent variable is the gas’s volume, and the dependent variable is the pressure of the gas. The temperature of the gas is a controlled variable. To learn more about experimental design, visit Fair tests: A do-it-yourself guide .

What is a control group?

In scientific testing, a control group is a group of individuals or cases that is treated in the same way as the experimental group, but that is not exposed to the experimental treatment or factor. Results from the experimental group and control group can be compared. If the control group is treated very similarly to the experimental group, it increases our confidence that any difference in outcome is caused by the presence of the experimental treatment in the experimental group. For an example, visit our side trip  Fair tests in the field of medicine .

What is the difference between a positive and a negative control group?

A negative control group is a control group that is not exposed to the experimental treatment or to any other treatment that is expected to have an effect. A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect. These sorts of controls are particularly useful for validating the experimental procedure. For example, imagine that you wanted to know if some lettuce carried bacteria. You set up an experiment in which you wipe lettuce leaves with a swab, wipe the swab on a bacterial growth plate, incubate the plate, and see what grows on the plate. As a negative control, you might just wipe a sterile swab on the growth plate. You would not expect to see any bacterial growth on this plate, and if you do, it is an indication that your swabs, plates, or incubator are contaminated with bacteria that could interfere with the results of the experiment. As a positive control, you might swab an existing colony of bacteria and wipe it on the growth plate. In this case, you  would  expect to see bacterial growth on the plate, and if you do not, it is an indication that something in your experimental set-up is preventing the growth of bacteria. Perhaps the growth plates contain an antibiotic or the incubator is set to too high a temperature. If either the positive or negative control does not produce the expected result, it indicates that the investigator should reconsider his or her experimental procedure. To learn more about experimental design, visit  Fair tests: A do-it-yourself guide .

What is a correlational study, and how is it different from an experimental study?

In a correlational study, a scientist looks for associations between variables (e.g., are people who eat lots of vegetables less likely to suffer heart attacks than others?) without manipulating any variables (e.g., without asking a group of people to eat more or fewer vegetables than they usually would). In a correlational study, researchers may be interested in any sort of statistical association — a positive relationship among variables, a negative relationship among variables, or a more complex one. Correlational studies are used in many fields (e.g., ecology, epidemiology, astronomy, etc.), but the term is frequently associated with psychology. Correlational studies are often discussed in contrast to experimental studies. In experimental studies, researchers do manipulate a variable (e.g., by asking one group of people to eat more vegetables and asking a second group of people to eat as they usually do) and investigate the effect of that change. If an experimental study is well-designed, it can tell a researcher more about the cause of an association than a correlational study of the same system can. Despite this difference, correlational studies still generate important lines of evidence for testing ideas and often serve as the inspiration for new hypotheses. Both types of study are very important in science and rely on the same logic to relate evidence to ideas. To learn more about the basic logic of scientific arguments, visit  The core of science .

What is the difference between deductive and inductive reasoning?

Deductive reasoning involves logically extrapolating from a set of premises or hypotheses. You can think of this as logical “if-then” reasoning. For example, IF an asteroid strikes Earth, and IF iridium is more prevalent in asteroids than in Earth’s crust, and IF nothing else happens to the asteroid iridium afterwards, THEN there will be a spike in iridium levels at Earth’s surface. The THEN statement is the logical consequence of the IF statements. Another case of deductive reasoning involves reasoning from a general premise or hypothesis to a specific instance. For example, based on the idea that all living things are built from cells, we might  deduce  that a jellyfish (a specific example of a living thing) has cells. Inductive reasoning, on the other hand, involves making a generalization based on many individual observations. For example, a scientist who samples rock layers from the Cretaceous-Tertiary (KT) boundary in many different places all over the world and always observes a spike in iridium may  induce  that all KT boundary layers display an iridium spike. The logical leap from many individual observations to one all-inclusive statement isn’t always warranted. For example, it’s possible that, somewhere in the world, there is a KT boundary layer without the iridium spike. Nevertheless, many individual observations often make a strong case for a more general pattern. Deductive, inductive, and other modes of reasoning are all useful in science. It’s more important to understand the logic behind these different ways of reasoning than to worry about what they are called.

What is the difference between a theory and a hypothesis?

Scientific theories are broad explanations for a wide range of phenomena, whereas hypotheses are proposed explanations for a fairly narrow set of phenomena. The difference between the two is largely one of breadth. Theories have broader explanatory power than hypotheses do and often integrate and generalize many hypotheses. To be accepted by the scientific community, both theories and hypotheses must be supported by many different lines of evidence. However, both theories and hypotheses may be modified or overturned if warranted by new evidence and perspectives.

What is a null hypothesis?

A null hypothesis is usually a statement asserting that there is no difference or no association between variables. The null hypothesis is a tool that makes it possible to use certain statistical tests to figure out if another hypothesis of interest is likely to be accurate or not. For example, if you were testing the idea that sugar makes kids hyperactive, your null hypothesis might be that there is no difference in the amount of time that kids previously given a sugary drink and kids previously given a sugar-substitute drink are able to sit still. After making your observations, you would then perform a statistical test to determine whether or not there is a significant difference between the two groups of kids in time spent sitting still.

What is Ockhams's razor?

Ockham’s razor is an idea with a long philosophical history. Today, the term is frequently used to refer to the principle of parsimony — that, when two explanations fit the observations equally well, a simpler explanation should be preferred over a more convoluted and complex explanation. Stated another way, Ockham’s razor suggests that, all else being equal, a straightforward explanation should be preferred over an explanation requiring more assumptions and sub-hypotheses. Visit  Competing ideas: Other considerations  to read more about parsimony.

What does science have to say about ghosts, ESP, and astrology?

Rigorous and well controlled scientific investigations 1  have examined these topics and have found  no  evidence supporting their usual interpretations as natural phenomena (i.e., ghosts as apparitions of the dead, ESP as the ability to read minds, and astrology as the influence of celestial bodies on human personalities and affairs) — although, of course, different people interpret these topics in different ways. Science can investigate such phenomena and explanations only if they are thought to be part of the natural world. To learn more about the differences between science and astrology, visit  Astrology: Is it scientific?  To learn more about the natural world and the sorts of questions and phenomena that science can investigate, visit  What’s  natural ?  To learn more about how science approaches the topic of ESP, visit  ESP: What can science say?

Has science had any negative effects on people or the world in general?

Knowledge generated by science has had many effects that most would classify as positive (e.g., allowing humans to treat disease or communicate instantly with people half way around the world); it also has had some effects that are often considered negative (e.g., allowing humans to build nuclear weapons or pollute the environment with industrial processes). However, it’s important to remember that the process of science and scientific knowledge are distinct from the uses to which people put that knowledge. For example, through the process of science, we have learned a lot about deadly pathogens. That knowledge might be used to develop new medications for protecting people from those pathogens (which most would consider a positive outcome), or it might be used to build biological weapons (which many would consider a negative outcome). And sometimes, the same application of scientific knowledge can have effects that would be considered both positive and negative. For example, research in the first half of the 20th century allowed chemists to create pesticides and synthetic fertilizers. Supporters argue that the spread of these technologies prevented widespread famine. However, others argue that these technologies did more harm than good to global food security. Scientific knowledge itself is neither good nor bad; however, people can choose to use that knowledge in ways that have either positive or negative effects. Furthermore, different people may make different judgments about whether the overall impact of a particular piece of scientific knowledge is positive or negative. To learn more about the applications of scientific knowledge, visit  What has science done for you lately?

1 For examples, see:

• Milton, J., and R. Wiseman. 1999. Does psi exist? Lack of replication of an anomalous process of information transfer.  Psychological Bulletin  125:387-391.
• Carlson, S. 1985. A double-blind test of astrology.  Nature  318:419-425.
• Arzy, S., M. Seeck, S. Ortigue, L. Spinelli, and O. Blanke. 2006. Induction of an illusory shadow person.  Nature  443:287.
• Gassmann, G., and D. Glindemann. 1993. Phosphane (PH 3 ) in the biosphere.  Angewandte Chemie International Edition in English  32:761-763.

Subscribe to our newsletter

• Understanding Science 101
• The science flowchart
• Science stories
• Grade-level teaching guides
• Teaching resource database
• Journaling tool
• Misconceptions

• August 2, 2023

New Research Tool Works When Control Groups Won’t

Where randomized experiments aren’t possible, researchers have a new statistical tool, based on the research of kathleen li.

Whether they’re studying vaccine adoption rates or consumer preferences, randomized experiments are the gold standard in the world of research.

In such experiments, researchers split study participants into groups by chance. One group undergoes an intervention. The other — the control group — does not. Then, researchers can say with confidence whether a certain intervention made an impact.

In the real world, though, randomized experiments are not always possible, says Texas McCombs Assistant Professor of Marketing Kathleen Li. “In many situations, you simply can’t, because you can’t convince companies to do it, or maybe it’s against the law. It’s still important, however, to know an intervention’s effect.”

In new research, with Venkatesh Shankar of Texas A&M University, Li creates a statistical tool for such situations. Called two-step synthetic control, it can help researchers get meaningful results when randomized trials are not feasible.

“Our framework allows managers and policymakers to estimate effects they previously weren’t able to estimate accurately,” Li says. “They get a more precise estimate that can help them make more informed decisions.”

A More Flexible Approach

Li’s tool adapts an existing research workaround, known as the synthetic control method. As the name implies, it creates synthetic control groups from the data, in place of real ones. The groups are weighted statistically and compared with a group undergoing an intervention.

But the synthetic control method doesn’t perfectly apply to all situations, especially ones in which the intervention group is very different from its control groups. In these scenarios, the method could lead to less accurate results.

One problem, says Li, is that the method is somewhat inflexible. The control groups that make up its weighted combination must add up to 100%. For example, researchers could decide that one group accounts for 20% and another 80%.

A more flexible method might lead to more accurate results, the researchers say, and they’ve devised one. Their two-step synthetic control approach goes through two stages:

· First, it determines whether the traditional synthetic control method applies to a given case.

· If it does not, the second step uses a more flexible framework that allows weighted controls to differ from 100% or to shift the control group up and down.

“This approach balances the tradeoff,” Li says. “We first want to get an accurate effect, but at the same time, we also want to be as precise as possible in order to provide the most informative information to key decision makers.”

Accuracy, she adds, is about how close a measurement is to the true value. Precision, on the other hand, refers to the tightness of the numerical band the measurement is thought to fall in.

Measuring Tampon Sales

To test their new method on a real-world situation, Li and Shankar looked at sales of tampons: how they responded in 2016, when New York repealed a sales tax on them.

Sales taxes on tampons have been a contentious issue worldwide, and many countries — including Australia, Germany, and India — have abolished or reduced them. Proponents of repeal argue that feminine hygiene products are basic necessities and should not be taxed.

But by 2019, only 13 states in the U.S. had repealed the tax, with opponents arguing that repeal would decrease state revenues. One key point of contention for policymakers has been how repeal would affect tampon sales.

To find out, Li and Shankar gathered 52 weeks of sales data before New York’s repeal and 17 weeks after. Their control group was 35 states that did not repeal the tax.

In their first step, the researchers applied the traditional synthetic control method to the data. They found the traditional method probably overestimated the actual increase in weekly sales, showing a 2.5% rise in New York.

In the second step, the researchers applied a more flexible method. It estimated that New York’s repeal caused a more modest increase in weekly tampon sales, of only 2.08%. That estimate probably is more accurate, as the more flexible method matches the actual sales figures before the intervention better.

Any market or public policy researcher can use the new method, Li says. In fact, she and Shankar will be making it available online for all to use.

But she offers one caution: More flexible methods tend to be less precise, with wider bands of uncertainty.

“That’s the trade-off,” Li says. “You want to be able to use the method that’s just as flexible as you need it. It’s why having this tool to see how flexible you can go and still be precise is critical.”

“A Two-Step Synthetic Control Approach for Estimating Causal Effects of Marketing Events” is forthcoming, online in Management Science.

Story by Deborah Lynn Blumberg

Related Posts

Global Investment Expert Edward Ho Joins McCombs Center for Global Business Advisory Board

In China, Property Rights Take Wrong Turn

When to Let Amazon Sell for You

• Open access
• Published: 09 August 2024

Evaluation of the BOPPPS model on otolaryngologic education for five-year undergraduates

• Dachuan Fan 1   na1 ,
• Chao Wang 2   na1 ,
• Xiumei Qin 1   na1 ,
• Shiyu Qiu 1   na1 ,
• Yatang Wang 1 &
• Jinxiao Hou   ORCID: orcid.org/0000-0001-6736-1714 3  

BMC Medical Education volume  24 , Article number:  860 ( 2024 ) Cite this article

Metrics details

This study aimed to assess the effectiveness of the BOPPPS model (bridge-in, learning objective, pre-test, participatory learning, post-test, and summary) in otolaryngology education for five-year undergraduate students.

A non-randomized controlled trial was conducted with 167 five-year undergraduate students from Anhui Medical University, who were allocated to an experimental group and a control group. The experimental group received instruction using the BOPPPS model, while the control group underwent traditional teaching methods. The evaluation of the teaching effectiveness was performed through an anonymous questionnaire based on the course evaluation questionnaire. Students’ perspectives and self-evaluations were quantified using a five-point Likert scale. Furthermore, students’ comprehension of the course content was measured through a comprehensive final examination at the end of the semester.

Students in the experimental group reported significantly higher scores in various competencies compared to the control group: planning work (4.27 ± 0.676 vs. 4.03 ± 0.581, P  < 0.05), problem-solving skills (4.31 ± 0.624 vs. 4.03 ± 0.559, P  < 0.01), teamwork abilities (4.19 ± 0.704 vs. 3.87 ± 0.758, P  < 0.05), and analytical skills (4.31 ± 0.719 vs. 4.05 ± 0.622, P  < 0.05). They also reported higher motivation for learning (4.48 ± 0.618 vs. 4.09 ± 0.582, P  < 0.01). Additionally, students in the experimental group felt more confident tackling unfamiliar problems (4.21 ± 0.743 vs. 3.95 ± 0.636, P  < 0.05), had a clearer understanding of teachers’ expectations (4.31 ± 0.552 vs. 4.08 ± 0.555, P  < 0.05), and perceived more effort from teachers to understand their difficulties (4.42 ± 0.577 vs. 4.13 ± 0.59, P  < 0.01). They emphasized comprehension over memorization (3.65 ± 1.176 vs. 3.18 ± 1.065, P  < 0.05) and received more helpful feedback (4.40 ± 0.574 vs. 4.08 ± 0.585, P  < 0.01). Lecturers were rated better at explaining concepts (4.42 ± 0.539 vs. 4.08 ± 0.619, P  < 0.01) and making subjects interesting (4.50 ± 0.546 vs. 4.08 ± 0.632, P  < 0.01). Overall, the experimental group expressed higher course satisfaction (4.56 ± 0.542 vs. 4.34 ± 0.641, P  < 0.05). In terms of examination performance, the experimental group scored higher on the final examination (87.7 ± 6.7 vs. 84.0 ± 7.7, P  < 0.01) and in noun-interpretation (27.0 ± 1.6 vs. 26.1 ± 2.4, P  < 0.01).

The BOPPPS model emerged as an effective and innovative teaching method, particularly in enhancing students’ competencies in otolaryngology education. Based on the findings of this study, educators and institutions were encouraged to consider incorporating the BOPPPS model into their curricula to enhance the learning experiences and outcomes of students.

Peer Review reports

Introduction

Otolaryngology is a distinctive clinical discipline characterized by its unique professional attributes that focus on the diagnosis and treatment of disorders affecting the ears, nose, throat, head and neck regions. Otolaryngologists frequently encounter various clinical manifestations associated with systemic diseases, requiring advanced clinical reasoning and complex problem-solving abilities [ 1 ]. Undergraduate otolaryngology education encompasses a wide range of knowledge areas and emphasizes the integration of theory and practice to train a highly qualified cadre of doctors [ 2 ]. The challenge of this specialized education lies in providing effective teaching modalities that ensure competency in the diagnosis and management of otolaryngologic disorders within a standardized framework [ 2 , 3 , 4 ].

In medical curricula, the traditional teaching prevalent in the current evidence relies on lecture-based instruction and emphasizes the delivery of syllabi and concepts [ 4 ]. However, the term “traditional” is not clearly defined and may vary depending on the individual teacher. In this format, students first receive reading materials, including textbooks and the course syllabus, and then passively absorb knowledge through face-to-face classroom sessions, while teachers impart theoretical knowledge, answer questions, and repeat any knowledge points that students had not been fully understood in the class, via PowerPoint slides and handouts [ 5 , 6 ]. This model often results in unsatisfactory learning outcomes as medical students acquire knowledge passively from instructors with little interaction, resulting in decreased motivation to study and innovate. Moreover, otolaryngology experience and training in medical schools have been gradually declining at undergraduate medical education worldwide [ 7 , 8 ]. As a consequence, undergraduate students and primary care practitioners often exhibit low competency in managing ear, nose, and throat problems, such as difficulty in accurately diagnosing common conditions, limited proficiency in performing basic examinations, and insufficient knowledge of appropriate treatment protocols [ 4 , 9 , 10 , 11 , 12 ]. Thus, it is crucial to restructure the current educational approach from conventional didactic learning, aiming to enhance students’ competencies by incorporating focused teaching and skills training [ 3 ].

The BOPPPS (bridge-in, learning objective, pre-test, participatory learning, post-test, and summary) model was a six-stage framework which was originally developed by the Center for Teaching and Academic Development, University of British Columbia, Canada [ 13 ]. It offered a comprehensive and coherent teaching process and theoretical foundation to achieve learning objectives [ 5 ]. Moreover, it clearly organized the teaching process and creates a closed-loop teaching unit with an integrated system that emphasizes the effectiveness of learning outcomes and the diversity of teaching methods [ 5 ]. Several studies have demonstrated that the BOPPPS model is more effective than traditional instruction in enhancing students’ skills and knowledge, as well as improving their self-learning ability, academic performance, and learning satisfaction across various disciplines, such as ophthalmology, thoracic surgery and gynecology [ 5 , 14 , 15 , 16 , 17 , 18 ]. However, the application of the BOPPPS model in otolaryngology education has not been fully explored.

In fact, we first applied the single BOPPPS teaching to integration cases in the spring of 2021 for the students of Class 2017, and then in 2022 for Class 2018. Unlike traditional teaching, the BOPPPS model encouraged active engagement from students through participatory learning activities, fostering deeper understanding, critical thinking, and application of knowledge. Moreover, while traditional teaching may focus primarily on content delivery, the BOPPPS model emphasized the integration of theoretical concepts with practical clinical scenarios, thereby promoting a more holistic approach to learning [ 6 , 18 ]. In this study, we conducted a preliminary evaluation of the effectiveness of the BOPPPS model for otolaryngology education among five-year undergraduates.

Participants and recruitment

This study was a non-randomized controlled trial conducted at Anhui Medical University between April 1, 2023, and May 30, 2023. We recruited 167 students majoring in clinical medicine from Anhui Medical University who were undergraduate students studying otolaryngology in their eighth semester. Informed consent was obtained from each participant prior to enrolment in the study. Each participant voluntarily agreed to take part in this study. The students were from almost all regions of China and approximately half of them were residents of Anhui province. They all received systematic pre-college education under the same guideline and using the same textbooks after passing the requirements of the entrance examination. The students were divided into 4 sections to be taught separately. Each section was usually taught by one teacher throughout the entire Otolaryngology course. All teachers had at least 10 years’ experience of teaching and met the standard requirements of teaching after group rehearsal of the course contents. We assigned them to two groups: an experimental group that used the BOPPPS model and a control group that used the traditional instructional approach.

Study design and setting

The study conducted over two months, focusing on the effectiveness of the BOPPPS model in teaching otolaryngology. The experimental group applied the BOPPPS model, while the control group received traditional lecture-based instruction. Both groups covered a total of 49 topics related to otolaryngology, with chronic sinusitis being one example. The course comprised 27 sessions with 45 min per session. The study included 169 five-year undergraduate students from Anhui Medical University, with 49 students in the experimental group and 118 students in the control group. Students were allocated to these groups based on their class schedules and availability. The same curriculum was used as the teaching content for both groups of students. The teaching processes were completed within the same duration for the experimental group and the control group. The control group received mainly traditional teaching [ 19 ]. In the traditional lecture-based format, teachers delivered theoretical knowledge through PowerPoint slides, handouts, and lectures. Students passively received information and took notes. The traditional teaching sessions involved the following steps: Reading Material : Students first received the reading material, including textbooks and the course syllabus. Classroom Instruction : Teachers used overhead projectors and PowerPoint slides to deliver the content face-to-face, with minimal student interaction. Teaching Materials : Students had access to teaching materials and reference book. Question and Answer : Teachers answered students’ questions and repeated any points that were not fully understood.

The experimental group applied the BOPPPS model for teaching, using the topic on chronic sinusitis as an example. The BOPPPS model is composed of six parts [ 6 , 20 ]: Bridge-in : Before class, the teacher introduces two problems of chronic sinusitis from online searching platforms ( https://pubmed.ncbi.nlm.nih.gov ) to motivate students’ interest in learning clinical diseases characterized by “rhinorrhea” and “headache”. The teacher also provides a clinical case with a framework for understanding the course’s main content by asking students to recall the anatomy and physiology of the paranasal sinuses and the common symptoms of chronic sinusitis. Objective : According to the course syllabus of Anhui Medical University, the teacher clearly states the diagnosis and treatment of chronic sinusitis as the focus of the course. Pre-assessment : The teacher administers a quiz or a poll to assess the students’ prior knowledge and understanding of chronic sinusitis. The teacher also asks students to share their questions or difficulties about the topic. Participatory learning : The teacher divides the students into small groups and assigns each group a clinical case related to chronic sinusitis. The students are instructed to discuss the case in their groups and answer questions based on the pre-assessment such as: what are the possible causes and risk factors of chronic sinusitis? what are the diagnostic tests and criteria for chronic sinusitis? what are the treatment options and goals for chronic sinusitis? how would you educate the patient about prevention and self-care? The teacher facilitates the discussion by providing feedback, guidance and additional information as needed. Post-assessment : The teacher conducts another quiz or a poll to evaluate the students’ learning outcomes and progress after the participatory learning. The teacher also urges students to reflect on their learning experience and identify their strengths and weaknesses. The teacher adjusts the subsequent content to improve teaching efficiency based on the post-assessment. Summary : The teacher summarizes the main points and key concepts of chronic sinusitis. The teacher also reviews the learning objectives and emphasizes the clinical implications and applications of chronic sinusitis. The teacher encourages students to expand their learning beyond the course and seek further learning resources if interested, such as by consulting expert consensus and clinical guidelines (e.g., European Position Paper on Rhinosinusitis and Nasal Polyps, 2020). To ensure clarity and concision, the teaching flowchart is depicted in Fig.  1 .

Flowchart of BOPPPS and traditional instructional teaching using chronic sinusitis as an example. Bridge-in : following the problem introduction or a clinical case, delve into the interest motivation by exploring the symptoms of chronic sinusitis, such as “rhinorrhea” and “headache,” commonly searched online, sparking our curiosity about this condition. Objective : diagnosis and treatment of chronic sinusitis based on the course syllabus. Pre-assessment : a quiz/poll; sharing any questions or areas of difficulty regarding the topic. Participatory learning : students are divided into small groups to analyze clinical cases of chronic sinusitis, discussing causes, diagnostics, treatments, and patient education. Post-assessment : quiz/poll, student reflection on learning experience, and subsequent content adjustment for improved teaching efficiency. Summary : the teacher summarizes key points of chronic sinusitis, reviews learning objectives, underscores clinical implications, and encourages students to explore additional resources for further learning

Assessment of teaching outcomes

To evaluate the efficacy of the BOPPPS instructional model, we administered an anonymous questionnaire to the students. The questionnaire was adapted from the course evaluation questionnaire [ 21 ]. The students from both groups filled out the questionnaire after completing the course. We quantified the students’ perspectives and self-evaluations using a five-point Likert-type scale ranging from a score of one for strong disagreement to a score of five for strong agreement.

We also tested the students’ understanding of the course content by administering a comprehensive final examination at the end of the semester. The written examination (with a total score of 100 points) assessed the theoretical knowledge of Otolaryngology. The examination questions consisted of three parts: medical-terms interpretation (28 points), single-choice questions (42 points) and short-answer questions (30 points). They were randomly selected from the examination question bank, which encompassed the students’ skills in Otolaryngology.

Statistical analysis

Statistical analyses were conducted using SPSS 26.0 (SPSS, Inc., Chicago, IL). The quantitative data were presented as means ± standard deviations and subjected to analysis using the t-test. Meanwhile, categorical data were analysed by the chi-square test. P  < 0.05 indicated that the difference was statistically significant.

Demographic characteristics of the participants

Table  1 depicted the main demographic features of the two groups of undergraduate students. The experimental group consisted of 49 students (30 males, 19 females) with a mean age of 21.29 years. The control group comprised 118 students (87 males, 31 female) with a mean age of 21.70 years. The two groups were comparable in their general characteristics, such as sex, age, and origin of the students ( P  > 0.05). No significant differences were observed between the two groups regarding sex, age, and family background ( P  > 0.05).

Comparison of student perspectives

In Table  2 , we compared students’ perspectives in the control group to those of the experimental group. Students in both groups considered the otolaryngology course to be too heavy (3.56 ± 1.050 vs. 3.39 ± 0.894), overly theoretical and abstract (3.75 ± 1.139 vs. 3.36 ± 1.00) and needed a good memory (4.25 ± 0.700 vs. 4.13 ± 0.461). There was no significant difference in learning pressure (3.40 ± 1.125 vs. 3.20 ± 0.962, P  > 0.05), course comprehension (3.42 ± 1.164 vs. 3.30 ± 1.013, P  > 0.05), and time spent (3.73 ± 1.086 vs. 3.53 ± 0.910, P  > 0.05) between the two groups. More students in the experimental group agreed that BOPPPS model significantly enhanced their ability to plan their own work (4.27 ± 0.676 vs. 4.03 ± 0.581, P  < 0.05), developed their problem-solving skills (4.31 ± 0.624 vs. 4.03 ± 0.559, P  < 0.01), helped them work as a team member (4.19 ± 0.704 vs.3.87 ± 0.758, P  < 0.05), sharpen their analytical skills (4.31 ± 0.719 vs. 4.05 ± 0.622, P  < 0.05), and improved their motivation for learning (4.48 ± 0.618 vs. 4.09 ± 0.582, P  < 0.01) than the control group. Through the experimental group course, students felt more confident about tackling unfamiliar problems than through the control group course (4.21 ± 0.743 vs. 3.95 ± 0.636, P  < 0.05). Compared to those in the control group, students in the experimental group demonstrated a significantly clearer understanding of the teaching staff’s expectations from the start (4.31 ± 0.552 vs. 4.08 ± 0.555, P  < 0.05). Furthermore, the experimental group perceived a greater effort from the staff to understand their difficulties (4.42 ± 0.577 vs. 4.13 ± 0.59, P  < 0.01), a stronger emphasis on comprehension rather than memorization (3.65 ± 1.176 vs. 3.18 ± 1.065, P  < 0.05), and received more helpful feedback from the teaching staff (4.40 ± 0.574 vs.4.08 ± 0.585, P  < 0.01). Additionally, students in the experimental group found the lecturers to be significantly better at explaining concepts (4.42 ± 0.539 vs.4.08 ± 0.619, P  < 0.01) and perceived a higher level of effort in making the subjects interesting (4.50 ± 0.546 vs. 4.08 ± 0.632, P  < 0.01) than those in the control group. Overall, the experimental group was significantly more satisfied with the course than the control group (4.56 ± 0.542 vs. 4.34 ± 0.641, P  < 0.05).

Evaluation of academic performance

The experimental group achieved significantly higher final examination scores compared to the control group (87.7 ± 6.7 vs. 84.0 ± 7.7), and the difference was statistically significant ( P  = 0.004). The experimental group also obtained significantly higher scores in noun-interpretation than the control group (27.0 ± 1.6 vs. 26.1 ± 2.4, P  = 0.005). However, there was no statistically significant difference in single-choice scores between the two groups (31.8 ± 6.1 vs. 30.0 ± 4.9, P  = 0.076), as well as in short-answer scores (28.2 ± 3.3 vs. 28.0 ± 3.4, P  = 0.690) (Fig.  2 ).

Comparison of examination scores between experimental and control groups

The evolution of medical education has been driven by advancements in medical knowledge and pedagogy, as well as the need to address the complexities of chronic disease management and adapt to demographic, economic, and organizational changes in the healthcare system [ 22 , 23 ]. In the past few decades, medical education has shifted from a disease-oriented approach to a problem-based approach, and finally to a competency-based approach [ 24 , 25 ]. This transformation signified a crucial shift towards a more holistic and integrated model of otolaryngologic medical education [ 26 , 27 , 28 ]. It recognized the dynamic and complex nature of the field and the changing healthcare environment, where the demands on future otolaryngologists extended far beyond mere anatomical knowledge.

This study was the first application of the BOPPPS model in otolaryngologic education for the fourth year undergraduates in terms of students’ perspectives and examination scores. The findings revealed several positive outcomes. Firstly, the BOPPPS model significantly developed students’ problem-solving skills, improved teamwork, sharpened analytical skills, and increased students’ motivation for learning by engaging students in challenging clinical scenarios and encouraging them to analyse complex situations. Those skills are crucial and essential to make quick and accurate decisions for optimal patient treatment. Several studies demonstrated that the BOPPPS model enhanced clinical practice abilities and increased student satisfaction, and that it better inspired enthusiasm and enhanced comprehensive abilities in clinical teaching practice, which was consistent with our findings [ 6 , 18 ]. Secondly, the model promoted effective communication and cooperation by engaging students in participatory activities and group discussions. This approach enhanced critical thinking abilities during problem-solving exercises, enabling students to assess medical information, interpret diagnostic findings, and explore diverse treatment alternatives. Thirdly, it cultivated a supportive and engaging learning environment, leading to increased confidence and a deeper understanding of the subject matter for students. By prioritizing comprehension over memorization and providing personalized guidance, the model optimized students’ learning strategies. These results were confirmed by a recent meta-analysis, which highlights the significant impact of the BOPPPS model across multiple disciplines in Chinese medical education [ 5 ]. The most crucial outcome was the significantly higher final examination scores achieved by the experimental group. These scores were not only important for evaluating the students’ academic achievement, but also for measuring educational quality in the field [ 6 , 18 ]. The application of the BOPPPS model with or without innovative teaching in medical education demonstrated its effectiveness, fulfilling the requirements of competency-based teaching, equipping future otolaryngologists with the necessary skills to make quick and accurate decisions in patient treatment, and meeting the needs of modern medical education [ 14 , 16 , 29 , 30 ].

Competency-based education was an outcomes-centered approach that focused on mastering specific skills and knowledge required in a field of study, rather than memorizing facts and information [ 31 , 32 , 33 ]. In our study, the BOPPPS model, a six-stage framework, was used to design and deliver effective and engaging instruction for otolaryngology education. Our results demonstrated significant improvements in analytical skills, problem-solving abilities, and motivation, thereby supporting the effectiveness of the BOPPPS model in achieving competency-based educational outcomes. Each stage has a specific purpose and function in the teaching process [ 20 , 34 ].

Bridge-in: This stage aims to capture the students’ attention and interest by linking their prior knowledge and experience to the new topic or concept. This stage can help students activate their existing competencies and connect them to the new learning objectives, as well as motivate them to learn more.

Objective: This stage defines the clear and measurable learning outcomes that the students are expected to achieve by the end of the lesson. This stage can help students concentrate on mastering specific competencies required in their field of study, as well as provide them with clear criteria and expectations for assessment.

Pre-assessment: This stage evaluates the students’ current level of knowledge and skills related to the topic, as well as their learning needs and preferences. This stage can help teachers identify the students’ strengths and weaknesses, as well as tailor their instruction accordingly. This stage can also help students self-assess their competencies and set their own learning goals.

Participatory learning: This stage engages the students in active and collaborative learning activities that help them acquire and apply the new knowledge and skills. This stage can help students develop and enhance their competencies through problem-solving exercises, case studies, simulations, role-plays, and other interactive methods. This stage can also help students practice their critical thinking, communication, teamwork, and other soft skills that are essential for their field of study.

Post-assessment: This stage evaluates the students’ learning outcomes and progress by measuring their achievement of the learning objectives. This stage can help teachers provide feedback and guidance to the students on their performance and improvement. This stage can also help students demonstrate their competencies and reflect on their learning process.

Summary: This stage reviews and reinforces the main points and key concepts of the lesson, as well as provides feedback and guidance for further learning. This stage can help students consolidate their competencies and transfer them to other contexts, as well as identify their areas for further development.

Implications for practice

As a result, the BOPPPS model could provide a structured and systematic way to assess and enhance students’ competencies, as well as encourage active participation and collaboration among students [ 6 , 18 , 35 ]. By using the BOPPPS model, teachers could create a meaningful and memorable learning experience for their students, preparing them for real-world challenges in their field of study. By focusing on practical application, personalized feedback, and collaborative learning, the model fostered a transformative learning experience that empowered students to become competent and well-rounded professionals in their chosen field [ 5 , 17 ]. The model’s application provided a comprehensive and in-depth approach to develop students’ abilities, ensuring they were well-prepared for their future careers.

The results of this study suggested that educators and institutions should explore integrating the BOPPPS model into their curricula to optimize the learning experience for aspiring otolaryngologists. The findings also supported the wider adoption of competency-based pedagogy, emphasizing the potential of BOPPPS to enhance students’ perceptions, academic performance, and overall learning experiences in otolaryngology education and beyond, aligning with other studies [ 5 , 17 , 28 , 35 ]. The findings underscored the significance of learner-centered and practice-oriented approaches in medical education, providing useful insights for curriculum design and instructional strategies [ 35 ]. As educators and institutions seeked to optimize learning outcomes and prepared competent healthcare professionals, the BOPPPS model served as a promising and effective tool for shaping the future of otolaryngology medical education [ 6 , 18 ].

All students from the five-year undergraduate program acknowledged the course’s heavy workload and its theoretical and abstract nature. They also recognized the importance of having a good memory for effectively navigating the course material. There were no significant differences between the two groups in terms of learning pressure, course comprehension, and the amount of time spent on the course. These findings indicated that while the BOPPPS model positively influenced some aspects of students’ learning experiences and academic performance, it did not drastically alter their overall perceptions of the course’s demands and challenges. The course’s heavy workload and abstract content may remain inherent challenges of otolaryngology education, regardless of the teaching methodology employed. To further enhance the learning experience, future studies could investigate ways to reduce the perceived heavy workload and abstract nature of the course while continuing to utilize the strengths of the BOPPPS model [ 30 , 36 , 37 ]. Implementing additional interactive and hands-on learning opportunities, incorporating practical case studies, and providing tailored support for memory retention could be potential strategies to adopt. Moving forward, educators and institutions can build upon the strengths of the BOPPPS model while exploring additional strategies to optimize students’ learning experiences in otolaryngology.

Limitations and future research suggestions

While this study offered valuable insights, it was important to recognize certain limitations in its design and scope. Firstly, the research focused on a specific group of fourth year undergraduates, potentially limiting the generalizability of the findings to students at different stages of their medical education. Expanding the study to include a more diverse cohort from various educational levels would provide a more comprehensive understanding of the model’s efficacy. Additionally, the study’s single-institution setting and relatively short duration might restrict the applicability of the results to other medical schools. Conducting future research involving multiple institutional settings, larger sample sizes and a longitudinal investigation extending over multiple years would enhance the external validity and enable a broader assessment of the BOPPPS model’s impact. In this study, the survey was designed to capture general aspects of the learning experience applicable to any teaching method, though we recognize the need for refined questions to better address the nuances of each methodology. While students from different classes had their teaching sessions conducted simultaneously to minimize information sharing, the possibility cannot be entirely eliminated. Furthermore, a crossover design was not feasible due to logistical constraints and the structured curriculum, but future research should incorporate this approach for a more direct comparison and to capture the long-term effects of the BOPPPS model on students’ academic performance and perceptions.

In this study, BOPPPS model increased student satisfaction and improved learning outcomes in otolaryngologic medical education by fostering active learning, problem-solving skills, teamwork, analytical thinking, and motivation. This comprehensive approach showed great promise in effectively cultivating future otolaryngologists. Educators and medical institutions should consider adopting similar innovative teaching methodologies to enhance the learning experiences and academic achievements of medical students.

Data availability

Data is provided within the manuscript or supplementary information files.

Steven RA, McAleer S, Jones SE, Lloyd SK, Spielmann PM, Eynon-Lewis N, Mires GJ. Defining performance levels in undergraduate otolaryngology education. J Laryngol Otol. 2022;136(1):17–23.

Article   Google Scholar  

Patel B, Saeed SR, Smith S. The provision of ENT teaching in the undergraduate medical curriculum: a review and recommendations. J Laryngol Otol. 2021;135(7):610–5.

Mayer AW, Smith KA, Carrie S. A survey of ENT undergraduate teaching in the UK. J Laryngol Otol. 2020;134(6):553–7.

Fung K. Otolaryngology–head and neck surgery in undergraduate medical education: advances and innovations. Laryngoscope. 2015;125(Suppl 2):S1–14.

Google Scholar  

Ma X, Zeng D, Wang J, Xu K, Li L. Effectiveness of bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary teaching strategy in Chinese medical education: a systematic review and meta-analysis. Front Med (Lausanne). 2022;9:975229.

Hu K, Ma RJ, Ma C, Zheng QK, Sun ZG. Comparison of the BOPPPS model and traditional instructional approaches in thoracic surgery education. BMC Med Educ. 2022;22(1):447.

Al-Hazimi A, Zaini R, Al-Hyiani A, Hassan N, Gunaid A, Ponnamperuma G, Karunathilake I, Roff S, McAleer S, Davis M. Educational environment in traditional and innovative medical schools: a study in four undergraduate medical schools. Educ Health (Abingdon). 2004;17(2):192–203.

Nandi PL, Chan JN, Chan CP, Chan P, Chan LP. Undergraduate medical education: comparison of problem-based learning and conventional teaching. Hong Kong Med J. 2000;6(3):301–6.

Oyewumi M, Isaac K, Schreiber M, Campisi P. Undergraduate otolaryngology education at the University of Toronto: a review using a curriculum mapping system. J Otolaryngol Head Neck Surg. 2012;41(1):71–5.

Ferguson GR, Bacila IA, Swamy M. Does current provision of undergraduate education prepare UK medical students in ENT? A systematic literature review. BMJ Open. 2016;6(4):e010054.

Mace AD, Narula AA. Survey of current undergraduate otolaryngology training in the United Kingdom. J Laryngol Otol. 2004;118(3):217–20.

Campisi P, Asaria J, Brown D. Undergraduate otolaryngology education in Canadian medical schools. Laryngoscope. 2008;118(11):1941–50.

Pattison P, Russell D. Instructional skills workshop handbook. Vancouver, Canada: UBC Centre for Teaching and Academic Growth; 2006.

Chen L, Tang XJ, Chen XK, Ke N, Liu Q. Effect of the BOPPPS model combined with case-based learning versus lecture-based learning on ophthalmology education for five-year paediatric undergraduates in Southwest China. BMC Med Educ. 2022;22(1):437.

Li Y, Li X, Liu Y, Li Y. Application effect of BOPPPS teaching model on fundamentals of nursing education: a meta-analysis of randomized controlled studies. Front Med (Lausanne). 2024;11:1319711.

Li Z, Cai X, Zhou K, Qin J, Zhang J, Yang Q, Yan F. Effects of BOPPPS combined with TBL in surgical nursing for nursing undergraduates: a mixed-method study. BMC Nurs. 2023;22(1):133.

Pan Y. A review on the application and development of the BOPPPS Model in Chinese Colleges and universities. Int J Educational Curriculum Manage Res. 2023;4(2):1–8.

Xu Z, Che X, Yang X, Wang X. Application of the hybrid BOPPPS teaching model in clinical internships in gynecology. BMC Med Educ. 2023;23(1):465.

Mennin S, Martinez-Burrola N. The cost of problem‐based vs traditional medical education. Med Educ. 1986;20(3):187–94.

Zhang L. Teaching design and practice of intensive reading course based on BOPPPS. J Lang Teach Res. 2020;11(3):503–8.

Broomfield D, Bligh J. An evaluation of the ‘short form’ course experience questionnaire with medical students. Med Educ. 1998;32(4):367–9.

Lucey CR. Medical Education: part of the Problem and Part of the solution. JAMA Intern Med. 2013;173(17):1639–43.

Norman G. Medical education: past, present and future. Perspect Med Educ. 2012;1(1):6–14.

McIntosh C, Patel KR, Lekakis G, Wong BJF. Emerging trends in rhinoplasty education: accelerated adoption of digital tools and virtual learning platforms. Curr Opin Otolaryngol Head Neck Surg. 2022;30(4):226–9.

Gantz BJ. Evolution of Otology and Neurotology Education in the United States. Otol Neurotol. 2018;39(4S Suppl 1):S64–8.

Ishman SL, Stewart CM, Senser E, Stewart RW, Stanley J, Stierer KD, Benke JR, Kern DE. Qualitative synthesis and systematic review of otolaryngology in undergraduate medical education. Laryngoscope. 2015;125(12):2695–708.

Comer BT, Gupta N, Mowry SE, Malekzadeh S. Otolaryngology Education in the setting of COVID-19: current and future implications. Otolaryngol Head Neck Surg. 2020;163(1):70–4.

Henri M, Johnson MD, Nepal B. A review of competency-based learning: tools, assessments, and recommendations. J Engin Educ. 2017;106(4):607–38.

Wen H, Xu W, Chen F, Jiang X, Zhang R, Zeng J, Peng L, Chen Y. Application of the BOPPPS-CBL model in electrocardiogram teaching for nursing students: a randomized comparison. BMC Med Educ. 2023;23(1):987.

Ma X, Ma X, Li L, Luo X, Zhang H, Liu Y. Effect of blended learning with BOPPPS model on Chinese student outcomes and perceptions in an introduction course of health services management. Adv Physiol Educ. 2021;45(2):409–17.

Wagner N, Fahim C, Dunn K, Reid D, Sonnadara RR. Otolaryngology residency education: a scoping review on the shift towards competency-based medical education. Clin Otolaryngol. 2017;42(3):564–72.

Frank JR, Snell LS, Cate OT, Holmboe ES, Carraccio C, Swing SR, Harris P, Glasgow NJ, Campbell C, Dath D. Competency-based medical education: theory to practice. Med Teach. 2010;32(8):638–45.

Chen JX, Thorne MC, Galaiya D, Campisi P, Gray ST. Competency-based medical education in the United States: what the otolaryngologist needs to know. Laryngoscope Investig Otolaryngol. 2023;8(4):827–31.

Wu C, He X, Jiang H. Advanced and effective teaching design based on BOPPPS model. Int J Continuing Eng Educ Life Long Learn. 2022;32(5):650–61.

Li P, Lan X, Ren L, Xie X, Xie H, Liu S. Research and practice of the BOPPPS teaching model based on the OBE concept in clinical basic laboratory experiment teaching. BMC Med Educ. 2023;23(1):882.

Jamil Z, Naseem A, Rashwan E, Khalid S. Blended learning: call of the day for medical education in the global South. SOTL South. 2019;3(1):57–76.

Lin GSS, Tan W-W, Tan H-J, Khoo C-W, Afrashtehfar KI. Innovative pedagogical strategies in health professions education: active learning in dental materials science. Int J Environ Res Public Health. 2023;20(3):2041.

Download references

Acknowledgements

Not applicable.

This work was supported by the Natural Science Foundation of Anhui Provincial Education Department (KJ2021A0315).

Author information

Dachuan Fan, Chao Wang, Xiumei Qin and Shiyu Qiu contributed equally to this work.

Authors and Affiliations

Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui Province, China

Dachuan Fan, Xiumei Qin, Shiyu Qiu, Yan Xu & Yatang Wang

Department of Economics and Trade, School of Economics and Management, Hefei University, No. 99 Jinxiu Avenue, Hefei, 230601, Anhui Province, China

Department of Hematology, the Second Affiliated Hospital of Anhui Medical University, NO. 678, Furong Road, Hefei, 230601, Anhui Province, China

Jinxiao Hou

You can also search for this author in PubMed   Google Scholar

Contributions

DC-F designed the study and drafted the manuscript. C-W designed the course evaluation questionnaire. XM-Q, SY-Q, Y-X, and YT-W collected data and assessed examination scores for eligibility. JX-H performed the statistical analysis and supervised the study. All authors critically reviewed and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jinxiao Hou .

Ethics declarations

Ethics approval and consent to participate.

This study was conducted following the guidelines of the Helsinki Declaration, and approved by the local Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (YX2024-034). The Ethics Committee of the Second Affiliated Hospital of Anhui Medical University approved all experimental protocols for this study. Each participant voluntarily took part in this study. Informed consent was obtained from all subjects and/or their legal guardian(s).

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Fan, D., Wang, C., Qin, X. et al. Evaluation of the BOPPPS model on otolaryngologic education for five-year undergraduates. BMC Med Educ 24 , 860 (2024). https://doi.org/10.1186/s12909-024-05868-3

Download citation

Received : 11 February 2024

Accepted : 06 August 2024

Published : 09 August 2024

DOI : https://doi.org/10.1186/s12909-024-05868-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• BOPPPS model
• Otolaryngology education
• Five-year undergraduates
• Competency-based education

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Model of Accelerated Aging in CB6F2 Mice Induced by Ionizing Radiation

• Published: 10 August 2024

Cite this article

• E. A. Yakunchikova 1 , 2 ,
• M. N. Yurova 1 ,
• I. S. Drachyov 2 ,
• E. A. Radetskaya 1 ,
• K. V. Altukhov 1 ,
• A. L. Semenov 1 ,
• A. V. Panchenko 1 ,
• M. L. Tyndyk 1 ,
• V. N. Bykov 1 &
• E. I. Fedoros 1  

A model for accelerated aging in mice was developed: CB6F2 mice aged 39-45 days were exposed to fractionated 4-fold relatively uniform γ-radiation ( 137 Cs, 0.98 Gy/min) at a total dose of 6.8 Gy. Radiation exposure led to delayed active growth, leukopenia, and lymphopenia for over 1 year during the post-radiation period. The death of irradiated males and females occurred significantly earlier than in control group animals. Median lifespans in the experimental group were 35-38% lower than in the control group ( p <0.001). Ionizing radiation exposure led to the early development of hair depigmentation, cachexia, and the development of aging-associated diseases. In irradiated mice, oncological pathology constituted 30-35% in the mortality structure, which is twice as often as in the control group. The developed model can be used to study the pathogenesis of accelerated aging under radiation exposure and the search for means of its prevention and treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Kutkov VA. Values in radiation protection and safety. Apparatura Novosti Radiats. Izmer. 2007;(3):2-25. Russian.

Bychkovskaya IB, Gilyano NYa, Fedortseva RF, Bedcher FS. About the peculiar form of radiation induced genome instability. Radiats. Biol. Radioekol. 2005;45(6):688-693. Russian.

Igrunkova AV, Valieva YaM, Kalinichenko AM, Kurkov AV, Popova KYu, Shestakov DYu, Zaborova VA. Cellular senescence: molecular biology and morphology. Mol. Med. 2022;20(4):16-21. Russian. https://doi.org/10.29296/24999490-2022-04-03

Anisimov VN. Biology of aging and cancer. Cancer Control. 2007;14(1):23-31. https://doi.org/10.1177/107327480701400104

Article   PubMed   Google Scholar  

Bykov VN, Grebenyuk AN, Ushakov IB. Radiation-protective agent and anti-aging agents: random and predictable matches. Usp. Gerontol. 2020;33(4):646-656. Russian. https://doi.org/10.34922/AE.2020.33.4.003

Chen Z, Cao K, Xia Y, Li Y, Hou Y, Wang L, Li L, Chang L, Li W. Cellular senescence in ionizing radiation (Review). Oncol. Rep. 2019;42(3):883-894. https://doi.org/10.3892/or.2019.7209

Ziegler DV, Wiley CD, Velarde MC. Mitochondrial effectors of cellular senescence: beyond the free radical theory of aging. Aging Cell. 2015;14(1):1-7. https://doi.org/10.1111/acel.12287

Masaldan S, Clatworthy SAS, Gamell C, Meggyesy PM, Rigopoulos AT, Haupt S, Haupt Y, Denoyer D, Adlard PA, Bush AI, Cater MA. Iron accumulation in senescent cells is coupled with impaired ferritinophagy and inhibition of ferroptosis. Redox Biol. 2018;14:100-115. https://doi.org/10.1016/j.redox.2017.08.015

Rivina L, Davoren MJ, Schiestl RH. Mouse models for radiation-induced cancers. Mutagenesis. 2016;31(5):491-509. https://doi.org/10.1093/mutage/gew019

Article   PubMed   PubMed Central   Google Scholar  

Selman C, Swindell WR. Putting a strain on diversity. EMBO J. 2018;37(22):e100862. https://doi.org/10.15252/embj.2018100862

Pathology of Tumours in Laboratory Animals, 2nd ed. Vol. 1: Tumours of the Rat. Turusov V, Mohr U, eds. IARC, 1990.

Wang Y, Liu L, Pazhanisamy SK, Li H, Meng A, Zhou D. Total body irradiation causes residual bone marrow injury by induction of persistent oxidative stress in murine hematopoietic stem cells. Free Radic. Biol. Med. 2010;48(2):348-356. https://doi.org/10.1016/j.freeradbiomed.2009.11.005

Fielder E, Weigand M, Agneessens J, Griffin B, Parker C, Miwa S, von Zglinicki T. Sublethal whole-body irradiation causes progressive premature frailty in mice. Mech Ageing Dev. 2019;180:63-69. https://doi.org/10.1016/j.mad.2019.03.006

Adelöf J, Ross JM, Lazic SE, Zetterberg M, Wiseman J, Hernebring M. Conclusions from a behavioral aging study on male and female F2 hybrid mice on age-related behavior, buoyancy in water-based tests, and an ethical method to assess lifespan. Aging (Albany NY). 2019;11(17):7150-7168. https://doi.org/10.18632/aging.102242

Download references

Author information

Authors and affiliations.

N. N. Petrov National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, St. Petersburg, Russia

E. A. Yakunchikova, M. N. Yurova, E. A. Radetskaya, K. V. Altukhov, A. L. Semenov, A. V. Panchenko, M. L. Tyndyk, V. N. Bykov & E. I. Fedoros

State Research Test Institute of Military Medicine, Ministry of Defense of the Russian Federation, St. Petersburg, Russia

E. A. Yakunchikova & I. S. Drachyov

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to E. I. Fedoros .

Additional information

Translated from Byulleten’ Eksperimental’noi Biologii i Meditsiny , Vol. 177, No. 3, pp. 356-361, March, 2024

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Yakunchikova, E.A., Yurova, M.N., Drachyov, I.S. et al. Model of Accelerated Aging in CB6F2 Mice Induced by Ionizing Radiation. Bull Exp Biol Med (2024). https://doi.org/10.1007/s10517-024-06190-0

Download citation

Received : 23 November 2023

Published : 10 August 2024

DOI : https://doi.org/10.1007/s10517-024-06190-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• accelerated aging
• ionizing radiation
• oncogenesis
• Find a journal
• Publish with us
• Track your research

• Grand Rapids/Muskegon
• Saginaw/Bay City
• All Michigan

Hello, Grand Rapids: Far-right group loses control of Ottawa board; alligator sighting

• Updated: Aug. 09, 2024, 10:16 a.m.
• | Published: Aug. 09, 2024, 10:11 a.m.

Pictured from left are Ottawa County Commissioner Roger Belknap; Joe Moss, who is chair of the Ottawa County Board of Commissioners; and Sylvia Rhodea, who is vice chair of the Ottawa County Board of Commissioners on Nov. 28, 2023. (MLive.com) Cory Morse | [email protected]

• Monica Scott | [email protected]

GRAND RAPIDS, MI - In 2003, I was a county government reporter in Florida working late one October night when I heard a lot of commotion on the police scanner about an alligator and a child.

I immediately drove the few minutes to the fish camp. It was an emotional and chaotic scene because a 12-year-old boy died after being attacked and pulled underwater by a 10-foot alligator. He had been swimming in the river, about 30 minutes from Orlando, with a couple buddies.

If you purchase a product or register for an account through a link on our site, we may receive compensation. By using this site, you consent to our User Agreement and agree that your clicks, interactions, and personal information may be collected, recorded, and/or stored by us and social media and other third-party partners in accordance with our Privacy Policy.

Top UN official tells Security Council that Islamic State group, affiliates gaining power in Africa

A top U.N. counterterrorism official has told the Security Council that a vast stretch of Africa could fall under the control of groups affiliated with the Islamic State group and affiliated terrorist groups

UNITED NATIONS -- A top U.N. counterterrorism official told the Security Council on Thursday that a vast stretch of Africa could fall under the control of the Islamic State group and affiliated terrorist organizations .

There was no known link between an alleged plot to attack Taylor Swift shows in Vienna and the group or its affiliates elsewhere in the world, but both suspects appeared to be inspired by the Islamic State group and al-Qaida, Austrian authorities said Thursday.

In a regular report to the council, Vladimir Voronkov , the undersecretary for counterterrorism, told members that IS group affiliates have “expanded and consolidated their area of operations” in West Africa and the Sahel.

A “vast territory stretching from Mali to northern Nigeria could fall under their effective control” if their influence continues, Voronkov said.

He said that IS group affiliates have also expanded operations in other parts of the continent, including parts of Mozambique, Somalia, and the Democratic Republic of the Congo, which saw a “dramatic increase in terrorist attacks” that killed large numbers of civilians.

Voronkov told the council that ISIS-K, the group’s Afghanistan affiliate, has “improved its financial and logistical capabilities” in the last six months and increased recruitment efforts. He said IS has demonstrated its global intent by claiming responsibility for ISIS-K attacks and increasing operations in Iraq and Syria.

Trending Reader Picks

Ex-Louisiana mayor is arrested and accused of raping minor following abrupt resignation

• Aug 2, 4:15 PM

Airport conveyor belt cause of death revealed

• Aug 9, 8:42 PM

Trump agrees to ABC News debate with Harris

• Aug 8, 8:38 PM

Romanian hired Hunter Biden to influence US: DOJ

• Aug 7, 5:59 PM

Ukraine continues major incursion into Russia

• Aug 8, 2:32 PM

ABC News Live

24/7 coverage of breaking news and live events

• Manage Account
• Share on Facebook
• Share to Flipboard
• Share on Pinterest
• + additional share options added
• Share on Reddit
• Share on LinkedIn
• Share on Whats App
• Send an Email
• Print this article
• Post a Comment
• Share on Tumblr

How Much of the Recorded Music Market Will the Grainges Control?

In an unprecedented scenario, Universal Music Group chairman/CEO Lucian and his ascendant son Elliot will control more than a third of the U.S. market — at competing companies.

• By Glenn Peoples

Glenn Peoples

If your last name is Grainge, you probably oversee a large chunk of the U.S. music business. 

Following Elliot Grainge ’s promotion to CEO of Atlantic Music Group effective Oct. 1, the Grainge family— Elliot and his father, Lucian Grainge , chairman/CEO of Universal Music Group (UMG) — will control roughly 37.6% of the U.S. recorded music market, according to Billboard ’s analysis of data from Luminate.  

Julie Greenwald to Exit Atlantic Records

The younger Grainge, whose record label 10K Projects was acquired by UMG competitor Warner Music Group in 2023, will lead a record label group with about 7.9% of the U.S. market’s equivalent album units (EAUs). That includes Atlantic Records, which had a 5.3% share through Aug. 1, along with the remaining labels that comprise Atlantic Music Group — 300 Elektra Entertainment (which includes the labels 300, Elektra, Fueled By Ramen, Roadrunner, Low Country Sound, DTA and Public Consumption) and 10K Projects — with an estimated 2.6% share. 

Trending on Billboard

The Grainge’s father-son CEO dynamic is unprecedented even for an industry that often sees the offspring of heavy hitters follow a parent into the business. There have been many family businesses run by successive generations — music publisher peermusic, for example — but never in modern history have a father and son been CEOs of a global music company and a major label music group simultaneously.  

Grainge, age 30, will ascend to CEO of Atlantic Music Group as WMG restructures its organizational chart and Atlantic retools to market music to digital natives (a.k.a. young people). CEO Robert Kyncl is “excited by the prospect of taking Atlantic’s culture making capabilities and adding the 10K Projects founder’s digitally native approach into the mix,” he said during Wednesday’s earnings call.

As Billboard reported in February, Atlantic laid off about two dozen staffers with the intention of “bringing on new and additional skill sets in social media, content creation, community building and audience insights,” with the goal of “dial[ing] up our fan focus and help[ing] artists tell their stories in ways that resonate,” Julie Greenwald , the company’s chairman/CEO, said at the time. Greenwald was to assume the new role of chairman upon Grainge’s promotion but announced her resignation on Tuesday (Aug. 6). She will officially step down at the end of January 2025.

Daily newsletters straight to your inbox

More From Pro

Umg share price regains some losses (with help from wmg) during chaotic week for stocks, inglewood mayor addresses hard summer 2024 noise complaints: ‘lessons learned’.

• By Katie Bain

Please, Please, Please Stop Using My Song: Can Artists Force Politicians to Lay Off Their Music?

• By Steve Knopper

How Chappell Roan Broke an Attendance Record at Lollapalooza Chicago

• By Lyndsey Havens

Billboard is a part of Penske Media Corporation. © 2024 Billboard Media, LLC. All Rights Reserved.

optional screen reader

Charts expand charts menu.

• Billboard Hot 100™
• Billboard 200™
• Hits Of The World™
• TikTok Billboard Top 50
• Songs Of The Summer
• Song Breaker
• Year-End Charts
• Decade-End Charts

Music Expand music menu

• R&B/Hip-Hop

Videos Expand videos menu

Culture expand culture menu, media expand media menu, business expand business menu.

• Business News
• Record Labels
• View All Pro

Pro Tools Expand pro-tools menu

• Songwriters & Producers
• Artist Index
• Royalty Calculator
• Market Watch
• Industry Events Calendar

Billboard Español Expand billboard-espanol menu

• Cultura y Entretenimiento

Get Up Anthems by Tres Expand get-up-anthems-by-tres menu

Honda music expand honda-music menu.