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What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.
The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.
As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .
Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.
In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.
Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.
The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.
Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.
Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.
However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.
How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.
If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.
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Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.
Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?
A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.
As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.
Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.
There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.
This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.
In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.
Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.
Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.
If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.
Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.
Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.
That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.
Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.
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Statistics By Jim
Making statistics intuitive
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
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
Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:
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!
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.
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 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 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 .
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.
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.
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.
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!
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.
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.
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
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:
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.
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.
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:
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:
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:
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:
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:
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:
The four most common types of interviews are:
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 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:
But triangulation can also pose problems:
There are four main types of triangulation :
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:
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 :
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 :
Quantitative research designs can be divided into two main categories:
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:
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 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.
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.
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:
In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
Depending on your study topic, there are various other methods of controlling variables .
There are 4 main types of extraneous variables :
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:
Disadvantages:
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 .
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 :
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 :
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.
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.
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:
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 :
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:
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 .
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.
Longitudinal study | Cross-sectional study |
---|---|
observations | Observations at a in time |
Observes the multiple times | Observes (a “cross-section”) in the population |
Follows in participants over time | Provides of society at a given point |
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 .
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 :
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:
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:
When designing the experiment, you decide:
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:
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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The independent variable is the thing the researchers are testing. They are trying to determine whether it’s responsible for any change that occurs in the experiment. The research control group is key for this as it allows them to isolate the independent variable’s effect on the experiment.
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Splitting the audience you’re testing into two identical groups will give you a control group and an experimental group.
Nothing will change for the control group during the research. For example, this group would receive a placebo in pharmaceutical research.
In contrast, one key variable changes for the experimental group. In a pharmaceutical experiment, researchers might administer a different drug. In advertising research, this might involve increasing the experimental group’s exposure to ads.
When evaluating the results, researchers will compare those obtained from the experimental group against the control group. The control group is the baseline.
In research where the two groups are truly identical, seeing different results between the groups suggests they were caused by the independent variable—the only thing that changed.
Examples of control groups in research exist in a wide range of business contexts. For example:
You want to test whether a 15% loyalty discount for repeat purchases would positively impact retention and revenue. So, you send a discount email to 50% of your customers who were randomly selected. The other 50% of customers are your control group.
You want to test whether a personal sales call will increase your chance of a sales conversion. You add this step to your existing nurturing campaign for a randomly selected portion of leads. Those who don’t receive a phone call are your control group.
You want to test whether different product packaging can change brand perceptions. To do this, you change the packaging for a randomly selected portion of customers. Customers who receive the same packaging as before are your control group. Sending a survey to all customers about their brand perceptions before and after the experiment will reveal the impact of the new packaging.
These are just some of the countless examples of control groups. Perhaps the most well-known example is in the medical field, where placebos treatments are used. Control groups receive placebo treatments under the exact same conditions as the experimental group to determine the treatment’s effects.
Control groups matter in research because they act as the benchmark to establish your results’ validity . They enable you to compare the results you see in your experimental group and determine if the variable you changed caused a different outcome.
Control groups and experimental groups should be identical in their makeup and environment in every possible way. You’ll be able to draw more definitive conclusions as long as the research process is identical for both groups. In other words, working with control groups improves your research’s internal validity .
Control groups are most common in experimental research, where you’re trying to determine the impact of a variable you’re changing. You split your research group into two groups that are as identical as possible. One receives a placebo, for example, while the other receives a treatment.
In this environment, the identical makeup of the group is essential. The most common way to accomplish this is by randomly splitting the group in two and ensuring that any variables you’re not testing remain the same throughout the research process.
You can also conduct experiments with multiple control groups. For example, when testing new ad messaging, the split between two control groups and one experimental group may be as follows:
Control group 1 receives no advertising
Control group 2 receives the existing advertising
Control group 3 receives the new ad messaging
This more complex type of experiment can test both the overall impact of ads and how much of that impact you could attribute to the new messaging.
Control groups are less common in non-experimental research but can still be useful. They most commonly occur in the following process designs:
In this research process, every person in the experimental group is matched to one other person based on their environmental and demographic similarities.
This is most common when randomly selecting two groups on a broader scale would not result in them being equal. It can help you ensure that the control group or individual continues to act as the baseline for the variable you are studying.
This is where multiple groups are part of the research, but they are not randomly assigned to test and control conditions.
Quasi-experimental design is most common when the groups you are studying already exist, like customers being shown new ad messaging versus non-customers. The control group in this example is made up of your non-customers, as the variable did not change for them.
While control groups tend to be similar across research contexts, they generally fall into two categories: negative and positive control groups.
The independent variable does not change in a negative control group. This group represents the true status quo, and you would test the experimental group against it.
Examples of negative control groups include many of the experiments listed above, like only changing product packaging or only offering a discount for one group of customers.
In positive control groups, the independent variable is changed where it is already known to have an effect. You would compare this group’s results against those from the experimental group receiving a variation of the same independent variable. This would enable you to determine if the effect changes.
In the example of a multi-control group experiment seen above, control group 1 (receiving no advertising) is a negative control group, while control group 2 (receiving the current level of advertising) is a positive control group.
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How science REALLY works...
The Understanding Science site is assembling an expanded list of FAQs for the site and you can contribute. Have a question about how science works, what science is, or what it’s like to be a scientist? Send it to [email protected] !
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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 .
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 .
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 .
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 .
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 .
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 .
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 .
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.
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.
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.
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.
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?
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:
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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.
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.
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.
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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Andrew m. lane.
1 Research Centre for Sport, Physical Activity (SPARC) School of Sport, Faculty of Education, Health and Wellbeing, Walsall Campus, University of Wolverhampton, Walsall WS1 3BD, UK; [email protected]
2 School of Psychology, Canterbury Campus, University of Kent, Canterbury CT2 7NP, UK; [email protected]
Andrew p. friesen.
3 Department of Kinesiology, Berks Campus, Pennsylvania State University, Berks, PA 19610, USA; ude.usp@617fxa
The data is not yet publicly available.
Background: A large-scale online study completed by this research team found that brief psychological interventions were associated with high-intensity pleasant emotions and predicted performance. The present study extends this work using data from participants ( n = 3376) who completed all self-report data and engaged in a performance task but who did not engage with an intervention or control condition and therefore present as an opportunistic no-treatment group. Methods: 41,720 participants were selected from the process and outcome focus goals intervention groups, which were the successful interventions ( n = 30,096), active-control ( n = 3039), and no-treatment ( n = 8585). Participants completed a competitive task four times: first as practice, second to establish a baseline, third following an opportunity to complete a brief psychological skills intervention, and lastly following an opportunity to repeat the intervention. Repeated measures MANOVA indicated that over four performance rounds, the intensity of positive emotions increased, performance improved, and the amount of effort participants exerted increased; however, these increases were significantly smaller in the no-treatment group. Conclusions: Findings suggest that not engaging in active training conditions had negative effects. We suggest that these findings have implications for the development and deployment of online interventions.
The effectiveness of psychological skills such as imagery, goal-setting, and self-talk has been demonstrated in many areas of application [ 1 ], including sport [ 2 ], surgery [ 3 ], and computer gaming [ 4 ]. A recent large-scale study of 44,742 participants found support for the utility of following brief online active psychological skills training to aid emotion regulation and improve performance in a competitive task [ 5 ]. The aforementioned study [ 5 ] tested the effects of three psychological skills: (a) imagery, (b) self-talk, and (c) if–then planning, with each skill directed to one of four different foci: (a) outcome goal, (b) process goal, (c) instruction, or (d) arousal control, resulting in 12 different techniques. A 13th group labelled as a control group received a repetition of instructions on how to perform the task from Olympic gold-medallist Michael Johnson. The argument for labelling these participants as a control group was that they received no active training. They [ 5 ] compared the extent to which performance in the 12 intervention conditions improved over four rounds against the control group. The results illustrated the benefits of engaging in active psychological skills training, and the control group significantly improved also. Interestingly, the control group showed greater improvement in performance, felt more energetic, and exerted more mental effort than participants following instructional interventions.
A key aspect of this study [ 5 ] was the method used to produce the active control group which is used to form the case for the present study. In their study, participants were informed that they would learn about sport psychology and receive personalized feedback from Michael Johnson. Specifically, control group participants were informed, “You have played the game now. You have to find the numbers and finding them can be challenging. It’s a different grid but the challenges will be similar. Spend some time getting mentally ready. Give yourself about 90 seconds to prepare before you start the next round”. Although not receiving specific instructions, the control group received encouragement to perform again from former Olympian Michael Johnson, and encouragement is motivational [ 6 ].
A control group should seek to control the positive beliefs of using the intervention, a point that drives blind and double-blind placebo groups. The control condition should elicit some of the symptoms of the intervention but not those that are in the active treatment (e.g., decaffeinated coffee, a treatment that tastes like coffee, and so could have the active ingredient, but actually does not). In sport psychology interventions these typically involve active training, and as such it is difficult to have a traditional control group.
The present study extends this work [ 5 ] using previously unreported data from the same experiment. The investigators [ 5 ] found many participants engaged in all the performance tests but did not engage with the interventions. These unused data represent a novel condition and offers opportunistic no-treatment control data against the active control and active-training groups used in the previous study [ 5 ]. We hypothesized that the “no-treatment” group would perform significantly worse than the “active-training” and “active-control” groups reported previously [ 5 ].
2.1. participants.
Participants were 74,204 volunteers who provided informed consent and were recruited to the study via the British Broadcasting Corporation (BBC) Lab UK ( M age = 34.66 years, SD = 14.13). The project was advertised on national television and radio as an online experiment investigating performing under pressure. Participants originated from 103 different countries covering all continents. In the present study, we selected 41,720 ( M age = 34.34, SD = 13.93) participants from the process and outcome focus goals interventions, which were the successful interventions ( n = 30,096; M age = 34.64, SD = 14.07), active-control ( n = 3039, M age = 31.50, SD = 13.41), and no-treatment ( n = 8585, M age = 34.35, SD = 13.93).
The study uses the same measures reported previously [ 5 ] and so these are described only briefly here.
The items to measure, “Happy”, “Anxious”, “Dejected”, “Angry”, and “Excited”, were used from the same-named factors in the Sport Emotion Questionnaire (SEQ) [ 7 ] and two items “Fatigued” and “Energetic” were included to reflect arousal [ 8 ]. Each item was rated on a 7-point Likert scale (1 = not at all to 7 = extremely) . A single measure of emotion was used so that a high score was indicative of pleasant emotion. Alpha coefficients for emotion at each completion were: Baseline α = 0.72, Round 1 α = 0.70, Round 2 α = 0.68, and Round 3 α = 0.70.
A cognitive task was developed to allow the capture of a large dataset via an online method. The concentration grid task required participants to find and click on numbers in sequence from 1 to 36 as quickly as possible from a 6 × 6 grid. Numbers were presented in a randomised order within the grid, and as such participants had to concentrate and scan the grid to locate and click on the correct number. Participants completed a practice round, where participants performed alone and not against a competitor. Based on practice round results, an artificial computer opponent was introduced to create a sense of competition. The computer opponent was matched against the participant’s grid completion time from the practice round. The participant’s performance was measured by the number of seconds required to complete the grid.
The Rating Scale of Mental Effort [ 9 ] is a single item scale that was used to assess mental effort (0 = no effort to 150 = complete effort).
Data were collected online via the BBC Lab UK website. Participants completed informed consent forms before proceeding to the start of the online experiment. Videos guided participants through the completion of self-report scales and the concentration task. The online programme was narrated by Michael Johnson. Random allocation to experimental treatment groups was completed automatically by an online programme based on demographic data provided by participants. All participants completed the concentration game task before group allocation to provide a baseline measure of performance that could be used to assess whether the groups had pre-existing differences. Participants then rated their mental effort immediately following performance.
An opportunistic no-treatment group ( n = 8595) emerged which consisted of participants who chose not to view the allocated intervention or encouragement video (i.e., active-control group). Instead, these participants immediately proceeded to a second completion of the concentration grid. Further, this was their decision. Therefore, considerations that arise when positive treatment is denied to a subsection of the sample in a randomised control design are not applicable. This no-treatment group is closer to a traditional control group. However, a key difference is that participants were not randomly allocated.
A repeated measures multivariate analysis of variance examining emotions, effort exerted and performance over the 4 rounds of practice, baseline, the implementation of the intervention, and finally, a repeat of the same intervention. The rationale for the data analysis strategy was to run as few tests as possible. With such a large sample size, it is easy to show significant results even though the size of the effect was low. In the present study, the focus is on significant interaction effects as they show that changes in data vary between groups.
Repeated MANOVA results revealed a significant intervention effect (Wilks lambda 18,83522 = 0.98, p < 0.0001, partial eta 2 = 0.10), a main effect for changes over time (Wilks lambda 9,41769 = 0.74, p < 0.0001, partial eta 2 = 0.26) and a main effect for active-training, active-control, and no-treatment (Wilks lambda 18,83522 = 0.98, p < 0.0001, partial eta 2 = 0.11).
Univariate results indicated that emotions became significantly more positive ( F 6,125304 = 1328.56, p < 0.0001, partial eta 2 = 0.03) following the completion of the intervention (see Figure 1 ). The pattern of significant differences showed that the active-training group benefited the most, followed by the active-control group, with the least benefits being found for the no-treatment group. Weaker significant interaction effects were found for effort invested in performance over rounds of competition ( F 6,125304 = 12.46, p < 0.0001, partial eta 2 = 0.01, see Figure 2 ) and for improvements in performance ( F 6,125304 = 6.42 p < 0.0001, partial eta 2 = 0.001, see Figure 3 ).
Emotion by Competition Rounds, by Active-Training, Active-Control, and No-Treatment.
Effort by Competition Round, by Active-Training, Active-control, and No-Treatment.
Performance by Competition Round, by Active-Training, Active-Control, and No-Treatment.
Results show that there were main effects for time ( F 3,120856 = 1328.56, p = <0.001, partial eta 2 = 0.007) with emotions became significantly more positive ( F 3,1328.56 = 2132.93, p < 0.0001, partial eta 2 = 0.49), performance improving ( F 3,120856 = 1249.97, p = <0.001, partial eta 2 = 0.007) and effort invested ( F 3,120856 = 4836.33, p = <0.001, partial eta 2 = 0.104).
The present study examined the effects of brief online interventions in comparison to a no-treatment group [ 5 ]. The large sample of data in the active-training and active-control groups offer a good opportunity to conduct a comprehensive examination. A total of 8585 participants did not complete the interventions but engaged fully in other parts of the experiment and therefore represent what typically looks like a non-treatment group. The key difference between these participants and a traditional control group was they were not randomly allocated. Participants in the no-treatment group showed performance improvement from baseline, increased intensity of positive emotions, and increased effort. This result is interpreted as suggesting participants in the no-treatment group were motivated to improve performance, and therefore resemble participants who sign up with a desire to improve performance. However, the rate of change between rounds was slower for the no-treatment group than the active-training and active-control groups, suggesting the active part of either control or training was influential. Compliance with participation protocols is a key factor when examining the effectiveness of interventions. In research, participants who do not comply with protocols is an issue. In real world settings, poor participant compliance minimizes the effectiveness of treatments ranging from COVID-19 vaccines to physical and mental health interventions.
Results demonstrated that the no-treatment group performed significantly worse, made less progress, and reported less optimal psychological states than the active-control and active-treatment groups. These results are not entirely unexpected but exploring possible reasons why they occurred and learning from using online interventions where naturally occurring no-treatment groups could emerge could have useful implications for future work. Positive benefits from participants receiving treatment could be explained by enhanced beliefs that the treatment would work, an effect normally described as a placebo effect. Controlling beliefs is typically achieved by using a blind placebo approach. However, this is not possible where an intervention requires the person to act consciously on information provided. A blind placebo arguably works much better in studies such as caffeine where people still participate in the treatment, believing they are taking the caffeine, but the active ingredients are removed. In such research, great care is made to make a placebo look like an authentic treatment. In a sport psychology intervention where a practitioner teaches the use of psychological skills, there is a requirement for the participant to be active in the process. An active-control group [ 5 ] is one in which basic instructions are repeated and so attempts to control for belief effects. The present study which used a no-treatment group offers the opportunity to compare the effects of active treatments against no-treatment. A no-treatment group resembles what happens in real life when people wish to pick up a skill and learn by trial and error and without specific guidance.
The active-control group benefited from participation in the study more than the no-treatment group. Receiving a message from an inspirational figure such as Michael Johnson and expecting personalised feedback can be argued to provide encouragement, which is motivating [ 6 ]. Whilst encouragement is a simple technique to use as an intervention, it is possible that the effectiveness of it in this context derives from it being delivered by a highly influential figure in sport. This raises the issue of the relative influence of the person who delivers the intervention as an active ingredient. Models of social influence from social psychology have highlighted the importance of the perceived status of the influencer [ 10 ]; however, this issue is under-examined within the context of conducting psychology research. We suggest future research compares the effectiveness of encouragement when the same message is given by different people, with a hypothesis that the more credible the persuader, the more influential it would be.
The current study can also inform future research that uses online methods to investigate psychology interventions. Online data collection that allows people to volitionally skip through the intervention creates naturally occurring control conditions where participants do not expect the intervention to work. In the present study, the no-treatment group was an opportunistic group that emerged once data had been collected and so differed from a traditional control group for whom the condition was randomised.
The present study offers a valuable contribution to knowledge in this area. Results show the value of online research which offers scalability and via data capture processes can facilitate the examination of points of engagement with the task and intervention being delivered. However, we recognise at least two limitations. The first limitation is that we did not obtain feedback from participants that measured any learning effects having completed the intervention. That is, we did not check to see if the knowledge gained was internalised before starting the task. The second limitation is that participants were not randomly allocated into the no-treatment group. We should not see engagement and disengagement as dichotomous concepts and appreciate that the intensity with which people engage with the intervention will vary. A limitation with online studies is that the conditions in which a person learns an intervention and takes a test has many unknown features. We suggest that future research focus on the learning process in terms of what intervention content is retained.
In conclusion, the BBC Lab UK data show the benefits of capturing all keyboard data. The present study used data that were not used previously [ 5 ]. On initial analysis these data were seen as incomplete; however, this shows the benefits of reflecting on what insights such data might provide. We encourage researchers to focus on using entire datasets to interrogate the issue of compliance in completing interventions, and to investigate why non-compliance occurs.
Conceptualization, A.M.L. and T.J.D.; data collection, A.M.L. and T.J.D.; writing—original draft preparation, A.M.L., C.J.B., T.J.D. and A.P.F.; writing—review and editing, A.M.L., C.J.B., T.J.D. and A.P.F. All authors have read and agreed to the published version of the manuscript.
This Research received no funding.
The study was approved by the Ethics Committee of the University of Wolverhampton (15.02.12).
Informed consent was obtained from all participants involved in the study.
Conflicts of interest.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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1.4.4 - control and placebo groups.
A control group is an experimental condition that does not receive the actual treatment and may serve as a baseline. A control group may receive a placebo or they may receive no treatment at all. A placebo is something that appears to the participants to be an active treatment, but does not actually contain the active treatment. For example, a placebo pill is a sugar pill that participants may take not knowing that it does not contain any active medicine. This can lead to a psychological phenomena called the placebo effect which occurs when participants who are given a placebo treatment experience a change even though they are not receiving any active treatment. Researchers use placebos in the control group to determine if any differences between groups are due to the active medicine or the participants' perceptions (the placebo effect).
Researchers want to know if adults who consume a drink that is high in vitamin B-12 have increased energy. They obtain a representative sample of adults. All participants are given a drink that they are told to consume every morning. They are not told what is in the drink. Half are given a drink that is high in vitamin B-12 while the other half are given a drink that tastes the same but contains no vitamin B-12.
The participants who received the drink with no vitamin B-12 are the placebo group . The purpose of the placebo group in this study is to make the two groups equivalent except for the presence of the vitamin B-12. By comparing these two groups, the researchers will be able to determine what impact the vitamin B-12 had on the response variable. We could also say that this served as a control group because this group did not receive any active ingredients.
BMC Sports Science, Medicine and Rehabilitation volume 16 , Article number: 170 ( 2024 ) Cite this article
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Badminton, a dynamic sport, demands players to display exceptional physical attributes such as agility, core stability, and reaction time. Backward walking training on a treadmill has garnered attention for its potential to enhance physical attributes and optimize performance in athletes while minimizing the risk of injuries.
By investigating the efficacy of this novel approach, we aim to provide valuable insights to optimize training regimens and contribute to the advancement of sports science in badminton.
Sixty-four participants were randomized into a control group ( n = 32) and an experimental group ( n = 32). The control group received routine exercise training, while the experimental group received routine exercise training along with additional backward walking training on the treadmill. Pre- and post-intervention measurements were taken for core stability using the Plank test, balance using the Star Excursion Balance test, reaction time using the 6-point footwork test, and agility using the Illinois Agility test.
The results showed that the experimental group demonstrated significant improvements in core stability ( p < 0.001), balance ( p < 0.001), reaction time ( p < 0.05), and agility ( p < 0.001) compared to the control group. The backward walking training proved to be effective in enhancing these physical attributes in badminton players.
Incorporating backward walking exercises into the training regimen of badminton players may contribute to their overall performance.
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Badminton is a popular and widely practiced racket sport that has gained immense popularity worldwide [ 1 ]. It is a fast-paced and highly dynamic game, requiring players to demonstrate a combination of technical skills, physical fitness, and mental acuity [ 2 ]. With its roots in ancient China and India, badminton has grown to become the national sport of several Asian countries, with a strong presence in both professional and recreational circles [ 3 ]. In the past few decades, badminton has gained global recognition as one of the fastest racket sports, attracting a diverse and ever-growing community of enthusiasts.
For winning the games and performing better the players must utilize a wide range of shot variations, including smashes, clears, drops, and drives, to outmaneuver their opponents and secure a competitive advantage [ 4 ]. The game’s rapid and dynamic nature necessitates a high level of physical fitness, making strength, endurance, power, reaction time, agility, speed, adaptability, balance, and coordination essential attributes for successful players [ 5 ]. To excel in badminton, athletes need to combine sound technical skills with a strong physical foundation to execute precise and powerful shots while maintaining fluid movement across the court [ 6 ].
Agility is a paramount attribute in badminton that significantly affects a player’s overall performance on the court [ 7 ]. Exceptional agility allows players to cover the court more efficiently, reach the shuttlecock in time, and execute shots accurately from various positions. In the dynamic and fast-paced nature of badminton, players must execute rapid body movements with precision and speed, making agility a critical factor in maintaining a competitive edge [ 8 ].
Reaction time is an essential characteristic for badminton players, as it has a significant impact on their ability to respond quickly and effectively to the dynamic and fast-paced nature of the game. Rapid shot return, anticipation, defensive skills, net play, drop shots, net kills, rally control, footwork and court coverage, deception and strategy, competitive edge, and mental agility are indispensable [ 9 ]. On the badminton court, training and enhancing reaction time through specific maneuvers and exercises can enhance a player’s performance and contribute to their success [ 10 , 11 ].
Another critical aspect of a badminton player’s physical preparation is core strength and stability [ 12 ]. Core muscles play a crucial role in stabilizing the spine, transferring force between the upper and lower extremities, and controlling the body’s center of gravity. They facilitate fluid movement and efficient power transfer during various game actions, such as lunges, jumps, and swings [ 13 ]. Core strength training has been extensively utilized not only to prevent lower back and lower limb injuries but also to optimize player performance in badminton and other sports [ 14 ].
Furthermore, posture and balance are key factors contributing to a player’s performance on the court. Maintaining proper body control and posture during rapid and complex movements is essential to execute shots accurately and efficiently [ 15 ]. The ability to control joint movement and position dynamically is crucial for swift changes in direction, evasive maneuvers, and quick responses to opponent shots. Badminton players with superior agility and balance tend to outperform their peers and are less prone to injuries resulting from incorrect footwork or unstable landing postures [ 16 , 17 ].
Backward walking, also known as retro walking, has gained popularity as an easy, cost-effective exercise that promotes health and quality of life. In the context of rehabilitation, backward walking training on treadmill has shown promising results in improving muscle action and lower extremity strength through increased motor unit recruitment, benefiting lower limb muscles [ 18 , 19 ]. Additionally, it has demonstrated positive effects on foot posture and alignment in long-distance runners. Moreover, backward walking has been associated with improvements in body balance and stability in adolescents [ 20 ]. Backward walking training has been widely utilized in various sports and has demonstrated its effectiveness in improving balance, stability, agility, coordination, and footwork skills. It has been particularly valuable in sports that require rapid changes of direction [ 21 , 22 ].
In this context, the present study aims to investigate the efficiency of backward walking training on treadmill on core stability, balance, agility, and reaction time in badminton players. While core strength training has been widely explored, the potential impact of backward walking on these specific aspects of physical performance remains relatively unexplored. Badminton involves quick, explosive movements and shuttlecock tracking which require exceptional lower limb strength, balance, and fast reaction times. Backward walking training is hypothesized to particularly enhance these abilities by improving proprioception and muscular coordination in ways that are directly translatable to badminton’s rapid on-court movements.
Understanding the benefits of backward walking on trunk stability, balance, agility, and reaction time can inform coaches and athletes on the optimal integration of this training approach to enhance performance and reduce the risk of injuries. By combining the sport’s rich history and global significance with cutting-edge research, this study endeavors to elevate the standard of badminton training and contribute to the development of well-rounded and resilient athletes.
Study design.
The study design was a two-tailed experimental study. This type of study design was commonly used in scientific research to explore relationships and causality between variables. In this design, two groups were compared, and the hypothesis was formulated as a two-tailed (non-directional) hypothesis. The sampling method employed for participant selection was convenience sampling.
The participants were selected based on their easy accessibility and availability to the researchers. The study included participants who were badminton players performing at the district level and above, and who had been actively practicing badminton for a period of more than 6 months in two badminton academies in Delhi NCR. The study focused specifically on participants of the both the genders within the age group of 18 to 26 years. Participants with recent knee and ankle injuries, recent fractures, or those currently on medication or supplements to improve performance were not included in the research. Additionally, individuals with neurological conditions were also excluded to ensure that the study sample comprised individuals without such conditions, thereby maintaining a more homogeneous group for analysis.
This study received ethical approval from the Ethical Committee of the Department of Physiotherapy, Faculty of Allied Health Sciences, Manav Rachna International Institute of Research and Studies. The approval reference number is MRIIRS/FAHS/PT/2022-23/S-008 dated 7th January 2023. The study design adhered to the guidelines outlined in the revised Helsinki Declaration of Biomedical Ethics, ensuring the ethical treatment of participants and the protection of their rights. Additionally, to ensure transparency and accountability, the study protocol was registered in the clinical trial registry at https://www.ctri.nic.in/ with the identifier CTRI/2023/05/052750. The registration date was 17th May 2023.
G*Power (version 3.1.9.2, Heinrich Heine-University, Düsseldorf, Germany) was used to calculate the sample size. An a priori power analysis using t-test to compare differences between two independent means, with a desired statistical power of 80%, a significance level of 5%, and an effect size of 0.72 resulted in a sample size of 64. The effect size was derived from a previous study [ 23 ], where the mean of the outcome variable “dynamic balance following backward walking” was used.
In this study, 64 participants voluntarily took part after receiving detailed explanations and providing informed consent. The participants were divided into two groups: the control group and the experimental group, based on their eligibility determined by inclusion and exclusion criteria. The control group involved individuals undergoing routine exercise training, while the experimental group received routine exercise training combined with backward walking training. The outcome measures like core stability, balance, reaction time and agility were assessed at both pre- and post-training. To ensure unbiased results, the participants were blinded to their group assignment, while the outcomes assessor remained aware of the groupings for accurate evaluation. The randomization procedure was carried out using a double-blinded trial methodology. This rigorous methodology helps to minimize potential biases and enhances the validity and reliability of the research findings. This study conforms to the Consolidated Standards of Reporting Trials (CONSORT) guidelines for reporting randomized controlled trials. We have included a completed CONSORT checklist as an additional file to provide a comprehensive overview of our trial’s design, analysis, and interpretation. Furthermore, a CONSORT flow diagram (Fig. 1 .) depicts the study procedures, including enrollment, randomization, pre-assessment, intervention, post-assessment, and data analysis.
Prior to the initiation of the actual study, all participants underwent two familiarization sessions to ensure they were adequately prepared and understood the tests involved in the study. During these sessions, participants were introduced to the equipment and detailed procedures for each test, which included the Plank test, Star Excursion Balance Test (SEBT), 6-point footwork test, and Illinois Agility Test. Each participant had the opportunity to practice under supervision, which helped standardize the test administration and ensure accurate, reliable results. These sessions were not included as the part of intervention and baseline data was collected after these sessions only.
A CONSORT flow diagram is depicting the study procedures
The Illinois Agility Test was utilized to assess the agility of badminton players. The methodology was adopted in a previous study [ 24 ]. This widely recognized test involves positioning 8 cones in a specific pattern on a flat surface to create a zigzag course. The badminton players were instructed to navigate through the course, executing rapid and accurate directional changes. The test’s validity and reliability were established in the study, making it an effective tool for evaluating agility among athletes.
To assess core stability and strength, participants underwent the plank test [ 25 ]. Detailed instructions were provided to each participant before the test. The plank test required participants to assume a prone lying position with their elbows supported on the ground, lifting their bodies while keeping their hands pronated and parallel to the floor. Participants were instructed to maintain a straight bodyline off the ground, with their ankles in a neutral position, supported on their toes. A neutral head position, facing the ground, was also emphasized during the test. The stopwatch was started as soon as the subject assumed the correct plank position. Each participant’s performance was then measured continuously, recording the time they were able to maintain the plank position until they reached their limit or experienced loss of balance. This process was repeated three times for each participant, and the average of the three readings was used for analysis.
The reaction time of the badminton players was assessed using the randomized six-point footwork drill as describe previously [ 11 ]. Results of the reliability analysis indicated the visual reaction system using the stopwatch had excellent Intraclass Correlation Coefficient (ICC) for both tests (ICC = 0.95).This drill was conducted on the badminton court, with six cones strategically placed at different locations, including the forehand front corner, backhand front corner, forehand side, backhand side, forehand backcourt corner, and backhand backcourt corner. The purpose of this training exercise was to enhance the players’ agility, speed, and footwork by replicating real-game scenarios that require quick reactions and precise foot movements. The players were instructed to move rapidly between these designated points in a random order, simulating the unpredictability of actual game situations. To objectively measure their performance, a stopwatch was used to record the time taken by each player to complete the drill. Each participant performed three repetitions of the test with a resting time of 5 min after every repetition to ensure the best performance every time. The reaction times were recorded for each trial, with data being noted for the best (maximum) times achieved across the trials.
The study utilized the Star Excursion Balance Test (SEBT) as a clinical tool to evaluate dynamic balance and postural control in participants [ 26 ]. The test involved creating a star-like pattern on the floor using tape, with eight distinct directions marked: anterior, anteromedial, anterolateral, medial, lateral, posterior, posteromedial, and posterolateral. Before commencing the test, participants received clear instructions and a detailed explanation of the procedure. They were asked to stand in a single-leg stance, with the tested limb placed at the center of the star pattern. During the test, participants lifted their non-tested leg and reached as far as possible along each marked direction, maintaining balance throughout each reach and returning to the starting position after each trial. Three trials were conducted for each direction, and the average reach distance achieved was recorded. To account for individual variations in leg length, the reach distance for each direction was normalized by dividing it by the participant’s limb length. The utilization of normalized units allowed for standardized measurements of balance performance, ensuring meaningful and comparable assessments across participants [ 16 ]. The SEBT was performed in a clockwise direction to maintain consistency in the testing procedure.
Routine training.
Participants in the control group received routine training, which consisted of three sessions per week for six weeks. The training program included dynamic warm-ups with activities such as jogging, leg swings, and arm circles to prepare the body for more strenuous activities and prevent injuries. Strength training focused on building muscle strength and endurance through exercises like squats, lunges, push-ups, and planks. Agility drills involved ladder drills and cone drills to improve quick directional changes and overall agility. Core stability exercises such as the Russian twist, bird-dog, and bridge were incorporated to strengthen core muscles, vital for balance and efficient movement patterns. Endurance training was performed through longer duration, moderate-intensity cardiovascular activities like running or cycling. Each session concluded with a cool-down phase involving static stretching targeting all major muscle groups to aid in recovery and decrease muscle stiffness. The intensity and repetitions of these exercises were individually adjusted based on each athlete’s Perceived Rate of Exertion (PRE), ensuring the training was challenging yet manageable, optimizing the training program’s effectiveness tailored to individual fitness levels and recovery needs.
Participants in the experimental group were instructed about the training regimen, which incorporates a ball hanging in front of the treadmill to encourage the participants to maintain a forward-facing gaze during the exercise. The training session began with a 4-minute session of forward walking on the treadmill, followed by a 1-minute rest period. After the rest, the participants switch to backward walking on the treadmill for another 4-minute session, followed by another 1-minute rest period. This sequence was repeated for a total of 12 min of exercise. The training protocol was scheduled to be performed three times a week, continuously for a duration of 6 weeks [ 27 ]. Throughout the training period, participants maintain a constant walking speed of 3 km/hr. Backward training regimen aimed to enhance participant’s walking skills and proprioception, promoting balance and coordination during backward movement.
The statistical analysis was performed using SPSS (version 24.0, IBM Corp., Armonk, NY, USA). Descriptive statistics, including mean and standard deviation (SD), were calculated to summarize the characteristics of the study variables. The normal distribution of the data was assessed by, the Shapiro-Wilk test. To calculate within-group comparisons, paired t-tests was used to examine the differences between pre and post-intervention measurements for trunk stability, balance, reaction time, and agility. Independent t-tests was used to compare the control and experimental groups at the baseline. To see the effects of the intervention over time, repeated measures analysis of variance (ANOVA) was used, considering the factors of time (pre and post) and group (control and experimental). The significance level was set at p < 0.05 for all statistical tests in the thesis.
The study was conducted on 64 badminton players divided equally in control and experimental groups. The control group consisted of a higher proportion of male participants compared to females, while a similar pattern was observed in the experimental group. The average age of participants in the control group was slightly higher than that of the experimental group. Heights were comparable in both groups, with the experimental group showing a slightly higher average. Average weight was similar in both groups, and the control group had a slightly higher BMI compared to the experimental group. Right-hand dominance was prevalent in most participants in both the control and experimental groups. Specifically, in the control group, a larger percentage of participants were right-handed, whereas the experimental group also had a higher number of right-handed participants. In both the control and experimental groups, a higher percentage of male participants had more than 5 years of badminton experience compared to females (Table 1 ).
Upon reviewing the participant characteristics presented in the provided data, it is clear that conducting a gender-based study for the comparison of outcome variables may not be feasible due to the limited number of female participants in both the control and experimental groups. Additionally, when comparing hand dominances, it is apparent that the majority of participants in both groups were right-hand dominant. As a result, we did not plan to present the results based on gender and hand dominance in the study, as the sample sizes for these subgroups were not sufficient for meaningful statistical comparisons. Instead, our primary focus was on comparing the outcome variables between the control and experimental groups and evaluating the impact of the intervention on the specified measures.
Table 2 presents the results of the independent t-test were used to assess differences between two independent groups at each time point—pre and post-intervention for agility, core stability, reaction time and balance. For the agility test, there was a significant improvement in the experimental group from pre (17.08 ± 0.43) to post (15.32 ± 0.34) with a mean difference of 1.75 (t = 21.28, p < 0.001, 95% CI [1.58, 1.92]), whereas the control group showed no significant difference (t=-0.13, p = 0.89, 95% CI [-1.09, 0.95]). Similarly, for the core stability, the experimental group showed a significant improvement from pre (3.44 ± 0.41) to post (5.39 ± 0.42) with a mean difference of -1.94 (t=-18.51, p < 0.001, 95% CI [-2.16, -1.73]), while the control group had no significant change (t=-0.98, p = 0.32, 95% CI [-0.26, 0.08]). Finally, for reaction time, the experimental group demonstrated a significant improvement from pre (17.93 ± 1.03) to post (15.02 ± 0.41) with a mean difference of 2.91 (t = 14.09, p < 0.001, 95% CI [2.49, 3.33]), while the control group had no significant difference (t = 0.05, p = 0.95, 95% CI [-0.51, 0.54]).
The independent t-test results for the SEBT measurements between pre and post-intervention showed significant improvements in the experimental group for various reach directions (Table 3 ). Notably, the experimental group displayed significant enhancements in anterior reach for both the right (MD= -6.87, p < 0.001) and left legs (MD= -8.21, p < 0.001), anterolateral reach for the right leg (MD = 10.40, p < 0.001), lateral reach for the right leg (MD = 9.46, p < 0.001), posterolateral reach for both the right (MD = 9.37, p < 0.001) and left legs (MD= -8.87, p < 0.001), and posteromedial reach for the right leg (MD = 9.18, p < 0.001). In contrast, the control group had no significant changes in most reach directions. However, both groups showed significant improvements in posterior reach for both legs.
Paired t-tests was conducted to compare the pre and post-intervention measurement within each group for the agility, core stability, reaction time and balance (Fig. 2 ). For the agility, the experimental group showed a significant improvement from pre (17.08 ± 0.43) to post (15.32 ± 0.34) with a mean difference of 1.75 (t = 21.28, p < 0.001, 95% CI [1.58, 1.92]), whereas the control group had no significant change (t = 0.88, p = 0.38, 95% CI [-0.62, 1.57]). Further, the core stability in the experimental group demonstrated a significant improvement from pre (3.44 ± 0.41) to post (5.39 ± 0.42) with a mean difference of -1.94 (t= -18.51, p < 0.001, 95% CI [-2.16, -1.73]), while the control group had no significant change (t= -0.23, p = 0.88, 95% CI [-0.40, -0.06]). Similarly, for the reaction time, the experimental group showed a significant improvement from pre (17.93 ± 1.03) to post (15.02 ± 0.41) with a mean difference of 2.91 (t = 14.09, p < 0.001, 95% CI [2.49, 3.33]), while the control group had no significant difference (t = 0.10, p = 0.92, 95% CI [-0.51, 0.54]). As a whole, the experimental group showcased significant enhancements in balance, exemplified by marked improvements across diverse reach directions. In contrast, the control group exhibited minimal alterations in SEBT performance, underscoring the distinct disparity between the two groups. (Table 4 ).
The repeated measures ANOVA was conducted to assess changes in performance measures (agility test, core stability, and the reaction time) over time within each group (Table 5 ). For the agility, there was a significant time effect (F = 16.87, p < 0.001, η² p = 0.21), indicating that performance improved from pre to post within both the experimental and control groups. However, the group effect (F = 5.03, p = 0.03, η² p = 0.08) and time x group interaction (F = 5.57, p = 0.02, η² p = 0.08) were not significant, suggesting that the improvement in performance did not differ significantly between the two groups. For the core stability and reaction time, there were significant time effects (core stability: F = 262.06, p < 0.001, η² p = 0.81; reaction time: F = 199.77, p < 0.001, η² p = 0.76), indicating performance improvements from pre to post within both groups. Additionally, significant group effects (core stability: F = 220.04, p < 0.001, η² p = 0.78; reaction time: F = 49.07, p < 0.001, η² p = 0.44) and time x group interactions (core stability: F = 161.15, p < 0.001, η² p = 0.72; reaction time: F = 171.9, p < 0.001, η² p = 0.73) were found for both core stability and reaction time, suggesting that the improvement in performance differed significantly between the experimental and control groups.
Pre and Post comparison of illinois agility test, plank test, and 6-point forward test between control and experimental group
The aim of the study was to investigate the efficiency of backward walking on agility, core stability, reaction time, and balance in badminton players. To assess these variables, the researchers employed specific outcome measures, including the Illinois agility test for agility, Plank test for core stability, the 6-point footwork test for reaction time, and the Star Excursion Balance Test (SEBT) for balance in the badminton players. The study included a total of 64 participants, with 32 individuals in each group (control and experimental).
Badminton is physically demanding, requiring athletes to possess high levels of aerobic and anaerobic fitness [ 28 ]. The ability to swiftly change direction, accelerate, and decelerate is essential for reaching the shuttlecock and maintaining court coverage effectively. The aerodynamics of a shuttlecock play a crucial role in badminton. Researchers investigate the factors influencing shuttlecock trajectory, spin, and speed, taking into account factors such as air resistance, drag, and shuttlecock design [ 29 ]. This knowledge helps players anticipate and react to shots more effectively. Backward walking training on treadmill offers a unique and innovative approach to enhancing the physical performance of athletes. By incorporating such exercises into their training regimen, players can improve their agility, balance, and proprioception, which are crucial attributes in badminton. By adding backward training to their training routines, badminton players can enhance their physical attributes, ultimately contributing to improved performance and reduced injury risk during competitive play [ 30 ].
In the present study, the control group received routine exercise training focusing on improving sports performance, whereas the experimental group received routine exercise training along with backward walking training. Pre and post-intervention assessment were taken to measure core stability using plank test, balance using the SEBT, reaction time using the 6-point footwork test and agility using the Illinois agility test. The experimental group demonstrated significant improvement in core stability, balance, reaction time, and agility as compared to the group following only regular exercise protocol.
There were significant difference in stability between the control and experimental groups. The improved core strength enhances dynamic balance, and agility in adolescent badminton players [ 14 ]. The six weeks of backward walking training leads to enhanced core strength, as evidenced by the outcomes of the plank test. This improvement in core strength could potentially contribute to the observed enhancements in agility and balance. These findings align with findings of previous studies which demonstrated that backward walking has the potential to enhance balance and stability among badminton players [ 31 ]. Notably, their study revealed the most significant improvements within the short-term duration of 4 weeks of training.
Backward training targets specific muscle groups involved in maintaining stability and generating power during quick movements on the court, such as the quadriceps, hamstrings, and calf muscles. Strengthening these muscles through backward training can help prevent injuries and improve overall lower body strength and stability. Additionally, backward training challenges players’ motor skills by requiring them to perform movements in reverse, leading to increased motor unit recruitment and improved coordination [ 32 ]. The focus on core stability during backward training can also benefit badminton players in maintaining a strong and balanced stance while executing shots and moving swiftly on the court [ 16 ].
Further, the control group did not show a significant difference in agility, whereas the experimental group of backward training exhibited a significant improvement in agility. These findings suggest that incorporating backward walking training can be effective in enhancing agility. Studies in the past shows that repeated backward running training (RBRT) can have positive effects on various measures of physical fitness in youth male soccer players and netball players [ 17 , 21 , 22 ]. Within-group analysis revealed that RBRT improved all performance variables, including speed, agility, power and other physical fitness measures.
In this study, there was no significant difference in the control group of the six-point footwork test, whereas there was a significant difference in the experimental group that underwent backward training. The backward training helped improve backward running when the shuttle was behind and helped maintain balance with control. A previous study reported discovered that a twelve-week intervention focused on agility training, utilizing the Visual Reaction Time technique with a foundation in six-point footwork and T-footwork, yielded significant differences in the recorded reaction and action times for the fixed-light-mode six-point footwork test [ 11 ]. Additional research has corroborated the notion that engaging in recurrent backward running exercises can enhance diverse aspects of physical fitness among adolescent male football players and netball players. These improvements encompass enhanced speed, agility, power, and other pertinent physical fitness indicators. The inclusion of backward training in conditioning and skills training regimens has the potential to yield positive outcomes in terms of improving physical fitness among adolescent male football and netball athletes [ 21 , 22 ].
The improved balance improves footwork performance in adolescent competitive badminton players also the visual reaction training improves the six-point footwork [ 15 ]. The improved footwork has also been associated with enhanced reaction time and agility [ 11 ]. Another study has shown the significant differences in short-sprint speed and power measures were observed in adolescent athlete after backward running training shows the effectiveness of backward training [ 17 ]. Balance training has been identified as an effective approach to mitigate the risk of falls during backward running, offering benefits during gameplay when players need to respond to the shuttle being behind them, thereby preventing potential falls and enhancing performance.
Backward walking training on treadmill offers a unique and innovative approach to enhancing the physical performance of athletes. By incorporating such exercises into their training regimen, players can improve their agility, balance, and proprioception, which are crucial attributes in badminton. By adding backward training to their training routines, badminton players can enhance their physical attributes, ultimately contributing to improved performance and reduced injury risk during competitive play.
While this study provides valuable insights into the effects of backward walking on trunk stability, balance, agility, and reaction time in badminton players, there are some limitations to consider. The six-week duration of the intervention may not fully capture the long-term effects. The study did not control for external factors that could influence the outcomes, such as participants’ training regimens or nutrition. Additionally, the lack of long-term follow-up limits our understanding of the durability of the observed improvements. There may also be unaccounted confounding variables that could influence the results. Future research should address these limitations to enhance the validity and broader applicability of the findings.
Despite the limitations, this study opens avenues for future research. Firstly, investigations could focus on exploring the optimal duration and frequency of backward walking training to maximize its effectiveness in improving trunk stability, balance, agility, and reaction time. Additionally, further studies could examine the underlying mechanisms through which backward walking influences these physical attributes, such as changes in muscle activation patterns or proprioceptive feedback. Moreover, investigations could extend beyond laboratory settings and explore the real-world application of backward walking training in badminton players during their actual game performance. Lastly, future research could explore the potential benefits of combining backward walking with other training modalities or interventions to enhance overall athletic performance in badminton players.
This study demonstrates that a six-week intervention of backward walking has the potential to improve trunk stability, balance, agility, and reaction time in badminton players. The experimental group showed significant and clinically relevant improvements as compared to the control group. The findings suggest that incorporating backward walking into training regimens may be an effective strategy for enhancing athletic performance in badminton players. However, further research is needed to validate the results in larger and more diverse populations, consider longer intervention duration, and address potential confounding factors to establish the full benefits and applicability of backward walking as a training modality.
All data generated or analyzed during this study will be available upon a reasonable request from the corresponding author.
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The authors extend their appreciation to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs; Rehabilitation Research Chair.
This study was funded by King Saud University, Deanship of Scientific Research, Vice Deanship of Scientific Research Chairs; Rehabilitation Research Chairs. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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Department of Physiotherapy, Faculty of Allied Health Sciences, Manav Rachna International Institute and Studies (MRIIRS), Faridabad, 121001, India
Omkar Sudam Ghorpade & Ankita Sharma
Faculty of Allied Health Sciences, Manav Rachna International Institute and Studies (MRIIRS), Faridabad, 121001, India
Moattar Raza Rizvi
Basic Medical Science Unit, Prince Sultan Military College of Health Sciences, Dhahran, 34313, Saudi Arabia
Harun J. Almutairi
Respiratory Care Department, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, 13713, Saudi Arabia
Fuzail Ahmad
Department of Physical Therapy & Health Rehabilitation, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
Shahnaz Hasan, Abdul Rahim Shaik & Mohamed K. Seyam
Physical Therapy Department, College of Nursing and Health Sciences, Jazan University, Jazan, 45142, Saudi Arabia
Shadab Uddin & Saravanakumar Nanjan
Rehabilitation Research Chair, Department of Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box. 10219, Riyadh, 11433, Saudi Arabia
Amir Iqbal & Ahmad H. Alghadir
College of Healthcare Professions, Dehradun Institute of Technology (D.I.T) University, Diversion Road, Makka Wala, Mussoorie, Uttarakhand, 248009, India
Department of Physiotherapy, Amity Institute of Allied and Health Sciences, Amity University, Noida, Uttar Pradesh, 201301, India
Ankita Sharma
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O.S.G. M.R.R. A.S. S.H. F.A. A.H.A. and A.I. proposed the study concept and design. O.S.G. M.R.R. and A.S. planned the methodology. O.S.G. and A.S. collected data. H.J.A. A.I., A.H.A., and F.A. contributed to the data analysis. F.A. S.H. A.R.S. M.K.S. S.U. S.N. A.H.A. and A.I. contributed to the data interpretation. O.S.G. A.S. M.R.A. F.A. S.H. and A.I. prepared the manuscript’s initial draft. O.S.G. M.R.R. A.S. F.A. S.H. A.R.S. M.K.S. S.U. S.N. A.H.A. and A.I. critically reviewed and edited the manuscript for intellectual content. All authors have read, understood, reviewed, and approved the manuscript’s final version to be submitted or published and take responsibility for the intellectual content of the same manuscript.
Correspondence to Amir Iqbal .
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Ghorpade, O.S., Rizvi, M.R., Sharma, A. et al. Enhancing physical attributes and performance in badminton players: efficacy of backward walking training on treadmill. BMC Sports Sci Med Rehabil 16 , 170 (2024). https://doi.org/10.1186/s13102-024-00962-x
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DOI : https://doi.org/10.1186/s13102-024-00962-x
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In research, the control group is the one not exposed to the variable of interest (the independent variable) and provides a baseline for comparison. The experimental group, on the other hand, is exposed to the independent variable. Comparing results between these groups helps determine if the independent variable has a significant effect on the outcome (the dependent variable).
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 ...
The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled", or held constant, in the control group. A single experiment may include multiple experimental ...
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).
Explains the function of experimental and control groups in the context of psychological experiments. It describes how experimental groups receive the intervention or manipulation under study, while control groups do not, serving as a baseline for comparison. This section stresses the value of random assignment in creating comparable groups, thereby enhancing the validity of research findings ...
Three types of experimental designs are commonly used: 1. Independent Measures. Independent measures design, also known as between-groups, is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.
There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the experimental group, while the group that does not receive the treatment (here, the dry pot) is called the control group.The control group provides a baseline that lets ...
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.
In many studies, control groups are crucial for the conclusion that can be drawn from the investigation. In the case of an experimental treatment study, a well-created control group makes the group type the independent variable of the experiment. Ideally, all conditions in the control groups (including the sample characteristics) should be ...
Table of Contents 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 ...
A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.
In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups. They each receive some level of the independent variable, which in this case is ...
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 ...
The alterations made to this group are deliberate and strategic, aiming to explore the effects of specific changes or treatments. Comparing the outcomes from the experimental group with those of the control group allows researchers to deduce the impact of the variable being tested, thereby, providing a framework for interpreting the results.
A control group is used as a comparison for the experimental group. The control group is the group in an ... Both the experimental and control groups should be kept as similar to each other as ...
A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other. The experimental group receives some sort of treatment, and their results are compared against those of the control group ...
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.
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 ...
Hugh Good. A control group is a common tool that researchers use. It allows them to prove a cause-and-effect relationship with an independent variable. This variable does not change for the control group. In this sense, the control group is the status quo. Researchers compare the effects in the experimental group against the 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 ...
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 ...
A control group should seek to control the positive beliefs of using the intervention, a point that drives blind and double-blind placebo groups. The control condition should elicit some of the symptoms of the intervention but not those that are in the active treatment (e.g., decaffeinated coffee, a treatment that tastes like coffee, and so ...
A control group is an experimental condition that does not receive the actual treatment and may serve as a baseline.A control group may receive a placebo or they may receive no treatment at all. A placebo is something that appears to the participants to be an active treatment, but does not actually contain the active treatment.For example, a placebo pill is a sugar pill that participants may ...
Treatment and control groups. In the design of experiments, hypotheses are applied to experimental units in a treatment group. [ 1] In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. [ 2] There may be more than one treatment group, more than one control group, or both.
The control group received routine exercise training, while the experimental group received routine exercise training along with additional backward walking training on the treadmill. Pre- and post-intervention measurements were taken for core stability using the Plank test, balance using the Star Excursion Balance test, reaction time using the ...