control group and experimental group in a experiment

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Biology archive

Course: biology archive   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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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.

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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!

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Controlled Experiment

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.

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating 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.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

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 that 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 participating in each condition.

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.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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control group

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• 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.

Control Group vs. Experimental Group: What's the Difference?

Key Differences

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Doug Corrance/The Image Bank/Getty Images

Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

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

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.

In conclusion, understanding the difference between a control group and an experimental group is crucial in designing and conducting reliable experiments. The control group serves as a baseline, allowing researchers to compare the effects of the experimental treatment. Without a control group, it is difficult to determine whether any observed effects are due to the treatment or to other factors. By contrast, the experimental group receives the treatment and is used to evaluate the effects of the intervention. By carefully controlling for different factors, scientists can use these groups to test hypotheses and draw meaningful conclusions about the impact of different treatments on the outcomes of interest.

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Frequently asked questions

What is 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

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled 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 .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

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.

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.

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

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.

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, analyzing 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.

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 generalizations —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., extramarital affairs)

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.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• 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.

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 ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

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 .

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

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

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

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 behaviors 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.

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)

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).

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

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 analyze 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 .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably 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 can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

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 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.

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 contextualize 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.

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.

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.

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

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: 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.

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.

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 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.

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).

Triangulation can help:

• Reduce research 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 labor-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 analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

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.

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 defense, 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.

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.

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.

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.

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 aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

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.

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 standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

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

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, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

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.

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.

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.

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

For a probability sample, you have to conduct 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.

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 .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed 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 analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed 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.

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 state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized 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.

You can organize 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. Randomization can minimize the bias from order effects.

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.

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

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.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

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 .

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

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.

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 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.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

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.

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.

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.

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 .

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 : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

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.

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.

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

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• 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

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 .

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

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.

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.

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.

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 dice 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 generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. 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.

“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.

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 .

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.

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.

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.

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.

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

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.

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 .

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 x 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.

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.

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 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.

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 is then collected from as large a percentage as possible of this random subset.

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.

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

Blinding is important to reduce research 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 behavior 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.

• 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 analyzing the data.

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

A true experiment (a.k.a. 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.

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 analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 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 5 or 7 possible responses, to capture their degree of agreement.

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 operationalization .

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

• Prepare and organize 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 .

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 organization 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.

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.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

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 standardize 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, labor-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 organizations.

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

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 randomization , 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.

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.

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.

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.

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!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

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

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.

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 .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

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.

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

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 generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

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

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

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

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

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 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.

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.

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

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.

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

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 analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.

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.

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 .

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 .

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.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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 analyze data (for example, 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.

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30 8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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.

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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.

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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.

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• Published: 09 August 2024

The protection afforded by kefir against cyclophosphamide induced testicular toxicity in rats by oxidant antioxidant and histopathological evaluations

• Songul Cetik Yildiz   ORCID: orcid.org/0000-0002-7855-5343 1 ,
• Cemil Demir   ORCID: orcid.org/0000-0002-6365-0196 1 ,
• Mustafa Cengiz   ORCID: orcid.org/0000-0002-6925-8371 2 ,
• Halit Irmak   ORCID: orcid.org/0000-0002-8184-9377 3 ,
• Betul Peker Cengiz   ORCID: orcid.org/0000-0002-2503-7446 4 &
• Adnan Ayhanci   ORCID: orcid.org/0000-0003-4866-9814 5  

Scientific Reports volume  14 , Article number:  18463 ( 2024 ) Cite this article

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• Cancer prevention
• Immunotherapy

Cyclophosphamide (CTX) is the most commonly used effective alkylating drug in cancer treatment, but its use is restricted because its toxic side effect causes testicular toxicity. CTX disrupts the tissue redox and antioxidant balance and the resulting tissue damage causes oxidative stress. In our study based on this problem, kefir against CTX-induced oxidative stress and testicular toxicity were investigated. Rats were divided into 6 groups: control, 150 mg/kg CTX, 5 and 10 mg/kg kefir, 5 and 10 mg/kg kefir + 150 CTX. While the fermented kefirs were mixed and given to the rats for 12 days, CTX was given as a single dose on the 12th day of the experiment. Testis was scored according to spermatid density, giant cell formation, cells shed into tubules, maturation disorder, and atrophy. According to our biochemical findings, the high levels of total oxidant status (TOS), and the low levels of total antioxidant status (TAS) in the CTX group, which are oxidative stress markers, indicate the toxic effect of CTX, while the decrease in TOS levels and the increase in TAS levels in the kefir groups indicate the protective effect of kefir. In the CTX-administered group, tubules with impaired maturation and no spermatids were observed in the transverse section of the testicle, while in the kefir groups, the presence of near-normal tubule structures and tubule lumens despite CTX showed the protective effect of kefir. In our study, it was observed that kefir had a protective and curative effect on CTX-induced toxicity and oxidative stress and could be a strong protector.

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Introduction.

Antineoplastic drugs may have gonadotoxic effects in varying amounts depending on factors such as the dose, type, and duration of the drug used. Cyclophosphamide (CTX), one of these cytotoxic agents, can cause infertility due to permanent and long-term gonadal toxicity 1 , 2 . Although the cytotoxic effects of CTX, which is widely used in cancer chemotherapy, limit the use of the drug, it also increases oxidative stress, which mediates the disruption of redox balance after exposure and causes many biochemical and physiological disorders 2 . In order for CTX to exert its potential coccoidal effect, it must first be metabolized and activated. CTX-induced immunosuppression occurs due to the release of its metabolites rather than the drug itself. The metabolism of CTX in the liver the formation of acrolein, a cytotoxic metabolite, and a simultaneous increase in reactive oxygen species (ROS) and lipid peroxidation are associated with oxidative stress 3 . The alkylating metabolite of CTX, phosphoramide mustard, is responsible for therapeutic activity and produces a wide range of adverse effects, such as testicular toxicity. Acrolein has also been reported to have adverse effects on fertility, including hemorrhagic cystitis and apoptotic changes in the testicles 4 . Furthermore, because the spermatozoa's mitochondrial membrane is rich in polyunsaturated fatty acids and deficient in antioxidants, it is more vulnerable to lipid peroxidation 5 . In the germinal epithelium, spermatogenesis is a vigorous meiotic division cycle that requires a lot of oxygen from the mitochondria. Nonetheless, low oxygen tension results from inadequate testicular vascularization. Because Leydig cells are sensitive to oxidative stress in both spermatogenesis and steroidogenesis, low oxygen levels may shield tissues from damage by free radicals 6 . These may explain how CTX causes toxicity in organs such as testicles, as CTX disrupts tissue redox balance, and tissue damage resulting from this disruption causes oxidative stress 7 . In many studies, It has been reported that CTX was histologically in testicular tubules may cause a decrease in germinal epithelium height and seminiferous tubule diameter, tubular atrophy, disruption of germinal epithelium and basement membrane integrity, edema in the interstitium, increase in collagen density and Leydig cell atrophy 8 . Normally, free radicals occur in the mitochondria of testicular cells but are scavenged by the antioxidant defense system 5 , 9 . Kefir, a natural antioxidant, is the most important prebiotic and probiotic fermented milk product needed to prevent oxidative damage and cytotoxicity caused by CTX. Fermented kefir slows the growth of cancer cells and accelerates apoptosis, with its immunotherapeutic, antioxidant, and antitumor properties 10 . Studies have shown that kefir exhibits activities such as antioxidative, antimicrobial, and anticarcinogenic properties, and protection against apoptosis 11 .

In this experimental study, the possible protective effect of kefir in the testicular damage model created with cyclophosphamide (CTX) was examined with biochemical and histopathological parameters, and the antioxidant and cytoprotective effects of kefir on testicular toxicity were compared. Since the kefir we used in our experimental study created microbial flora at different times, fermented kefirs on different days were tested. As a result of the test we conducted on the kefirs on different days, no significant change was observed between the kefirs of the 1st, 2nd, and 3rd days, so the kefirs of all days were mixed and used to be given to the rats. Kefir used in experimental studies was used in very different doses and durations. In our study, we gave kefir to rats by gavage method for 12 days.

As seen in Table 1 , our data were scored according to testicular spermatid density, giant cell formation, tubule-sloughed cells, maturation disorder, and atrophy. In the CTX-administered group, testicular spermatid density, giant cell formation, cells sloughed into tubules, maturation disorder, and atrophy levels were seen as moderate changes (score 2). In the groups given CTX + kefir, this moderate change decreased to a slight change (score 1) and approached the control group (Table 1 ).

Total oxidant status (TOS) level, one of the parameters we measured as an indicator of CTX-induced oxidative damage, was found to be very high in the group given a single dose of 150 mg/kg CTX. TOS levels decreased significantly in the groups given 5 and 10 mg/kg kefir along with CTX. As a matter of fact, our findings showed that the TOS level, which increases with oxidative stress caused by CTX, can mostly be eliminated by kefir (Fig.  1 ).

Comparison of TOS values of experimental groups administered Control, 150 mg/kg CTX, 5 mg/kg kefir, 5 mg/kg kefir + 150 mg/kg CTX, l0 mg/kg kefir, l0 mg/kg kefir + 150 mg/kg CTX. (*** p  < 0.001 compared to control; ### p  < 0.001 compared to CTX group).

Comparing the total antioxidant status (TAS) level, which is an important biomarker; In the second group, which was given only CTX, the TAS level decreased significantly. This shows that CTX causes an increase in oxidative stress and has a decreasing effect on antioxidant levels. In the groups given kefir along with CTX, the TAS level increased despite CTX and approached the control level, indicating that kefir has an antioxidative and protective effect (Fig.  2 ).

Comparison of TAS values of experimental groups administered Control, 150 mg/kg CTX, 5 mg/kg kefir, 5 mg/kg kefir + 150 mg/kg CTX, l0 mg/kg kefir, l0 mg/kg kefir + 150 mg/kg CTX. (*** p  < 0.001 compared to control; ** p  < 0.05 compared to control; * p  < 0.01 compared to control; # p  < 0.01 compared to CTX group).

According to testicular histopathology findings, normal tubule lumens (blue star) were seen in the transverse section of the testis of control group animals. In the transverse section of the testicles of the group given 150 mg/kg CTX, maturated tubules (yellow asterisks) with no spermatids were observed. Close to normal tubule structures and tubule lumens were observed in rats given 5 and 10 mg/kg kefir. In the group given CTX + 10 mg/kg kefir, a maturated tubule (yellow star) with no spermatids was observed in the transverse section of the testicle. The histopathological findings of the group given 150 mg/kg CTX + 10 mg/kg kefir were better than the group given 150 mg/kg CTX + 5 mg/kg kefir (Fig.  3 ).

( a ) Normal appearance tubule lumens in the transverse section of the testicle (blue star), ( b ) A tubule with impaired maturation, with no spermatids observed in the transverse section of the testicle (yellow star), ( c ) Close to normal tubule structures and tubule lumens, ( d ) A tubule with impaired maturation, with no spermatids observed in the transverse section of the testicle (yellow star), ( e ) Close to normal tubule structures and tubule lumens, ( f ) Close to normal tubule structures and tubule lumens (H&E; X200).

The most commonly used alkylating type antineoplastic drugs are used in chemotherapy to regress or stop tumor progression. Although chemotherapy basically aims to stop or destroy tumor growth without damaging healthy cells, antineoplastic drugs have low selective properties and although they destroy cancer cells, they can also cause undesirable toxicities on healthy cells. In order to enable Cyclophosphamide (CTX), an antineoplastic drug, to be used more effectively and safely in high doses, studies on the development of methods that prevent its toxic effects are important. Infertility is a major concern for patients receiving CTX therapy. In the testis, cells in the seminiferous tubules of the germinal epithelium are the most sensitive structures to the toxic effects of CTX because they have the highest mitotic and meiotic indices.

Cancer chemotherapy can cause many side effects such as infertility by causing temporary or long-term gonadal damage. According to our data, moderate changes were observed in testicular spermatid density, giant cell formation, shedding of cells into tubules, maturation disorder, and atrophy levels in the group given 150 mg/kg CTX (Table 1 ). In a study, it was reported that although there was no major distortion in the testicles in the CTX group, degeneration, bleeding, and cell loss were observed in the seminiferous tubules of the testicles 7 . Parallel with our study we also stated that CTX-induced reproductive damage can be attributed to oxidative stress and DNA damage 9 . Another factor that may cause oxidative stress in the testicles is defined as infection in the literature 12 . In their study, Kim et al. (2016) also determined shedding, vacuolization, decrease in the number of spermatocytes, and degeneration in the testicular germ cell epithelium of rats given CTX 13 . Likewise, in another study, significant damage such as hemorrhage between the seminiferous tubules, disorganization and separation of cells of the spermatogenic series, and vacuoles in germ cells were detected in the testicles of rats given CTX 14 . In the groups given kefir along with CTX, the moderate change seen in the CTX group decreased to a slight change and approached the control group (Table 1 ). In this sense, in addition to its antioxidant and antitumor properties, kefir also has an anti-inflammatory effect, suggesting that it can eliminate CTX-induced testicular damage due to oxidative stress.

Normally, the oxidative state is in balance with ROS production and ROS elimination in the cell, while disruption of this balance results in damage to the cell 15 . Some reports showed that CTX could disrupt the redox equilibrium of tissues, which suggests that the biochemical and physiological disturbances may result from oxidative stress 8 . In parallel with this information in our study, TOS level, which is an indicator of CTX-induced oxidative damage, was found to be quite high in the group given a single dose of 150 mg/kg CTX (Fig.  1 ). Studies showed that CTX had the lowest Johnsen score mean, consistent with its gonadotoxic effects 16 , 17 . Since both spermatogenesis and Leydig cell steroidogenesis are sensitive to oxidative stress, it is thought that the low oxygen tension that characterizes this tissue may be an important component of the testicles' protective mechanisms from damage caused by free radicals 6 . According to our findings, TOS levels decreased in the groups given 5 and 10 mg/kg kefir along with CTX (Fig.  1 ). Since these undesirable effects of CTX may be due to inducing oxidative stress in tissues and disrupting the oxidant-antioxidant balance 18 , and it has been reported that the expression of antioxidant enzymes is inhibited by CTX treatment, which reduces intratesticular testosterone concentration 19 , we aimed to use kefir, which has antioxidant properties. As a matter of fact, our results showed that kefir alleviated the oxidative damage caused by CTX by reducing the TOS level with its antioxidative properties (Fig.  1 ).

Disruption of the balance between antioxidant and oxidant systems causes toxicities and tissue damage. The toxic effect of CTX is related to its active metabolite, ACR. It has been stated that this toxic effect of CTX occurs by destroying the antioxidant defense systems of acrolein, which is formed as a result of its metabolism, and causes the formation of high amounts of free radicals. In our study, when we compared the TAS level, which is an important biomarker, it was seen that the TAS level decreased significantly in the only dose CTX-given group. Studies have shown that oxidative stress increases with a decrease in antioxidant enzymes 20 and an increase in lipid peroxidation in rats treated with CTX 21 . It is reported that deterioration of the balance between antioxidant and oxidant systems causes tissue damage 22 . Antioxidative biological compounds may protect cells and tissues from the harmful effects of ROS and other free radicals produced during CTX exposure. As a matter of fact, our results show that kefir has an antioxidative and protective effect by increasing the TAS level despite CTX and approaching the control level in the groups where kefir was given together with CTX (Fig.  2 ). In a study, it was found that the oxidative stress index (OSI) value, which shows the status of oxidative and antioxidative systems, was higher in the CTX group than in the control group 23 .

According to testicular histopathology findings, in the transverse section of the testicles of the group given 150 mg/kg CTX, maturated tubules with no spermatids were observed (Fig.  3 ) and this showed that CTX damaged testicular tissue. A study's histological analysis showed that the CTX-treated group's spermatogenetic cells were disorganized and their seminiferous tubules were irregular. Moreover, spermatogenetic cells were shown to pour into the tubular lumen in this investigation, resulting in a decrease in tubule diameter 23 . In accordance with similar studies 24 , 25 , signs of degeneration such as atrophy in seminiferous tubules and decrease in tubule diameter, and loss of spermatogenic cells were observed in our study. Also, a decrease in tubule thickness and loss at the spermatogenic level was reflected in the sperm count and morphology of our CTX-given group findings. In a study localization and morphology of telocytes have been demonstrated in the rat male reproductive system 25 . Moreover, studies have shown that CTX, which induces oxidative stress in testicular and epididymal tissues, can cause a decrease in sperm count and motility. Additionally, it has been shown that oxidative stress caused by CTX can lead to apoptosis and shrinkage in seminiferous tubules, thinned seminiferous epithelium, and a decrease in interstitial cells and spermatogenic cells, especially in post-meiotic stages 26 , 27 . Testis blood barrier damage and aberrant expression of functional proteins were seen in an animal experiment with CTX intervention, wherein Sertoli cells experienced morphological, and functional abnormalities 28 . Fermented kefir, which has antioxidant, anti-apoptotic, anti-lipid peroxidation and anti-inflammatory activities 11 , could alleviate or avoid this damage. In the group given CTX + 10 mg/kg kefir, a maturated tubule with no spermatids was observed in the transverse section of the testicle. The histopathological findings of the group given 150 mg/kg CTX + 10 mg/kg kefir were better than the group given 150 mg/kg CTX + 5 mg/kg kefir (Fig.  3 ). We have previously shown that immune activities have been observed in humans and various animals after ingestion of lactic acid bacteria found in kefir, and it has been observed that lactic acid bacteria increase non-specific resistance against tumors or infections in humans or animals or have a strengthening effect on specific immune reactions 11 . In other studies, it has been reported that kefir consumption has antioxidant, and anticarcinogenic effects 29 , 30 , 31 , in parallel with the results of our study. In this study, it was observed that kefir was histologically and biochemically effective against the toxic effects of CTX on the testicles when high doses were required. For this reason, it can be stated that kefir can be used as an alternative supplement when CTX is used in high doses.

The use of complementary and alternative treatments, which prevent the toxic effects of many antineoplastic chemical agents such as CTX and allow them to be used in higher or effective doses for a long time, has increased rapidly recently and has gained importance in many areas including the medical industry, the country's economy, and even social psychology. The most severe histopathological, and biochemical picture was seen in the group where high-dose CTX was used, confirming that CTX has a highly toxic effect on the testicles. An important way to ensure the effectiveness of cancer treatment is to regulate the microbiota through probiotic consumption. So, kefir is a very effective agent both in reducing the side effects of CTX and in providing cancer immunotherapy that uses the power of the patient's own immune system to destroy cancerous cells. In addition, we hope that our study will contribute to the literature for future scientific studies.

Kefir fermentation

In our study, commercially supplied and freeze-dried kefir yeast and 1 L of cow’s milk were preferred for kefir fermentation. Three groups of kefirs were created, with fermentation at 24–26 °C temperature at intervals of 24, 48, and 72 h on days 1, 2, and 3. It was kept at + 4 °C ready for use. We gave kefir to rats by gavage method for 12 days. Kefirs from the 1st, 2nd, and 3rd days were mixed and given by gavage method for 12 days.

Chemicals and injections

Cyclophosphamide (CTX) (Sigma-Aldrich) was commercially available. 500 mg CTX was dissolved in 25 ml bidistilled water to prepare for injection of 150 mg/kg CTX. The injection was performed as a single dose intraperitoneally (i.p.)/body-weight (b.w.) on the 12th day of the experiment, using sterile disposable syringes.

Ethical approval

This experimental study was approved by the Ethics Committee of Eskisehir Osmangazi University Animal Experiments Local Ethics Committee (784-145 / 2020. And the entire study was conducted in accordance with the Animal Experiments Local Ethics Committee Directive.

Experimental setup

In our experimental study, healthy, males, 200 ± 20 gr, about 3 months age Wistar albino rats were used. During the experiment, the animals were kept in rooms with 12;12 light/dark lighting, 45–50% humidity, and 22 ± 2 °C temperature. And were given tap water and normal pellet feed. The 42 rats used in this study were divided into 6 groups, each group including 7 rats. Group 1 (control), single dose of 150 mg/kg/b.w CTX to the 2nd group, 5 mg/kg/b.w kefir to the 3rd group, 5 mg/kg/b.w kefir + 150 mg/kg/b.w CTX to the 4th group, 10 mg/kg/b.w kefir was given to the 5th group, 10 mg/kg/b.w kefir + 150 mg/kg/b.w CTX was given to the 6th group. Kefir was given to rats by gavage method for 12 days. A single dose of CTX was given i.p. on the last day of the experiment, namely the 12th day. At the end of the experiment, biochemical parameters and testicular tissues were taken under anesthesia.

Biochemical parameters

Total antioxidant status (tas) (mmol/l).

TAS levels were measured using commercially available kits (Relassay, Turkey). The novel automated method is based on the bleaching of the characteristic color of a more stable ABTS (2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) radical cation by antioxidants. The assay has excellent precision values, which are lower than 3%. The results were expressed as mmol Trolox equivalent/L.

Total oxidant status (TOS) (µmol/L)

TOS levels were measured using commercially available kits (Relassay, Turkey). In the new method, oxidants present in the sample oxidized the ferrous ion-o-dianisidine complex to the ferric ion. The oxidation reaction was enhanced by glycerol molecules abundantly present in the reaction medium. The ferric ion produced a colored complex with xylenol orange in an acidic medium. The color intensity, which could be measured spectrophotometrically, was related to the total amount of oxidant molecules present in the sample. The assay was calibrated with hydrogen peroxide and the results were expressed in terms of micromolar hydrogen peroxide equivalent per liter (μmol H2O2 equivalent/L).

Histopathology

Before being examined under a light microscope, tissue samples were preserved in a 10% Neutral Buffer formaldehyde solution. Following identification, tissue samples were put into cassettes and given a two-hour rinse under running water. Tissues were run through a succession of increasing alcohol concentrations (60–100%) in order to extract water. The tissues were then polished by passing them through xylene before being implanted in melted paraffin. For each group, 4-micron-thick slices were cut from paraffin blocks and stained with hematoxylin–eosin stain. Using the Leica Q Vin 3 program on the Leica DCM 4000 computer-aided imaging system (Germany), the sections were assessed and captured on camera. A criteria table was created as a result of the evaluations made with Hematoxylin–Eosin (H&E) staining.

The quantitative values we obtained at the end of the study were evaluated by applying the Duncan test after one-way ANOVA, which is used in the statistical analysis of more than two independent groups, with the SPSS 26.00 statistical data program.

Data availability

The authors declare that all data supporting the findings of this study are available within the paper. Moreover, the datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This study was financed by Mardin Artuklu University BAP Coordination Office (MAU.BAP.20.SHMYO.004). Ethical statement: This exprimental study was approved by Ethics Committee of Eskisehir Osmangazi University Animal Experiments Local Ethics Committee (784-145 / 2020. And the entire study was conducted in accordance with the Animal Experiments Local Ethics Committee Directive. A part of this study was presented at IV. International Siirt Conference on Scientific Research Siirt University, November 17-18, 2023.

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Department of Medical Services and Techniques, Health Services Vocational School, Mardin Artuklu University, Mardin Artuklu University Campus, 47200, Mardin, Turkey

Songul Cetik Yildiz & Cemil Demir

Department of Elementary Education, Faculty of Education, Siirt University, Siirt, Turkey

Mustafa Cengiz

Department of Computer Sciences, Mardin Artuklu University, Mardin, Turkey

Halit Irmak

Eskisehir Yunus Emre State Hospital, Eskisehir, Turkey

Betul Peker Cengiz

Department of Biology, Science Faculty, Eskisehir Osmangazi University, Eskişehir, Turkey

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S.C.Y.: Writing – original draft, Review & editing, Methodology, Visualization. C.D.: Conceptualization, Methodology, Review & editing. M.C.: Visualization, Conceptualization, Review & editing. B.P.C.: Formal analysis, Methodology H.I.: Statistical analysis, Methodology A.A.: Conceptualization, Review & editing, All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Songul Cetik Yildiz .

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Cetik Yildiz, S., Demir, C., Cengiz, M. et al. The protection afforded by kefir against cyclophosphamide induced testicular toxicity in rats by oxidant antioxidant and histopathological evaluations. Sci Rep 14 , 18463 (2024). https://doi.org/10.1038/s41598-024-67982-y

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Therapeutic efficacy of thrombin-preconditioned mesenchymal stromal cell-derived extracellular vesicles on Escherichia coli -induced acute lung injury in mice

• Yuna Bang 1 , 4   na1 ,
• Sein Hwang 1 , 2   na1 ,
• Young Eun Kim 1 , 3 ,
• Dong Kyung Sung 1 ,
• Misun Yang 1 , 3 ,
• So Yoon Ahn 1 , 3 ,
• Se In Sung 1 , 3 ,
• Kyeung Min Joo 2 , 4 &
• Yun Sil Chang 1 , 2 , 3  

Respiratory Research volume  25 , Article number:  303 ( 2024 ) Cite this article

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Acute lung injury (ALI) following pneumonia involves uncontrolled inflammation and tissue injury, leading to high mortality. We previously confirmed the significantly increased cargo content and extracellular vesicle (EV) production in thrombin-preconditioned human mesenchymal stromal cells (thMSCs) compared to those in naïve and other preconditioning methods. This study aimed to investigate the therapeutic efficacy of EVs derived from thMSCs in protecting against inflammation and tissue injury in an Escherichia coli (E. coli) -induced ALI mouse model.

In vitro, RAW 264.7 cells were stimulated with 0.1 µg/mL liposaccharides (LPS) for 1 h, then were treated with either PBS (LPS Ctrl) or 5 × 10 7 particles of thMSC-EVs (LPS + thMSC-EVs) for 24 h. Cells and media were harvested for flow cytometry and ELISA. In vivo, ICR mice were anesthetized, intubated, administered 2 × 10 7 CFU/100 µl of E. coli . 50 min after, mice were then either administered 50 µL saline (ECS) or 1 × 10 9 particles/50 µL of thMSC-EVs (EME). Three days later, the therapeutic efficacy of thMSC-EVs was assessed using extracted lung tissue, bronchoalveolar lavage fluid (BALF), and in vivo computed tomography scans. One-way analysis of variance with post-hoc TUKEY test was used to compare the experimental groups statistically.

In vitro, IL-1β, CCL-2, and MMP-9 levels were significantly lower in the LPS + thMSC-EVs group than in the LPS Ctrl group. The percentages of M1 macrophages in the normal control, LPS Ctrl, and LPS + thMSC-EV groups were 12.5, 98.4, and 65.9%, respectively. In vivo, the EME group exhibited significantly lower histological scores for alveolar congestion, hemorrhage, wall thickening, and leukocyte infiltration than the ECS group. The wet-dry ratio for the lungs was significantly lower in the EME group than in the ECS group. The BALF levels of CCL2, TNF-a, and IL-6 were significantly lower in the EME group than in the ECS group. In vivo CT analysis revealed a significantly lower percentage of damaged lungs in the EME group than in the ECS group.

Intratracheal thMSC-EVs administration significantly reduced E. coli -induced inflammation and lung tissue damage. Overall, these results suggest therapeutically enhanced thMSC-EVs as a novel promising therapeutic option for ARDS/ALI.

Acute respiratory distress syndrome (ARDS) and severe acute lung injury (ALI) are critical respiratory diseases characterized by uncontrolled inflammation, bilateral lung damage, fibrosis, and non-cardiogenic pulmonary edema [ 1 , 2 , 3 ]. ARDS/ALI develops by primary causes including bacterial or viral pneumonia, inhalation of toxic substances, or sepsis, and leads to poor prognosis and high mortality rates [ 2 , 4 , 5 ]. Treatment strategies for ARDS/ALI traditionally involve combinations of antibiotics and prone positioning to address individual symptoms without any definite treatment options. Considering the severity and diverse complications, such as inflammation, edema, and fibrosis, a comprehensive therapeutic approach to improve the overall pathophysiology of ALI is urgently needed [ 6 , 7 ].

Recently, new therapeutic modalities for ARDS/ALI are being explored using mesenchymal stromal cells (MSCs) or MSC-derived extracellular vehicles (EVs) [ 8 , 9 , 10 ]. The therapeutic efficacy of MSCs depends on paracrine signaling, with bioactive factors released into EVs [ 11 , 12 , 13 ]. Paracrine enhancement of MSCs through various priming methods has shown promise because EV cargo content is stimulus-dependent [ 14 , 15 ]. Our previous investigations revealed that EV production and cargo content significantly increase, primarily via proteinase-activated receptor (PAR)-1 and partly via a PAR3-dependent pathway, in thrombin-preconditioned human MSCs (thMSCs) compared to those in naïve and other preconditioning methods [ 16 , 17 ]. Furthermore, the transplantation of thMSCs in neonatal rat models of intraventricular hemorrhage and hypoxic-ischemic encephalopathy, as well as thMSC-derived EVs in neonatal meningitis, confirmed their significant therapeutic efficacy in attenuating inflammation, decreasing cell death, and reducing subsequent tissue injuries [ 18 , 19 , 20 ].

Among the pathophysiological processes of ALI, inflammation, leukocyte infiltration, impaired vascular permeability, edema, and fibrosis are closely associated with PAR signaling, a member of the G protein-coupled receptor family expressed in epithelial, endothelial, and immune cells [ 21 , 22 ]. On inflammation and tissue damage, increased thrombin production cleaves and activates PARs triggering a cascade of reactions, leading to the release of prothrombotic mediators, inflammatory cytokines, and chemokines IL-6, TNF-α, and CCL2 [ 22 ]. This cascade increases vascular permeability, endothelial activation, and edema, ultimately causing severe tissue injury [ 23 , 24 , 25 ] .

Thus, we hypothesized that EVs derived from MSCs with enhanced function via thrombin preconditioning-mediated PAR activation (thMSC-EVs) would provide substantial protection against ARDS/ALI [ 17 ]. This study aimed to assess the therapeutic efficacy of EVs from thrombin-preconditioned Warton jelly-derived MSCs (thWJ-MSCs) in an Escherichia coli (E. coli) -induced ALI mouse model. To our knowledge, this is the first investigation of thrombin-preconditioned MSC-derived EVs in an ARDS/ALI preclinical model.

WJ-derived MSCs preparation

Human WJ-MSCs were provided by the Good Manufacturing Practice Facility of Samsung Medical Center and expanded as previously described [ 20 ]; WJ-MSCs from passage 6 were used in this study and were characterized of its surface markers, proliferation rate, and differentiation potential according to the minimal MSC criteria set by the ISCT (Supplementary Figure S1 ). WJ-MSCs were cultured in minimum essential medium (MEM)-α (Gibco; Grand Island, NY, USA) with 10% fetal bovine serum (FBS. Gibco; Grand Island, NY, USA) and 0.1% gentamicin (Gibco; Grand Island, NY, USA) in a 5% CO 2 humidified incubator at 37 ℃. Thrombin preconditioning was done following the previously established method [ 20 ]. Briefly, At 90% confluency, the culture medium was washed three times with Dulbecco’s phosphate-buffered saline (Welgene; Daegu, South Korea) to remove residual FBS, and replaced with serum-free MEMα supplemented with 20 units/mL of thrombin (Reyon Pharmaceutical Co, Ltd; Seoul, South Korea) for 3 h. The levels of HGF and VEGF in thrombin-preconditioned WJ-MSCs measured from the conditioned medium are presented in Supplementary Figure S2 .

EV isolation & quantification

The thrombin preconditioned medium of WJ-MSCs was harvested and filtered using a 0.2 μm bottle top vacuum filtration system (Corning; Corning, NY, USA). EVs were then isolated and diafiltrated in DPBS using a tangential flow filtration system (KrosFlo ® KR2i, Repligen; Waltham, MA, USA) with pore size 300 kDa mPES membrane (S02-E300-05-N, Repligen; Waltham, MA, USA). Subsequently, the concentrated EVs were filtered via a 0.2 μm filter (S6534-FMOSK, Sartorius; Göttingen, Germany) and analyzed using Nanoparticle Tracking Analysis (NanoSight NS300; Malvern, Malvern, UK) (Fig.  1 ). thMSC-EVs were aliquoted and stored at -70 ℃ until subsequent experiments. thMSC-EVs were confirmed of markers GM130 (1:1000; Cell Signaling Technology, Danvers, MA, USA), TSG101 (1:1000; Abcam, Cambridge, UK), and flotillin-1 (1:1000; Cell Signaling Technology, Danvers, MA, USA) using western blot. The size of naïve MSC-EVs are presented in Supplementary Figure S3 .

Characterization of thrombin preconditioned WJ-MSCs-derived EVs. ( A ) Nanoparticle tracking analysis (NTA) evaluated protein concentration and size distribution. ( B ) EV-specific markers were analyzed using western blot. GM130, negative EV marker (Golgi membrane marker); TGS101, and Flotillin-1 are positive markers of EV surface. Full blot images can be found in Figure S9. GM130, Golgi matrix protein 130; TGS101, Tumor susceptibility gene 101; FLOT-1, Flotillin-1

E. Coli preparation

Kanamycin-resistant E. coli strain E69 was kindly provided by Dr. Kwang Sik Kim from Johns Hopkins Hospital. The E. coli was cultured in suspension overnight in Brain-Heart-Infusion broth (BHI, BD Bioscience; Franklin Lakes, NJ, USA) with 53 µg/mL kanamycin (Sigma Aldrich; Burlington, Massachusetts, USA) at 37 °C and 200 rpm. 300 µL of cultured broth was freshly diluted in 7 mL BHI broth, further incubated for 2 h, and centrifuged for 7 min at 3500 rpm. Optical density (OD) was measured at 600 nm and diluted to an OD value of approximately 0.6 using a Multiskan Sky spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). 100 µl of E. coli culture was then spread on BHI agar plates using a sterilized spreader (SPL Life Science, Pocheon-si, South Korea) and incubated the plates overnight at 37 °C. The final E. coli concentration in 50 µL normal saline was 10 7 colony-forming units (CFU).

In vitro LPS-induced ALI inflammation modeling using alveolar macrophages

Alveolar macrophage cell line, RAW 264.7 (Korean Cell Line Bank; Seoul, Republic of Korea), were maintained in Dulbecco’s Modified Eagle Medium (Gibco; Grand Island, NY, USA) supplemented with 10% FBS and 1% penicillin/streptomycin (Invitrogen, Carlsbad, California, USA) in a humidified chamber under 5% CO 2 at 37 ℃, as previously described [ 26 ]. At 80% confluence, RAW264.7 cells were stimulated with 0.1 µg/mL lipopolysaccharide (LPS O111:B4. Sigma Aldrich; Burlington, Massachusetts, USA) for 1 h in a 96-well plate. Then, equal volumes (5 µL) of PBS and thMSC-EVs (5 × 10 7 particles / 5 µL) were added as the LPS Ctrl and LPS + thMSC-EVs groups, respectively, and maintained for 24 h. Culture media were collected for Enzyme-linked immunosorbent assay (ELISA) of pro-inflammatory cytokines.

Flow cytometric analysis

The extent of M1 activation and M2 activation in RAW 264.7 cells was assessed using flow cytometry. RAW 264.7 cells in the normal control (NC), LPS Ctrl, and LPS + thMSC-EV groups were collected and centrifuged at 450 × g at 4 °C for 10 min. Anti-CD86 antibody (BD Biosciences, Franklin Lakes, NJ, USA) and anti-CD206 antibody (BD Biosciences, Franklin Lakes, NJ, USA) were incubated for 20 min and FACS was performed as previously described [ 27 ]. Dead cells and doublets were excluded from the population.

ALI animal model

All animal experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC, Approval number: 20,230,126,004) of the Samsung Biomedical Research Institute, an AAALAC International (Association for Assessment and Accreditation of Laboratory Animal Care)-accredited facility, under the National Institutes of Health Guidelines for Laboratory Animal Care. A brief description of the experimental design is presented in Fig.  2 . 8 weeks old ICR male mice were purchased from Orient Co. (Seoul, Republic of Korea) and stabilized for 1 week. Experimental ALI induction was performed as shown in Fig.  2 . The mice were anesthetized using an intraperitoneal (IP) injection of 45 mg/kg ketamine and 8 mg/kg xylazine cocktail. The vocal cords of the mice were visualized using an otoscope by placing the animals in an inclined plane, as previously described [ 28 ]. Mice were endotracheally intubated using a catheter (introcan certo catheter 22G, 0.9 \(\:\times\:\) 25 mm, B/Braun, Melsungen, Germany). E. coli (2 × 10 7 CFU/100 µL) was administered into the lungs of mice via the catheter. 50 min after E. coli administration, the ALI control (ECS) and thMSC-EVs treated (EME) groups were administered equal volumes of saline (50 µL) and thMSC-EVs (1 × 10 9 particles/50 µL), respectively. The timing of administration was determined based on time-dependent cytokine and bacterial CFU measurements (Supplementary Figure S5 , Supplementary Table 1 ). The survival and body weight of the mice were monitored daily (Supplementary Figure S6 ). Ceftriaxone (100 mg/kg) was administered IP once daily. In vivo micro-computed tomography (micro-CT) scanning of the lungs was performed two days post-injury. On day 3, the mice were anesthetized with pentobarbital (60 mg/kg, IP) to collect the lung tissues and bronchoalveolar lavage fluid (BALF) for further analysis. Transcardial perfusion was performed prior to tissue excision. The excised lungs were then inflated with saline using a 3 mL syringe, fixed overnight in 4% paraformaldehyde, and embedded in a paraffin block. BALF was collected by irrigating twice with 1 mL of aseptic saline using a 22-gauge catheter, as previously described [ 26 ].

In vivo experimental design. ALI, acute lung injury; NC, normal control; E. coli , Escherichia coli; thMSC-EVs, EVs derived from thrombin-preconditioned Warton’s jelly MSCs; IP, intraperitoneal injection; IT, intratracheal injection; Micro CT, Micro-computed tomography

Micro CT imaging and lung injury analysis

Micro CT scanning was performed using the Siemens Inveon Micro-PET/CT scanner (Siemens Medical Solutions, Knoxville, TN, USA). All micro CT analysis protocol and figures are presented in Table S2 , Figure S7 , and Figure F8 under the Supplementary File. The mice were anesthetized with 2–3% isoflurane in 100% oxygen during the scan. The CT images were obtained during the expiratory breathing phase. Briefly, each mouse was scanned for 20 min using a 1.5 mm-thick aluminum filter. For each scan, the Inveon Acquisition Workplace (IAW, Siemens Medical Solutions, Knoxville, TN, USA) software package was used to reconstruct the data into an effective pixel size with a downsampling factor of 2 using the Shepp and Logan filter back-projection algorithm. A phantom scan was conducted for Hounsfield unit (HU) calibration, establishing grayscale values for air and water (ranging from 0 to -1000 HU).

Ten axial micro-CT slices were selected for tissue analysis. The criteria for slide selection were strictly applied to all animals to reduce inter-animal variance. The first slide without a visible diaphragm was marked as the first slide. Every eighth image and ten consecutive images were selected. All images for analysis were matched for 24-bit and the same field of view was used to remove variance. The aerated regions of the lung were semi-automatically measured using the Inveon Research Workplace (IRW, Siemens Medical Solutions, Knoxville, TN, USA) software. The tissue regions of the lungs, excluding the heart, were manually outlined by the investigator using the ImageJ software (National Institutes of Health, Bethesda, MD, USA). The detailed methods are provided in the Supplementary Material. The percentage of damaged tissue was calculated using the following equation:

All analyses were performed blindly.

Lung injury scores

Paraffin-embedded lung tissues were sectioned into 5 μm-thick slices, deparaffinized, and stained using hematoxylin and eosin (H&E). Three slides per tissue representing the ventral, medial, and dorsal regions of the lungs were selected for analysis. Furthermore, a total of 24 serial images, 8 images from the left lobe and 16 images from the right lobe, were taken and scored. Histological lung injury scores were measured using the following four criteria specified in a previous study [ 28 ]: alveolar congestion, alveolar hemorrhage, infiltration of leukocytes, and thickening of the alveolar wall. Each category was scored on a five-point scale from 0 to 4 as follows: 0, no or minimal lung injury and 1, 2, 3, and 4 with lung injury in 25, 50, 75 and > 75% of the field, respectively. All data were analyzed blindly.

Wet-dry lung ratios

To assess pulmonary edema, the wet weight of the lungs was measured immediately after excision. Subsequently, the lungs were dried at 60 ℃ for 72 h to measure the dry weight. The wet/dry ratio for each lung was assessed by dividing the mass of the wet lung by that of the dry lung, as described previously [ 28 ].

ELISA for inflammatory cytokines

To assess the levels of pro-inflammatory cytokines, such as CCL-2, IL-1α, IL-1β, IL-6, TNF-α, and Interferon Gamma (IFN-γ) in the lungs, commercial ELISA kits (R&D Systems, Minneapolis, MN, USA) were used following the manufacturer’s instructions.

Statistical analyses

All data were analyzed using GraphPad Prime 8 software (GraphPad, San Diego, CA, USA). Survival curves were assessed using the log-rank test. One-way analysis of variance with post-hoc Tukey’s test was used to statistically compare the experimental groups. Data in bar graphs are presented with mean ± standard error of mean (SEM). In the box and whisker plots, the first, median, and third quartiles are presented as boxes, and the minimum and maximum are presented as whiskers. Specific p values and sample populations are indicated in the figure legends. Statistical significance was set at p  < 0.05.

thMSC-EVs significantly reduced M1 polarization and pro-inflammatory cytokine secretions of LPS-induced RAW 264.7 cells

LPS-induced mouse alveolar macrophage RAW 264.7 cells were treated with either thMSC-EVs or PBS after 1 h of LPS stimulation (Fig.  3 A). The levels of inflammatory cytokines (IL-1β, IL-6, and CCL-2) and MMP-9 and the extent of M1 macrophage polarization were measured 24 h after (Figs.  3 B and 4 ) using ELISA and flow cytometry, respectively. The levels of IL-1β, IL-6, CCL-2, and MMP-9 were significantly higher in the LPS Ctrl and LPS + thMSC-EVs groups than those in the NC group. LPS + thMSC-EVs group was significantly lower in the levels of IL-1β, CCL-2, and MMP-9 compared to the LPS Ctrl group, however, the level of IL-6 did not reach statistical significance. FACS confirmed that the percentages of M1 polarization in the normal control, LPS Ctrl, and LPS + thMSC-EV groups were 12.5, 98.4, and 65.9%, respectively (Fig.  4 ). The percentages of M2 polarization in the normal control, LPS Ctrl, and LPS + thMSC-EV groups were < 0.3%, 21.7%, and 29.7%, respectively. Dead cells and doublets were excluded from the population (Supplementary Figure S4 ).

Anti-inflammatory effect of thMSC-EVs in LPS-stimulated RAW 264.7 cells. ( A ) Study design of the LPS-stimulated RAW 264.7 cells ALI in vitro model. ( B ) The levels of pro-inflammatory cytokines IL-1β, IL-6, CCL-2, and MMP-9 were measured using ELISA. n  = 4, 6, 6 in the NC, LPS Ctrl, and LPS + thMSC-EVs groups, respectively. Data are presented as box and whisker plot. whiskers represent the min and max. **, p  < 0.01 vs. NC; ##, p  < 0.01 vs. LPS Ctrl group. One-way analysis of variance (ANOVA) post hoc Tukey analysis was used. NC, normal control; LPS Ctrl, LPS control group; LPS + thMSC-EVs, thMSC-EVs treated group

Flow cytometric analysis of LPS-induced RAW 264.7 cells. Percent representation of CD86 (a marker of M1 macrophage) and CD206 (a marker of M2 macrophage) expressing RAW 264.7 cells. NC, normal control; LPS Ctrl, LPS control group; LPS + thMSC-EVs, thMSC-EVs treated group

Intratracheal administration of thMSC-EVs attenuated tissue injury in E. Coli -induced ALI mouse

The lungs of E. coli -induced ALI mice were excised, fixed, paraffin-embedded, and sectioned for histological evaluation. Figure  5 A shows the representative H&E-stained lung sections from each group. Histologic scores of ALI pathophysiology including alveolar congestions, hemorrhage, wall thickening, and leukocyte infiltration were higher in the E.coli- induced ALI lung tissues than in the NC group (Fig.  5 B). These elevated scores were significantly reduced after thMSC-EV administration, as evidenced by the significantly lower scores in the EME group than those in the ECS group.

Intratracheal administration of thMSC-EVs attenuated tissue injury in E. coli -induced ALI mice. ( A ) Representative microscopic images of lung tissues of each group. (Original magnification; \(\:\:\times\:\:\) 40, scale bars; 200 μm) ( B ) Scored histological grades of alveolar congestion, alveolar hemorrhage, leukocyte infiltration, and alveolar wall thickening in lung tissue. n  = 10, 14, and 19 in NC, ECS, and EME groups, respectively. Data are presented as a box and whisker plot. Whiskers represent the min and max. **, p  < 0.01 vs. NC; #, p  < 0.05 vs. ECS; ##, p  < 0.01 vs. ECS. One-way ANOVA post hoc Tukey was used. NC, normal control; ECS, E. coli -induced ALI control group; EME, thMSC-EVs treatment group after E. coli -induced ALI

Intratracheal thMSC-EV administration attenuated pulmonary edema in E. Coli -induced ALI mouse

The extent of E. coli -induced pulmonary edema was evaluated by measuring the tissue wet-dry mass ratio. Representative images of tissues are shown in Fig.  6 A. A higher wet-dry ratio indicates more fluid in the lung tissue. E. coli induction significantly increased the wet-dry ratio, in both the ECS and EME groups compared to that in the NC group (Fig.  6 B). However, this ratio was significantly lower in the EME group than in the ECS group.

Lung tissue and lung water content. ( A ) The lung tissue of NC, ECS, and EME groups. ( B ) Lung water content was measured as wet-dry lung ratio in normal control, ALI control, and treated with the thMSC-EVs group. n  = 14, 19, and 23 in the NC, ECS, and EME groups, respectively. Data are presented as a box and whisker plot. Whiskers represent the min and max. **, p  < 0.01 vs. NC; #, p  < 0.05 vs. ECS. One-way ANOVA post hoc Tukey was used. NC, normal control; ECS, E. coli -induced ALI control group; EME, thMSC-EVs treatment group after E. coli -induced ALI

thMSC-EVs attenuated inflammatory cytokine levels in the BALF of E. Coli -induced ALI mouse

The obtained BALF samples were used for cytokine analysis. Levels of the pro-inflammatory cytokines, CCL-2, IL-1α, IL-1β, IL-6, TNF-α, and IFN-γ were measured (Fig.  7 ). These were significantly higher in the ECS group than in the NC group. Only IL-1α, IL-1β, and TNF-α levels were significantly increased in the thMSC-EVs-treated EME group compared to those in the NC group. The levels of CCL2, TNF-α, and IL-6 in the EME group were significantly lower than those in the ECS group. Moreover, the levels of CCL2, IFN-γ, and IL-6 were not significantly different from those in the NC group. The levels of IL-1α and IL-1β in the EME group were not significantly different from those in the ECS group, however, the mean value was lower in the EME group.

Intratracheal administration of thMSC-EVs attenuated inflammatory cytokine secretion in E. coli -induced ALI mice. The levels of pro-inflammatory cytokines such as CCL-2, IL-1α, IL-1β, INF-γ, TNF-α, and IL-6 were measured using ELISA. n  = 18, 25, and 25 in the NC, ECS, and EME groups, respectively. Data are presented as a box and whisker plot. Whiskers represent the min and max. **, p  < 0.01 vs. NC; *, p  < 0.05 vs. NC; #, p  < 0.05 vs. ECS. One-way ANOVA post hoc Tukey analysis was used. NC, normal control; ECS, E. coli -induced ALI control group; EME, thMSC-EVs treatment group after E. coli -induced ALI

CT analysis revealed that thMSC-EVs significantly attenuated lung damage in E. Coli -induced ALI mouse

In vivo, micro-CT was performed on each mouse to assess lung damage using the calculation of the percentage of damaged lung regions (Fig.  8 A). The HU setting allows air-exchanging lung parenchyma to be distinguished from denser tissue areas such as infected lesions and injured tissue areas. The aerated lung parenchyma appeared dark, whereas lesions appeared as white patches. These images were used to generate a 3D-rendered aerated parenchyma (Fig.  8 B) and calculate the percentage of damaged tissue (Fig.  8 C). In Fig.  8 B, only the aerated tissue regions appeared white, allowing visualization of the retained air-exchanging parenchyma. The percentage of damaged tissue in both E. coli -induced ECS and EME groups were significantly higher than that in the NC group. However, thMSC-EVs administration significantly reduced the percentage of damaged lungs in the EME group compared with that in the ECS group (Fig.  8 C).

Computerized tomography (CT) scans of mice lungs. ( A ) The air-filled areas in the mice lung CT scans appeared as dark backgrounds, while the lesions resulting from the E. coli administration were observed as hyperintense patches. ( B ) Semi-automated 3D rendered image of the mouse lung parenchyma and airways. The white region represents the aerated parenchyma, allowing visualization of retained parenchyma. ( C ) Calculated percent of damaged lung using CT images from Fig.  8 A. n  = 4, 23, and 21 in the NC, ECS, and EME groups, respectively. Data are represented as a box and whisker plot. Whiskers represent the min and max. **, p  < 0.01 vs. NC; ##, p  < 0.01 vs. ECS. One-way ANOVA post hoc Tukey analysis was used. NC, normal control; ECS, E. coli -induced ALI control group; EME, thMSC-EVs treatment group after E. coli -induced ALI

In the present study, we have demonstrated that intratracheal thMSC-EVs administration significantly attenuated lung injury in E. coli -induced ALI mice, as evidenced by decreased lung edema and inflammatory cytokine levels in the BALF, as well as decreased histological lung injury scores and damaged regions observed in lung CT. Numerous studies have investigated the therapeutic effects of MSC-EVs in ALI animal models [ 8 , 9 , 10 ] and proposed MSC-EVs as promising therapeutic candidates for ALI owing to their cell-free nature and low immunogenicity [ 29 , 30 ]. However, enhancing the regenerative and protective potency of MSCs is critical, considering their severity, which leads to a high mortality rate. We have previously investigated the enhanced therapeutic efficacy of preconditioned MSCs, specifically with LPS and thrombin [ 26 , 27 , 31 ]. Preconditioning involves educating MSCs before transplantation to the injured area by pre-exposing them to specific stimuli, such as LPS or E. coli for inflammation and thrombin for hemorrhagic injury, allowing MSCs to readily exert therapeutic effects. E. coli -preconditioned MSCs exert anti-inflammatory and bactericidal effects by secreting defensin [ 31 ], whereas thrombin-preconditioned MSCs significantly increase angiogenic factors which improve vascular permeability and tissue injury through PAR1 and PAR3 signaling [ 17 ]. We have further confirmed the enhanced therapeutic efficacy of thMSCs compared to the naïve MSCs in neonatal IVH study, and further confirmed equivalent therapeutic efficacy of thMSCs and thMSC-EVs in neonatal meningitis study [ 18 , 32 ]. Building on our previous confirmation of antimicrobial, anti-inflammatory, and anti-apoptotic effects of LPS-preconditioned MSCs in an E. coli -induced ALI mouse model [ 26 , 31 ], this study aimed to further assess the therapeutic efficacy of thrombin-preconditioned MSCs derived EVs in tissue injuries using the same E. coli -induced ALI mouse model in the present study.

The interplay between inflammation and coagulation pathways mediating PAR activation in ARDS/ALI is well known to induce severe and diffuse lung tissue injury [ 2 , 22 , 23 , 25 , 33 , 34 ]. Among the four PAR subtypes, PAR1, PAR3, and PAR4 are activated by thrombin, a serine protease that regulates blood coagulation. Hemorrhage increases thrombin levels in the area, further activating PAR-expressing endothelial, epithelial, and immune cells. Upon infection, activated immune cells induce vascular permeability, recruiting immune cells and blood coagulation factors to the injury site. Hypercoagulability can increase circulatory levels of fibrinogen and d-dimer, not only within the blood but also in the lungs [ 35 ]. Fibrin formation, reflecting localized microthrombi and endothelial damage in the pulmonary microcirculation, leads to plasma exudation, tissue factor-mediated thrombin generation, and the development of fibrinous hyaline membranes, a characteristic of the inflammatory response in ARDS [ 24 ]. The interaction between inflammation and vascular activation cumulatively induces lung damage [ 36 ]. An increase in PAR signaling has been recapitulated in multiple experimental ALI animal models [ 25 , 37 ]. The present study observed that MSCs preconditioned with thrombin, a substance indicative of coagulative status, improved lung injury in E. coli-induced infectious ARDS via an anti-inflammatory response from EVs. We postulate that the protective factors secreted through EVs in PAR-activated thrombin-preconditioned MSCs may have been the mechanism of thMSC-EVs’ improvement of tissue injuries, which is known to be mediated by PAR. However, further study is needed to determine whether its efficacy varies according to the degree of hypercoagulable status in vivo, as evidenced by the variable level of hypercoagulability markers such as fibrinogen and d-dimer.

In this study, we histologically confirmed a significant reduction in leukocyte infiltration, pulmonary edema, and alveolar wall thickening after the intratracheal administration of thMSC-EVs. Our previous report demonstrated the thrombin-activated PARs in MSCs enhanced the secretion of the angiogenic cargo angiogenin, angiopoietin-1, and vascular endothelial growth factor (VEGF) in thrombin-preconditioned MSCs compared to those in LPS-preconditioned MSCs [ 17 ]. We have confirmed a significant increase in the secretion of HGF and VEGF in thrombin-preconditioned MSCs, in which the thMSC-EVs were isolated, compared to the naïve MSCs (Supplementary Figure S2 ). HGF, VEGF, angiogenin, and angiopoietin-1 are known to reduce inflammation, endothelial cell activation, apoptosis, and fibrosis [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ], which presumably contributed to attenuating tissue injuries in the present study. Gupta et al. demonstrated the therapeutic effect of MSCs against bacterial pneumonia in a mouse model of PAR1-mutated mouse bone marrow-derived MSCs, suggesting that PAR signaling is critical for the survival and therapeutic efficacy of MSCs [ 45 ]. Our findings of reduced alveolar wall thickness, alveolar congestion, and leukocyte infiltration can be attributed to the protective cargo content of thMSC-EVs.

Upon thrombin activation, PAR-expressing epithelial, endothelial, and immune cells secrete significant levels of inflammatory cytokines and chemokines [ 22 ]. Classically activated M1 macrophages upregulate the production of pro-inflammatory cytokines, including IL-6, IL-1α, IL-1β, and TNF-α, which, under excessive secretion and accumulation, leads to increased vascular permeability and damaged alveolar epithelium and endothelium [ 2 , 22 , 26 , 46 , 47 ]. In this study, a significant reduction of pro-inflammatory cytokines IL-1β, CCL-2, and MMP-9 can be attributed to the suppression of M1 polarization, without meaningful modulation of M2 polarization, in LPS-induced RAW 264.7 cells (Fig.  4 ). Several studies have also reported a reduction of lung injury via M1 suppression, supporting the idea that macrophage polarization is a dynamic process rather than strictly dichotomous [ 48 , 49 , 50 , 51 ]. Decreased levels of the major neutrophil-recruiting factor, CCL-2, were also evident in BALF and tissue histology measurements. MMP-9, a proteinase secreted by macrophages and regulating extracellular matrix degradation, is associated with fibrosis, indicating the therapeutic efficacy of thMSC-EVs in fibrosis [ 52 , 53 ]. In vivo, thMSC-EVs significantly reduced the levels of TNF- 𝛼 , IL-6, and CCL-2 in E. coli -induced mice, with non-significant decreases in the mean of IL-1 𝛼 and IL-1β (Fig. 7 ). Statistical non-significance can be attributed to the lack of antimicrobial effects of thMSC-EVs, though not measured in this study. In our previous study using LPS-preconditioned MSCs, TLR4 signaling activation mediated immune modulation and bacterial clearance synergistically, resulting in a broader anti-inflammatory response [ 31 ]. However, thMSC-EVs administration to our previous E. coli -induced meningitis rat model study did not show bacterial clearance, similar to the present study. The stimuli-dependent modulation of cargo in MSCs-derived EVs is a highly advantageous approach for developing disease-specific therapeutics [ 18 ]. In summary, this study confirmed the protective therapeutic effect of thMSC-EVs by significantly reducing alveolar wall thickening, congestion, vascular permeability, and macrophage activation.

Challenges in the classical method of CT image analysis in small rodents include ambiguous air-surface contrast and complicated segmentation of the lung parenchymal regions because of intrathoracic structures, including the vasculature, heart, and airways [ 54 , 55 ]. Here, we present a more feasible and accurate method for quantitative CT image analysis of small rodents by manually but precisely segmenting the lungs using semi-automatically calculated aerated parenchymal volumes. The therapeutic effect of thMSC-EVs was further confirmed macroscopically using CT image analysis. Quantitative CT image analysis revealed reduced diffuse patchy regions after intratracheal thMSC-EVs administration. Thus, we present a reliable and reproducible CT image analysis method which allows the precise evaluation of lung tissue in small rodents.

Despite several recent studies, the demand for an effective treatment for ARDS/ALI remains unmet [ 56 , 57 , 58 ]. A pharmacological approach investigates the use of drugs like anticoagulants, utilizing the effective targeting of a specific signaling pathway. A study suggested that nebulized antithrombin effectively ameliorated acute lung injury by decreasing coagulation and inflammation without altering the systemic coagulation [ 59 ]. MSCs are another promising therapeutic approach for lung injury, considering their stable secretion and delivery of regenerative factors to neighboring cells via EVs [ 13 , 60 , 61 , 62 ]. WJ-MSCs are known for their higher proliferative capacity, non-tumorigenic properties, and rich secretome than those with other sources of MSCs [ 13 , 63 ]. Recently, MSCs-derived EVs have been clinically evaluated for treating ARDS. [ 64 , 65 , 66 , 67 , 68 ]. EVs are advanced stem cell therapeutics, characteristically cell-free, low in immunogenicity and tumorigenic potential, and are more feasible form as “off the shelf” therapeutics [ 69 , 70 , 71 ]. Studies on preconditioned MSCs or derived EVs in ARDS/ALI preclinical models are not frequently performed despite their significant advantage in enhancing therapeutic efficacy. Therefore, the significance of this study lies in its investigation of the therapeutic efficacy of cargo-enhanced, injury-preexposed MSCs in an ALI experimental model. Considering that the therapeutic effect of EVs depends on their cargo, strategies to modulate and enhance its cargo cannot be overemphasized as the power of EV therapeutics. In that sense, thMSC-EVs may offer a broader range of therapeutic effects than specific pathway-targeting therapeutics such as anticoagulants in ARDS, since its much-enhanced cargo and production amount secreted by the MSCs with thrombin preconditioning involve various pathways but not limited to anti-inflammation. During the isolation of thMSC-EVs using the TFF system, thrombin should have theoretically filtered out, leaving no significant leftovers to alter the study results. However for translational research, the confirmation of thrombin leftover levels will be included in future studies.

From gross to microscopic analysis, we have thoroughly evaluated the therapeutic effect of thMSC-EVs in reducing E.coli -induced acute lung injury. thMSC-EVs reduced tissue damage, pulmonary edema, inflammatory cytokine levels (CCL2, TNF-α, and IL-6), and damaged lung regions on CT image analysis, which are crucial therapeutic indicators of ARDS/ALI. There was no significant difference in body weight and mortality, which are indicators of animal well-being. However, we postulate that considering the severity of ALI-induction and intratracheal, not systemic, administration of thMSC-EVs, 3-day observation was too short to observe acute improvements in body weight and survival. A longer observation may help confirm the holistic animal improvements. Therefore, a long-term follow-up study will be needed. In conclusion, therapeutically enhanced MSC-EVs represent a novel potential therapeutic option for ARDS/ALI.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Acknowledgements

We thank Donglim Kang for EV production and Yea Jin Lee for technical assistance in animal study.

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR22C1363); by the Korean Fund for Regenerative Medicine (KFRM) grant funded by the Korean government (Ministry of Science and ICT, Ministry of Health & Welfare) (23C0119L1); and the Future Medicine 2030 Project from Samsung Medical Center (SMX1240621).

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Yuna Bang and Sein Hwang contributed equally to this work.

Authors and Affiliations

Cell and Gene Therapy Institute, Samsung Medical Center, Seoul, 06351, Republic of Korea

Yuna Bang, Sein Hwang, Young Eun Kim, Dong Kyung Sung, Misun Yang, So Yoon Ahn, Se In Sung & Yun Sil Chang

Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea

Sein Hwang, Kyeung Min Joo & Yun Sil Chang

Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea

Young Eun Kim, Misun Yang, So Yoon Ahn, Se In Sung & Yun Sil Chang

Department of Anatomy & Cell Biology, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea

Yuna Bang & Kyeung Min Joo

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Contributions

Conceptualization, Y.S.C.; methodology, Y.B., S.H. and Y.E.K.; formal analysis, Y.B., S.H. and Y.E.K.; investigation, Y.B., S.H.,Y.E.K, D.K.S., M.Y.,S.Y.A., S.I.S.,K.M.J.,Y.S.C.; writing-original draft, Y.B.,S.H.; writing-review and editing, Y.S.C.; supervision, Y.S.C.; funding acquisition, S.Y.A., and Y.S.C. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yun Sil Chang .

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Ethics approval and consent to participate.

All animal experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC, Approval number: 20230126004) of the Samsung Biomedical Research Institute, an AAALAC International (Association for Assessment and Accreditation of Laboratory Animal Care)-accredited facility, under the National Institutes of Health Guidelines for Laboratory Animal Care.

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Not applicable.

Competing interests

The funders had no role in the design of this study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. Yun Sil Chang, So Yoon Ahn, and Dong Kyung Sung declare potential conflicts of interest arising from a filed or issued patent titled “Composition for treating infectious diseases comprising exosomes derived from thrombin-treated stem cells. (10-2020-0161582) (18/036,474) (2023-528342) (202180077317.3) (21898556.2)” and “Method for promoting generation of stem cell-derived exosome by using thrombin (10-1643825) (03081223) (6343671) (09982233)” as co-inventors, not as patentees.

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Bang, Y., Hwang, S., Kim, Y.E. et al. Therapeutic efficacy of thrombin-preconditioned mesenchymal stromal cell-derived extracellular vesicles on Escherichia coli -induced acute lung injury in mice. Respir Res 25 , 303 (2024). https://doi.org/10.1186/s12931-024-02908-w

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What Is the Difference Between a Control Variable and Control Group?

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In experiments, controls are factors that you hold constant or don't expose to the condition you are testing. By creating a control, you make it possible to determine whether the variables alone are responsible for an outcome. Although control variables and the control group serve the same purpose, the terms refer to two different types of controls which are used for different kinds of experiments.

Why Experimental Controls Are Necessary

A student places a seedling in a dark closet, and the seedling dies. The student now knows what happened to the seedling, but he doesn't know why. Perhaps the seedling died from lack of light, but it might also have died because it was already sickly, or because of a chemical kept in the closet, or for any number of other reasons. 

In order to determine why the seedling died, it is necessary to compare that seedling's outcomes to another identical seedling outside the closet. If the closeted seedling died while the seedling kept in sunshine stayed alive, it's reasonable to hypothesize that darkness killed the closeted seedling. 

Even if the closeted seedling died while the seedling placed in sunshine lived, the student would still have unresolved questions about her experiment. Might there be something about the particular seedlings that caused the results she saw? For example, might one seedling have been healthier than the other to start with?

To answer all of her questions, the student might choose to put several identical seedlings in a closet and several in the sunshine. If at the end of a week, all of the closeted seedlings are dead while all of the seedlings kept in​ the sunshine are alive, it is reasonable to conclude that the darkness killed the seedlings.

Definition of a Control Variable

A control variable is any factor you control or hold constant during an experiment. A control variable is also called a controlled variable or constant variable. 

If you are studying the effect of the amount of water on seed germination, control variables might include temperature, light, and type of seed. In contrast, there may be variables you can't easily control, such as humidity, noise, vibration, and magnetic fields.

Ideally, a researcher wants to control every variable, but this isn't always possible. It's a good idea to note all recognizable variables in a lab notebook for reference.

Definition of a Control Group

A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable .

In an experiment to determine whether zinc helps people recover faster from a cold, the experimental group would be people taking zinc, while the control group would be people taking a placebo (not exposed to extra zinc, the independent variable).

A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable. Usually, controlled experiments have control groups. Sometimes a controlled experiment compares a variable against a standard.

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Intestinal microbiome profiles in broiler chickens raised with different probiotic strains.

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Silva, J.M.S.d.; Almeida, A.M.D.S.; Borsanelli, A.C.; Athayde, F.R.F.d.; Nascente, E.d.P.; Batista, J.M.M.; Gouveia, A.B.V.S.; Stringhini, J.H.; Leandro, N.S.M.; Café, M.B. Intestinal Microbiome Profiles in Broiler Chickens Raised with Different Probiotic Strains. Microorganisms 2024 , 12 , 1639. https://doi.org/10.3390/microorganisms12081639

Silva JMSd, Almeida AMDS, Borsanelli AC, Athayde FRFd, Nascente EdP, Batista JMM, Gouveia ABVS, Stringhini JH, Leandro NSM, Café MB. Intestinal Microbiome Profiles in Broiler Chickens Raised with Different Probiotic Strains. Microorganisms . 2024; 12(8):1639. https://doi.org/10.3390/microorganisms12081639

Silva, Julia Marixara Sousa da, Ana Maria De Souza Almeida, Ana Carolina Borsanelli, Flávia Regina Florencio de Athayde, Eduardo de Paula Nascente, João Marcos Monteiro Batista, Alison Batista Vieira Silva Gouveia, José Henrique Stringhini, Nadja Susana Mogyca Leandro, and Marcos Barcellos Café. 2024. "Intestinal Microbiome Profiles in Broiler Chickens Raised with Different Probiotic Strains" Microorganisms 12, no. 8: 1639. https://doi.org/10.3390/microorganisms12081639

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