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What Is a Double-Blind Study? | Introduction & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 17 October 2022.

In experimental research , subjects are randomly assigned to either a treatment or control group . A double-blind study withholds each subject’s group assignment from both the participant and the researcher performing the experiment.

If participants know which group they are assigned to, there is a risk that they might change their behaviour in a way that would influence the results. If researchers know which group a participant is assigned to, they might act in a way that reveals the assignment or directly influences the results.

Double blinding guards against these risks, ensuring that any difference between the groups can be attributed to the treatment.

Table of contents

Different types of blinding, importance of blinding, frequently asked questions about double-blind studies.

Blinding means withholding which group each participant has been assigned to. Studies may use single, double or triple blinding.

Single blinding occurs in many different kinds of studies, but double and triple blinding are mainly used in medical research.

Single blinding

If participants know whether they were assigned to the treatment or control group, they might modify their behaviour as a result, potentially changing their eventual outcome.

In a single-blind experiment, participants do not know which group they have been placed in until after the experiment has finished.

If participants in the control group realise they have received a fake vaccine and are not protected against the flu, they might modify their behaviour in ways that lower their chances of becoming sick – frequently washing their hands, avoiding crowded areas, etc. This behaviour could narrow the gap in sickness rates between the control group and the treatment group, thus making the vaccine seem less effective than it really is.

Double blinding

When the researchers administering the experimental treatment are aware of each participant’s group assignment, they may inadvertently treat those in the control group differently from those in the treatment group. This could reveal to participants their group assignment, or even directly influence the outcome itself.

In double-blind experiments, the group assignment is hidden from both the participant and the person administering the experiment.

If these experimenters knew which vaccines were real and which were fake, they might accidentally reveal this information to the participants, thus influencing their behaviour and indirectly the results.

They could even directly influence the results. For instance, if experimenters expect the vaccine to result in lower levels of flu symptoms, they might accidentally measure symptoms incorrectly, thus making the vaccine appear more effective than it really is.

Triple blinding

Although rarely implemented, triple-blind studies occur when group assignment is hidden not only from participants and administrators, but also from those tasked with analysing the data after the experiment has concluded.

Researchers may expect a certain outcome and analyse the data in different ways until they arrive at the outcome they expected, even if it is merely a result of chance.

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Blinding helps ensure a study’s internal validity , or the extent to which you can be confident any link you find in your study is a true cause-and-effect relationship.

Since non-blinded studies can result in participants modifying their behaviour or researchers finding effects that do not really exist, blinding is an important tool to avoid bias in all types of scientific research.

Risk of unblinding

Unblinding occurs when researchers have blinded participants or experimenters, but they become aware of who received which treatment before the experiment has ended.

This may result in the same outcomes as would have occurred without any blinding.

You randomly assign some students to the new programme (the treatment group), while others are instructed with a standard programme (the control group). You use single blinding: you do not inform students whether they are receiving the new instruction programme or the standard one.

If students become aware of which programme they have been assigned to – for example, by talking to previous students about the content of the programme – they may change their behaviour. Students in the control group might work harder on their reading skills to make up for not receiving the new programme, or conversely to put in less effort instead since they might believe the other students will do better than them anyway.

Inability to blind

Double or triple blinding is often not possible. While medical experiments can usually use a placebo or fake treatment for blinding, in other types of research, the treatment sometimes cannot be disguised from either the participant or the experimenter. For example, many treatments that physical therapists perform cannot be faked.

In such cases, you must rely on other methods to reduce bias.

• Running a single- rather than double- or triple-blind study. Sometimes, although you might not be able to hide what each subject receives, you can still prevent them from knowing whether they are in the treatment or control group. Single blinding is particularly useful in non-medical studies where you cannot use a placebo in the control group.
• Relying on objective measures that participants and experimenters have less control over rather than subjective ones, like measuring fever rather than self-reported pain. This should reduce the possibility that participants or experimenters could influence the results.
• Pre-registering data analysis techniques. This will prevent researchers from trying different measures of analysis until they arrive at the answer they’re expecting.

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

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

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

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

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Double-Blind Experimental Study And Procedure Explained

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What is a Blinded Study?

• Binding, or masking, refers to withholding information regarding treatment allocation from one or more participants in a clinical research study, typically in randomized control trials .
• A blinded study prevents the participants from knowing about their treatment to avoid bias in the research. Any information that can influence the subjects is withheld until the completion of the research.
• Blinding can be imposed on any participant in an experiment, including researchers, data collectors, evaluators, technicians, and data analysts. 
• Good blinding can eliminate experimental biases arising from the subjects’ expectations, observer bias, confirmation bias, researcher bias, observer’s effect on the participants, and other biases that may occur in a research test.
• Studies may use single-, double- or triple-blinding. A trial that is not blinded is called an open trial.

Double-Blind Studies

Double-blind studies are those in which neither the participants nor the experimenters know who is receiving a particular treatment.

Double blinding prevents bias in research results, specifically due to demand characteristics or the placebo effect.

Demand characteristics are subtle cues from researchers that can inform the participants of what the experimenter expects to find or how participants are expected to behave.

If participants know which group they are assigned to, they might change their behavior in a way that would influence the results. Similarly, if a researcher knows which group a participant is assigned to, they might act in a way that reveals the assignment or influences the results.

Double-blinding attempts to prevent these risks, ensuring that any difference(s) between the groups can be attributed to the treatment. 

On the other hand, single-blind studies are those in which the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

Single-blind studies are beneficial because they reduce the risk of errors due to subject expectations. However, single-blind studies do not prevent observer bias, confirmation bias , or bias due to demand characteristics.

Because the experiments are aware of which participants are receiving which treatments, they are more likely to reveal subtle clues that can accidentally influence the research outcome.

Double-blind studies are considered the gold standard in research because they help to control for experimental biases arising from the subjects’ expectations and experimenter biases that emerge when the researchers unknowingly influence how the subjects respond or how the data is collected.

Using the double-blind method improves the credibility and validity of a study .

Example Double-Blind Studies

Rostock and Huber (2014) used a randomized, placebo-controlled, double-blind study to investigate the immunological effects of mistletoe extract. However, their study showed that double-blinding is impossible when the investigated therapy has obvious side effects. 

Using a double-blind study, Kobak et al. (2005) found that S t John’s wort ( Hypericum perforatum ) is not an efficacious treatment for anxiety disorder, specifically OCD.

Using the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), they found that the mean change with St John’s wort was not significantly different from the mean change found with placebo. 

Cakir et al. (2014) conducted a randomized, controlled, and double-blind study to test the efficacy of therapeutic ultrasound for managing knee osteoarthritis.

They found that all assessment parameters significantly improved in all groups without a significant difference, suggesting that therapeutic ultrasound provided no additional benefit in improving pain and functions in addition to exercise training.

Using a randomized double-blind study, Papachristofilou et al. (2021) found that whole-lung LDRT failed to improve clinical outcomes in critically ill patients admitted to the intensive care unit requiring mechanical ventilation for COVID-19 pneumonia.

Double-Blinding Procedure

Double blinding is typically used in clinical research studies or clinical trials to test the safety and efficacy of various biomedical and behavioral interventions.

In such studies, researchers tend to use a placebo. A placebo is an inactive substance, typically a sugar pill, that is designed to look like the drug or treatment being tested but has no effect on the individual taking it. 

The placebo pill was given to the participants who were randomly assigned to the control group. This group serves as a baseline to determine if exposure to the treatment had any significant effects.

Those randomly assigned to the experimental group are given the actual treatment in question. Data is collected from both groups and then compared to determine if the treatment had any impact on the dependent variable.

All participants in the study will take a pill or receive a treatment, but only some of them will receive the real treatment under investigation while the rest of the subjects will receive a placebo. 

With double blinding, neither the participants nor the experimenters will have any idea who receives the real drug and who receives the placebo. 

For Example

A common example of double-blinding is clinical studies that are conducted to test new drugs.

In these studies, researchers will use random assignment to allocate patients into one of three groups: the treatment/experimental group (which receives the drug), the placebo group (which receives an inactive substance that looks identical to the treatment but has no drug in it), and the control group (which receives no treatment).

Both participants and researchers are kept unaware of which participants are allocated to which of the three groups.

The effects of the drug are measured by recording any symptoms noticed in the patients.

Once the study is unblinded, and the researchers and participants are made aware of who is in which group, the data can be analyzed to determine whether the drug had effects that were not seen in the placebo or control group, but only in the experimental group. 

Double-blind studies can also be beneficial in nonmedical interventions, such as psychotherapIes.

Reduces risk of bias

Double-blinding can eliminate, or significantly reduce, both observer bias and participant biases.

Because both the researcher and the subjects are unaware of the treatment assignments, it is difficult for their expectations or behaviors to influence the study.

Results can be duplicated

The results of a double-blind study can be duplicated, enabling other researchers to follow the same processes, apply the same test item, and compare their results with the control group.

If the results are similar, then it adds more validity to the ability of a medication or treatment to provide benefits. 

It tests for three groups

Double-blind studies usually involve three groups of subjects: the treatment group, the placebo group, and the control group.

The treatment and placebo groups are both given the test item, although the researcher does not know which group is getting real treatment or placebo treatment.

The control group doesn’t receive anything because it serves as the baseline against which the other two groups are compared.

This is an advantage because if subjects in the placebo group improved more than the subjects in the control group, then researchers can conclude that the treatment administered worked.

Applicable across multiple industries

Double-blind studies can be used across multiple industries, such as agriculture, biology, chemistry, engineering, and social sciences.

Double-blind studies are used primarily by the pharmaceutical industry because researchers can look directly at the impact of medications. 

Disadvantages

Inability to blind.

In some types of research, specifically therapeutic, the treatment cannot always be disguised from the participant or the experimenter. In these cases, you must rely on other methods to reduce bias.

Additionally, imposing blinding may be impossible or unethical for some studies. 

Double-blinding can be expensive because the researcher has to examine all the possible variables and may have to use different groups to gather enough data. 

Small Sample Size

Most double-blind studies are too small to provide a representative sample. To be effective, it is generally recommended that double-blind trials include around 100-300 participants.

Studies involving fewer than 30 participants generally can’t provide proof of a theory. 

Negative Reaction to Placebo

In some instances, participants can have adverse reactions to the placebo, even producing unwanted side effects as if they were taking a real medication. 

It doesn’t reflect real-life circumstances

When participants receive treatment or medication in a double-blind placebo study, each individual is told that the item in question might be real medication or a placebo.

This artificial situation does not represent real-life circumstances because when a patient receives a pill after going to the doctor in the real-world, they are told that the product is actual medicine intended to benefit them.

When situations don’t feel realistic to a participant, then the quality of the data can decrease exponentially.

What is the difference between a single-blind, double-blind, and triple-blind study?

In a single-blind study, the experimenters are aware of which participants are receiving the treatment while the participants are unaware.

In a double-blind study, neither the patients nor the researchers know which study group the patients are in. In a triple-blind study, neither the patients, clinicians, nor the people carrying out the statistical analysis know which treatment the subjects had.

Is a double-blind study the same as a randomized clinical trial?

Yes, a double-blind study is a form of a randomized clinical trial in which neither the participants nor the researcher know if a subject is receiving the experimental treatment, a standard treatment, or a placebo.

Are double-blind studies ethical?

Double blinding is ethical only if it serves a scientific purpose. In most circumstances, it is unethical to conduct a double-blind placebo controlled trial where standard therapy exists.

What is the purpose of randomization using double blinding?

Randomization with blinding avoids reporting bias, since no one knows who is being treated and who is not, and thus all treatment groups should be treated the same. This reduces the influence of confounding variables and improves the reliability of clinical trial results.

Why are double-blind experiments considered the gold standard?

Randomized double-blind placebo control studies are considered the “gold standard” of epidemiologic studies as they provide the strongest possible evidence of causality.

Additionally, because neither the participants nor the researchers know who has received what treatment, double-blind studies minimize the placebo effect and significantly reduce bias.

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

Cakir, S., Hepguler, S., Ozturk, C., Korkmaz, M., Isleten, B., & Atamaz, F. C. (2014). Efficacy of therapeutic ultrasound for the management of knee osteoarthritis: a randomized, controlled, and double-blind study. American journal of physical medicine & rehabilitation , 93 (5), 405-412.

Kobak, K. A., Taylor, L. V., Bystritsky, A., Kohlenberg, C. J., Greist, J. H., Tucker, P., … & Vapnik, T. (2005). St John’s wort versus placebo in obsessive–compulsive disorder: results from a double-blind study. International Clinical Psychopharmacology , 20 (6), 299-304.

Papachristofilou, A., Finazzi, T., Blum, A., Zehnder, T., Zellweger, N., Lustenberger, J., … & Siegemund, M. (2021). Low-dose radiation therapy for severe COVID-19 pneumonia: a randomized double-blind study. International Journal of Radiation Oncology* Biology* Physics , 110 (5), 1274-1282. Rostock, M., & Huber, R. (2004). Randomized and double-blind studies–demands and reality as demonstrated by two examples of mistletoe research. Complementary Medicine Research , 11 (Suppl. 1), 18-22.

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Double-Blind Studies in Research

A double-blind study is one in which neither the participants nor the experimenters know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results. Double-blind studies are particularly useful for preventing bias due to demand characteristics or the placebo effect .

For example, let's imagine that researchers are investigating the effects of a new drug. In a double-blind study, the researchers who interact with the participants would not know who was receiving the actual drug and who was receiving a placebo.

A Closer Look at Double-Blind Studies

Let’s take a closer look at what we mean by a double-blind study and how this type of procedure works. As mentioned previously, double-blind indicates that the participants and the experimenters are unaware of who is receiving the real treatment. What exactly do we mean by ‘treatment'? In a psychology experiment, the treatment is the level of the independent variable that the experimenters are manipulating.

This can be contrasted with a single-blind study in which the experimenters are aware of which participants are receiving the treatment while the participants remain unaware.

In such studies, researchers may use what is known as a placebo. A placebo is an inert substance, such as a sugar pill, that has no effect on the individual taking it. The placebo pill is given to participants who are randomly assigned to the control group. A control group is a subset of participants who are not exposed to any levels of the independent variable . This group serves as a baseline to determine if exposure to the independent variable had any significant effects.

Those randomly assigned to the experimental group are given the treatment in question. Data collected from both groups are then compared to determine if the treatment had some impact on the dependent variable .

All participants in the study will take a pill, but only some of them will receive the real drug under investigation. The rest of the subjects will receive an inactive placebo. With a double-blind study, the participants and the experimenters have no idea who is receiving the real drug and who is receiving the sugar pill.

Double-blind experiments are simply not possible in some scenarios. For example, in an experiment looking at which type of psychotherapy is the most effective, it would be impossible to keep participants in the dark about whether or not they actually received therapy.

Reasons to Use a Double-Blind Study

So why would researchers opt for such a procedure? There are a couple of important reasons.

• First, since the participants do not know which group they are in, their beliefs about the treatment are less likely to influence the outcome.
• Second, since researchers are unaware of which subjects are receiving the real treatment, they are less likely to accidentally reveal subtle clues that might influence the outcome of the research.  

The double-blind procedure helps minimize the possible effects of experimenter bias.   Such biases often involve the researchers unknowingly influencing the results during the administration or data collection stages of the experiment. Researchers sometimes have subjective feelings and biases that might have an influence on how the subjects respond or how the data is collected.

In one research article, randomized double-blind placebo studies were identified as the "gold standard" when it comes to intervention-based studies.   One of the reasons for this is the fact that random assignment reduces the influence of confounding variables.

Imagine that researchers want to determine if consuming energy bars before a demanding athletic event leads to an improvement in performance. The researchers might begin by forming a pool of participants that are fairly equivalent regarding athletic ability. Some participants are randomly assigned to a control group while others are randomly assigned to the experimental group.

Participants are then be asked to eat an energy bar. All of the bars are packaged the same, but some are sports bars while others are simply bar-shaped brownies. The real energy bars contain high levels of protein and vitamins, while the placebo bars do not.

Because this is a double-blind study, neither the participants nor the experimenters know who is consuming the real energy bars and who is consuming the placebo bars.

The participants then complete a predetermined athletic task, and researchers collect data performance. Once all the data has been obtained, researchers can then compare the results of each group and determine if the independent variable had any impact on the dependent variable.  

A Word From Verywell

A double-blind study can be a useful research tool in psychology and other scientific areas. By keeping both the experimenters and the participants blind, bias is less likely to influence the results of the experiment. 

A double-blind experiment can be set up when the lead experimenter sets up the study but then has a colleague (such as a graduate student) collect the data from participants. The type of study that researchers decide to use, however, may depend upon a variety of factors, including characteristics of the situation, the participants, and the nature of the hypothesis under examination.

National Institutes of Health. FAQs About Clinical Studies .

Misra S. Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies . Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

Goodwin, CJ. Research In Psychology: Methods and Design . New York: John Wiley & Sons; 2010.

Kalat, JW. Introduction to Psychology . Boston, MA: Cengage Learning; 2017.

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

Double Blind Study (Definition + Examples)

The impact of many treatments can only be confirmed after their effect has been verified in a double-blind study.

What Is a Double-Blind Study? 

A double-blind study is an experiment where both researchers and participants are “blind to” the crucial aspects of the study, such as the hypotheses, expectations, or the allocation of subjects to groups. In double-blind clinical trials, neither the experimenters nor the participants are aware of who is receiving a treatment.

Why Do a Double-Blind Study?

The main purpose of double-blind studies is to minimize the effects of experimenter bias . In other words, the results of the research are less likely to be affected by external factors, such as the experimenters verbally or nonverbally communicating their assumptions about the treatment’s efficiency or the expectations of the participants.

Double-blind studies serve as an invaluable scientific method in the pharmaceutical industry trials where they are regularly used for determining the impact of new medications. Double-blind studies are the very foundation of modern evidence-based medicine. They are often referred to as the gold standard for testing medications, that is, the most accurate test available. 

While they are best known for their application in medicine, double-blinded studies are widely used to validate theories and ideas in many other fields including agriculture, biology, chemistry, engineering, forensics, and social sciences. 

Example of Double-Blind Study

Identifying successful treatments is a complex procedure. Let’s say that a physician prescribes a new medication to a patient. After taking the medication, the patient reports improvement in his or her condition. Yet this doesn’t simply mean that the treatment is effective. In fact, in many cases patients will see improvements even when they are not taking active medication. 

In order to properly test the medication, a double-blind study will have to take place in which the experimenter (acting as the physician) administers either the medication or a placebo to the participant (acting as the patient). Only a third-party knows whether the medication was real or not. The participant's answers about their treatment will be recorded and sent to that third party.

Double-blind studies aren't just used to test new medication. A double-blind study was used to see if airport security dogs could sniff out COVID!

Double-Blind Studies and Placebo Effect

The placebo effect is a crucial component of double-blind studies. 

A placebo is an inactive substance that has no effect on the individual who is taking it. It looks just like the medication that is being tested so that the participants can’t say whether they are receiving the treatment or not. 

How to Conduct a Double-Blind Study

Subjects in double-blind studies are typically divided into three different groups: treatment or experimental group, placebo group, and control group. 

Participants who are not receiving any treatment are placed in the control group. This group serves as a baseline for determining whether the medication in question has any significant effects. If the control group gets better over time, then this improvement will set a standard against which the other two groups are compared. 

People placed in the treatment group are given the actual medication, while subjects in the placebo group are offered a placebo pill. Neither the participants in the treatment and placebo groups nor the experimenters have the information on who is receiving the real drug.

At the end of the trial, data collected from the groups are compared to determine if the treatment had the expected outcome. If subjects in the placebo group fare better than the control group, this positive development can be attributed to the participants’ belief that the pill works. But if people in the treatment group improve more than those in the placebo one, then the results can be attributed to the effect of the medication.

Other Types of Blind Studies

Several different types of blind studies are being used in research, such as double-blind comparative studies, single-blind studies, and triple-blind studies.

Double-blind comparative studies

In double-blind comparative studies, one group of participants is given a standard drug instead of a placebo. These studies compare the effects of new medicine and an old one whose impact has already been proven. This kind of study is useful in determining whether a new treatment is more effective than the existing one. 

Single-blind studies

In single-blind studies, only the participants are not informed whether they are receiving the real treatment. The experimenters, on the other hand, know which participants belong to which group.  

Triple-blind studies

Triple-blind studies are clinical trials in which knowledge about the treatment is hidden not only from subjects and experimenters but also from anyone involved in organizing the study and data analysis. 

Limitations of Double-Blind Studies

Despite their significance, double-blind studies hold a number of limitations and are not applicable to every type of research.

Number of Participants

To be effective, a double-blind study must include at least 100 participants and preferably as many as 300. Although effective treatments can also be proven in some small-scale trials, many double-blind studies are too limited in size to provide a representative sample and establish meaningful patterns. Studies involving fewer than 30 participants generally can't provide proof of a theory. 

Types of Double-Blind Studies 

Double blinding is not feasible in all types of trials. For instance, it is not possible to design studies on therapies such as acupuncture, physical therapy, diet, or surgery in a double-blind manner. In these cases, researchers and participants can’t be kept unaware of who is receiving therapy .

Nocebo Effect

Participants in clinical trials must be informed of the possible side effects that may result from the experimental treatment. However, the mere suggestion of a negative outcome may lead to the adverse placebo effect, also known as the nocebo effect. It can result in participant dropouts and the need for additional medications to treat the side effects.

In research, the use of a placebo is acceptable only in situations when there is no proven acceptable treatment for the condition in question. For ethical reasons, participants must always be informed of the possibility that they will be given a placebo. As a consequence, some participants may think that they feel the effects of the placebo, which makes them believe that they are in the treatment group. This high positive expectancy is a disadvantage that can lead to a misinterpretation of the results.

Costs of Double-Blind Studies

Double-blind procedures are very expensive. They may take several months to complete, as experiments often require numerous trials using different groups in order to collect enough data. As a result, double-blind studies can cost up to several million dollars, depending on the amount of work required and the industry in which the product is being tested.

Related posts:

• The Placebo Effect (Examples + How it Works in Psychology)
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• Variable Interval Reinforcement Schedule (Examples)

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Double Blind Study – Blinded Experiments

In science and medicine, a blind study or blind experiment is one in which information about the study is withheld from the participants until the experiment ends. The purpose of blinding an experiment is reducing bias, which is a type of error . Sometimes blinding is impractical or unethical, but in many experiments it improves the validity of results. Here is a look at the types of blinding and potentials problems that arise.

Single Blind, Double Blind, and Triple Blind Studies

The three types of blinding are single blinding, double blinding, and triple blinding:

Single Blind Study

In a single blind study , the researchers and analysis team know who gets a treatment, but the experimental subjects do not. In other words, the people performing the study know what the independent variable is and how it is being tested. The subjects are unaware whether they are receiving a placebo or a treatment. They may even be unaware what, exactly, is being studied.

Example: Violin Study

For example, consider an experiment that tests whether or not violinists can tell the difference a Stradivarius violin (generally regarded as superior) and a modern violin. The researchers know the type of violin they hand to a violinist, but the musician does not (is blind). In case you’re curious, in an actual experiment performed by Claudia Fritz and Joseph Curtin, it turned out violinists actually can’t tell the instruments apart.

Double Blind Study

In a double blind study, neither the researchers nor subjects know which group receives a treatment and which gets a placebo .

Example: Drug Trial

Many drug trials are double-blinded, where neither the doctor nor patient knows whether the drug or a placebo is administered. So, who gets the drug or the placebo is randomly assigned (without the doctor knowing who gets what). The inactive ingredients, color, and size of a pill (for example) are the same whether it is the treatment or placebo.

Triple Blind Study

A triple blind study includes an additional level of blinding. So, the data analysis team or the group overseeing an experiment is blind, in addition to the researchers and subjects.

Example: Vaccine Study

Triple blind studies are common as part of the vaccine approval process. Here, the people who analyze vaccine effectiveness collate data from many test sites and are unaware of which group a participant belongs to.

Some guidelines advocate for removing terms like “single blind” and “double blind” because they do not inherently describe which party is blinded. For example, a double blind study could mean the subjects and scientists are blind or it could mean the subjects and assessors are blind. When you describe blinding in an experiment, report who is blinded and what information is concealed.

The point of blinding is minimizing bias. Subjects have expectations if they know they receive a placebo versus a treatment. And, researchers have expectations regarding the expected outcome. For example, confirmation bias occurs when an investigator favors outcomes that support pre-existing research or the scientist’s own beliefs.

Unblinding is when masked information becomes available. In experiments with humans, intentional unblinding after a study concludes is typical. This way, a subject knows whether or not they received a treatment or placebo. Unblinding after a study concludes does not introduce bias because the data has already been collected and analyzed.

However, premature unblinding also occurs. For example, a doctor reviewing bloodwork often figures out who is getting a treatment and who is getting a placebo. Similarly, patients feeling an effect from a pill or injection suspect they are in the treatment group. One safeguard against this is an active placebo. An active placebo causes side effects, so it’s harder to tell treatment and placebo groups apart just based on how a patient feels.

Although premature unblinding affects the outcome of the results, it isn’t usually reported. This is a problem because unintentional unblinding favors false positives, at least in medicine. For example, if subjects believe they are receiving treatment, they often feel better even if a therapy isn’t effective. Premature unblinding is one of the issues at the heart of the debate about whether or not antidepressants are effective. But, it applies to all blind studies.

Uses of Blind Studies

Of course, blind studies are valuable in medicine and scientific research. But, they also have other applications.

For example, in a police lineup, having an officer familiar with the suspects can influence a witness’s selection. A better option is a blind procedure, using an office who does not know a suspect’s identity. Product developers routinely use blind studies for determining consumer preference. Orchestras use blind judging for auditions. Some employers and educational institutions use blind data for application selection.

• Bello, Segun; Moustgaard, Helene; Hróbjartsson, Asbjørn (October 2014). “The risk of unblinding was infrequently and incompletely reported in 300 randomized clinical trial publications”. Journal of Clinical Epidemiology . 67 (10): 1059–1069. doi: 10.1016/j.jclinepi.2014.05.007
• Daston, L. (2005). “Scientific Error and the Ethos of Belief”. Social Research . 72 (1): 18. doi: 10.1353/sor.2005.0016
• MacCoun, Robert; Perlmutter, Saul (2015). “Blind analysis: Hide results to seek the truth”. Nature . 526 (7572): 187–189. doi: 10.1038/526187a
• Moncrieff, Joanna; Wessely, Simon; Hardy, Rebecca (2018). “Meta-analysis of trials comparing antidepressants with active placebos”. British Journal of Psychiatry . 172 (3): 227–231. doi: 10.1192/bjp.172.3.227
• Schulz, Kenneth F.; Grimes, David A. (2002). “Blinding in randomised trials: hiding who got what”. Lancet . 359 (9307): 696–700. doi: 10.1016/S0140-6736(02)07816-9

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Double-blind study.

Sharoon David ; Paras B. Khandhar .

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Last Update: July 17, 2023 .

• Definition/Introduction

A clinical research study or a clinical trial is an experiment or observation performed on human subjects to generate data on the safety and efficacy of various biomedical and behavioral interventions. [1]

Blinding or masking refers to the withholding of information regarding treatment allocation from one or more participants in a clinical research study. It is an essential methodological feature of clinical studies that help maximize the validity of the research results. [2]

• Issues of Concern

Blinding covers any of the numerous participants of the clinical trial, e.g., researchers, subjects, technicians, and data analysts. Single-, double-, and triple-blinding are commonly used blinding strategies in clinical research. A single-blind study masks the subjects from knowing which study treatment, if any, they are receiving. A double-blind study blinds both the subjects as well as the researchers to the treatment allocation. Triple-blinding involves withholding this information from the patients, researchers, as well as data analysts.Randomized, double-blind placebo-controlled trials involve the random placement of participants into two groups; an experimental group that receives the investigational treatment and a control group that acquires a placebo. Neither the researchers nor the study subjects know who is getting the experimental treatment and who is getting a placebo. This type of clinical study ranks as the gold standard for the validation of treatment interventions. [3]

Unfortunately, blinding is not possible to achieve in all clinical trials. For example, the method of drug delivery may not be amenable to blinding. An excellent clinical protocol may help ensure that within the ethical and practical constraints, blinding is achieved as effectively as possible.

• Clinical Significance

Bias refers to a deviation from the truth in the collection, analysis, interpretation, or publication of data, leading to false conclusions. Poor blinding of a clinical research study may lead to bias that may result in inflated effect size and increase the risk of type I error. Even a small error in blinding may lead to a statistically significant result without any real difference between the study groups. [4]

Keeping both the researchers and the subjects blinded to treatment allows a double-blinded study to prevent the researchers from treating the study groups differently. The double-blinded study minimizes the risk of various types of biases, such as observer bias or confirmation bias, which may influence the results of the investigation. [5] [6]  It may also help avoid a disproportionately large placebo effect in the patients involved in the study. [3]

Unblinding may occur during any portion of the blinded clinical trial. Unblinding that occurs before the conclusion of a trial may be a source of bias that the study should document and report. It is the responsibility of all the healthcare professionals involved in a clinical trial, such as physicians, nurses, pharmacists, technicians, and data analysts, to maintain blinding as effectively as possible during the trial and to report any premature unblinding. [7]

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Disclosure: Sharoon David declares no relevant financial relationships with ineligible companies.

Disclosure: Paras Khandhar declares no relevant financial relationships with ineligible companies.

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Double-Blind Studies: The Secret to Reliable Research Results

Double-blind studies are essential to research in various fields, including health and psychology. In a double-blind study, neither the participants nor the researchers know who receives a particular treatment. This procedure prevents bias in research results, which demand characteristics or the placebo effect can cause.

By withholding information about the treatment, double-blind studies help ensure that the results are as accurate and unbiased as possible. This method is beneficial when testing the efficacy of new medications or treatments. Without a double-blind study, researchers may unintentionally influence the results by giving more attention or care to one group over another.

Double-blind studies are also beneficial for preventing the placebo effect when a participant experiences an improvement in symptoms simply because they believe they are receiving treatment. The placebo effect is minimized by keeping both the participants and researchers in the dark about who is receiving the treatment. Overall, double-blind studies are a crucial tool for producing trustworthy and reliable research results.

Importance of Double-Blind Studies

When conducting research, minimizing potential biases that may influence the results is essential. One way to achieve this is by using double-blind studies. Double-blind studies are a type of research where neither the participants nor the researchers know who is receiving a particular treatment. This procedure is utilized to prevent bias in research results.

By using a double-blind study, we can minimize the effects of demand characteristics or the placebo effect. Demand characteristics are the cues that participants pick from the researcher, which may influence their behavior. The placebo effect is the phenomenon where a participant’s belief in a treatment’s effectiveness improves their condition, even if the treatment is not practical.

Double-blind studies are instrumental in the field of medicine. They are commonly used to test the efficacy of new drugs. By using a double-blind study, we can ensure that the results are not biased towards the drug being tested. This is important because if the results were biased, it could lead to the approval of an ineffective or harmful drug.

In addition to medicine, double-blind studies are also used in psychology research. For example, in a study on the effects of a new therapy, a double-blind study can ensure that the participant’s beliefs about the therapy do not influence the results.

Double-Blind Studies in Clinical Trials

Clinical trials are an essential part of medical research. They help researchers determine whether a new treatment is safe and effective. Double-blind studies are often used in clinical trials to prevent bias and maximize the validity of the research results.

Designing a Clinical Trial

In a clinical trial, participants are randomly assigned to either a treatment or control group. In a double-blind study, neither the participants nor the researchers know who receives the treatment and the placebo. This helps prevent bias in the results.

To design a double-blind study, researchers must carefully consider the study’s objectives, the population being studied, and the treatment being tested. They must also ensure that the investigation is conducted ethically and that all participants are fully informed about the study’s risks and benefits.

Analyzing Results

Researchers analyze the results after a double-blind clinical trial to determine whether the treatment is effective. They compare the outcomes of the treatment group and the control group to see if there is a significant difference between the two. If the treatment is effective, researchers may seek approval from regulatory agencies to make the treatment available to the public.

Analyzing the results of a double-blind study requires careful statistical analysis. Researchers must ensure that the study’s sample size is large enough to detect meaningful differences between the treatment and control groups. They must also control for any variables affecting the results, such as age, gender, or underlying health conditions.

Double-Blind Studies in Social Sciences

In social sciences, double-blind studies are commonly used to investigate the effects of various interventions, such as psychotherapy, medication, or lifestyle changes. Double-blind studies are instrumental in social sciences research because they help eliminate bias and increase the validity of the findings.

Methodology

In a double-blind study, neither the participants nor the researchers know who receives the treatment and the placebo. This helps eliminate the placebo effect and other biases affecting the study results.

To conduct a double-blind study, researchers must first recruit participants and randomly assign them to either the treatment or the control group. Then, the researchers must administer the treatment and the placebo in an identical way in appearance, taste, and smell. This ensures that the participants cannot tell whether they receive the treatment or the placebo.

During the study, the researchers must also ensure that they know the treatment assignment to the participants. For example, they may use coded labels or have a third party administer the treatment.

Interpreting Findings

When interpreting the findings of a double-blind study, it is vital to consider the limitations of the study design. For example, double-blind studies may only be feasible in some situations, such as when the treatment involves a surgical procedure or has obvious side effects.

It is also essential to consider the sample size and the characteristics of the study population. Double-blind studies may not be generalizable to all people, and the findings may not apply to individuals with certain conditions or factors.

Despite these limitations, double-blind studies remain essential in social sciences research. They help increase the validity of the findings and provide valuable insights into the effectiveness of various interventions.

Challenges in Double-Blind Studies

Double-blind studies are an essential methodological feature of clinical research studies that help maximize the validity of the research results. However, there are several challenges associated with conducting double-blind studies. In this section, we will discuss some of the most significant challenges that researchers face when conducting double-blind studies.

Limitations

One of the primary challenges of double-blind studies is the limitations associated with blinding. Blinding is a process that involves withholding information regarding treatment allocation from one or more participants in a clinical research study. However, blinding is only sometimes possible or practical. For example, in some studies, it may be impossible to blind the participants or the researchers due to the nature of the intervention or the study design.

Another limitation of double-blind studies is the potential for unblinding. Unblinding occurs when a participant or a researcher becomes aware of the treatment allocation. Unblinding can occur accidentally or intentionally and compromise the study results’ validity.

Ethical Considerations

Double-blind studies also raise ethical considerations. For example, in some studies, blinding may not be ethical if the intervention poses a significant risk to the participants. In such cases, the participants must be informed of the treatment allocation to ensure their safety.

Additionally, blinding can create ethical dilemmas for researchers. For example, researchers may be hesitant to withhold or deceive participants’ information. Moreover, researchers must ensure that the participants understand the risks and benefits of participating in the study and provide informed consent.

Conducting double-blind studies comes with several challenges, including limitations and ethical considerations. As researchers, we must be aware of these challenges and take steps to address them to ensure the validity and ethicality of our studies.

Future of Double-Blind Studies

As we move towards a more technologically advanced future, how we conduct double-blind studies is also evolving. This section will discuss some technological advancements and emerging trends shaping the future of double-blind studies.

Technological Advancements

One of the most significant technological advancements impacting double-blind studies is wearable devices. These devices can track physiological parameters such as heart rate, blood pressure, and sleep patterns, providing researchers with a wealth of data. This data can be used to monitor the effects of a particular treatment and determine if it is effective.

Another technological advancement that is gaining popularity is the use of mobile apps. These apps can collect data from study participants, making it easier for researchers to monitor their progress. For example, an app could remind participants to take their medication at a specific time, ensuring they adhere to the study protocol.

Emerging Trends

One of the emerging trends in double-blind studies is the use of virtual reality (VR) . VR technology can be used to create realistic environments that simulate real-world scenarios. This can be particularly useful in studies that involve phobias or anxiety disorders. For example, a VR environment could simulate a fear of flying, allowing researchers to study the effects of a particular treatment in a controlled environment.

Another emerging trend is using artificial intelligence (AI) in double-blind studies. AI can analyze large amounts of data quickly and accurately, making it easier for researchers to identify patterns and trends. For example, AI could analyze data from wearable devices, identifying specific physiological parameters affected by a particular treatment.

Frequently Asked Questions

How do double-blind studies work.

Double-blind studies are a type of research study in which neither the participants nor the experimenters know which group each participant has been assigned to. This type of blinding helps to prevent bias in research results. In a double-blind study, each subject’s group assignment is withheld from both the participant and the researcher performing the experiment. The assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

What is the purpose of conducting double-blind studies?

The purpose of conducting double-blind studies is to prevent bias in research results. By withholding the group assignment from both the participant and the researcher performing the experiment, researchers can ensure that the results of the study are not influenced by any preconceived notions or expectations of the participants or the researchers.

What are the advantages of using double-blind studies in research?

The advantages of using double-blind studies in research are numerous. By preventing bias in research results, researchers can ensure that the results of the study are more accurate and reliable. This can help to improve the quality of research and the validity of the conclusions drawn from the research.

What are the limitations of double-blind studies?

While double-blind studies are an effective way to prevent bias in research results, they do have some limitations. For example, double-blind studies may be more difficult to conduct than other types of studies. Additionally, double-blind studies may be more expensive and time-consuming than other types of studies.

How are double-blind studies different from single-blind studies?

In a single-blind study, only the participants are blinded to the group assignment. In a double-blind study, both participants and experimenters are blinded. This type of blinding helps to prevent bias in research results. Single-blinding occurs in many different kinds of studies, but double- and triple-blinding are mainly used in medical research.

What are some examples of successful double-blind studies?

There have been many successful double-blind studies conducted in various fields of research. For example, a double-blind study conducted in the field of medicine found that a certain medication was more effective than a placebo in treating a particular condition. Another double-blind study conducted in the field of psychology found that a certain type of therapy was more effective than another type of therapy in treating a particular mental health condition.

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Double-Blind Procedure

A double-blind procedure is a research method commonly used in scientific studies to minimize bias and increase the validity of the results obtained. It involves keeping both the participants and the experimenters unaware of certain information to prevent conscious or subconscious influences that could affect the outcome of the study.

The double-blind procedure typically involves the following steps:

• Random Assignment: Participants are randomly assigned to different groups (e.g., experimental group and control group).
• Blinding Participants: Participants are unaware of their group assignment and any additional information that could potentially influence their behavior or responses during the study.
• Blinding Experimenters: The experimenters who interact directly with the participants and collect data are also unaware of the group assignments, individual characteristics, or any other information that could sway their behavior or assessments.
• Data Collection: Data is collected from the participants using various measures, such as surveys, questionnaires, observations, or physiological recordings.
• Data Analysis: The collected data is analyzed without knowledge of the group assignments, allowing for an unbiased evaluation of the effects being investigated.
• Unblinding: Only after the analysis and interpretation of results, the group assignments are revealed to the experimenters, participants, and researchers involved in the study.

Benefits and Importance

The double-blind procedure serves several important purposes:

• Eliminating Bias: By ensuring that both the participants and experimenters are unaware of certain information, the procedure helps prevent preconceived notions, expectations, or personal beliefs from influencing the results or introducing bias into the study.
• Increasing Reliability: The use of double-blind methodology enhances the reliability and validity of the findings, as it reduces the potential for systematic errors or manipulation by either the participants or the experimenters.
• Enhancing Objectivity: Blinding both participants and experimenters promotes objectivity in data collection, interpretation, and analysis. It allows researchers to focus solely on the scientific inquiry rather than subjective influences.
• Establishing Causality: The double-blind procedure helps establish a causal relationship between the manipulated variable (independent variable) and the measured outcome (dependent variable) by minimizing confounding factors and alternative explanations.

Overall, the double-blind procedure is a crucial methodology in many fields of research, including medicine, psychology, and social sciences, as it strengthens the credibility and validity of scientific investigations.

Double-Blind Study

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DOUBLE-BLIND TRIAL. The double-blind trial is a research method that attempts to reduce the bias in research studies. In the classic double-blind trial, subjects are randomly assigned to receive an active medication or a placebo. The placebo is formulated to look and perhaps even taste like the active medication – but the placebo contains no active ingredients. We use the term “double-blind” to indicate that investigators and patients (and parents) do not know whether the patient is getting the active medication or the placebo. The treatment mask is intended to reduce bias and expectation.

When a new medication is being introduced, there may be a lot of interest and hope for the new medication. In the absence of placebo control, this interest and hope could lead to false impressions about the benefits of the medication. Indeed, high expectations can also contribute to the so-called “placebo effect.” In several recent studies in children with autism spectrum disorders, as...

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References and Reading

Vitiello, B., & Scahill, L. (2011). Clinical trials methdology and design. In A. Martin, L. Scahill, & C. Kratochvil (Eds.), Pediatric psychopharmacology: Principles and practice (pp. 711–724). New York: Oxford University Press.

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Lawrence David Scahill

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Blinding: A detailed guide for students

Posted on 26th June 2017 by Saul Crandon

What is blinding?

Blinding is about ensuring that participants and/or personnel within a study are unaware of a particular element of that study. It is done to minimise bias  [1,2].  Although blinding can be implemented in a range of study designs, for the purposes of this article, we will focus specifically on randomised controlled trials (RCTs). You will often see that RCTs are described as ‘double-blinded’. This means that both the study participants and researchers are unaware of the group allocations. In other words, neither the patients nor researchers know who is receiving the ‘actual’ intervention or the ‘dummy’ intervention.

There are a number of different levels of blinding:

Single = One party is blinded to treatment allocation, this usually refers to either patients only or researchers only

Double = Two parties are blinded to treatment allocation, this usually refers to patients and researchers

Triple = Three parties are blinded to treatment allocation, this usually includes patients, researchers and then other staff involved in the running of the study (e.g. data collectors, statisticians etc).

Common methods of blinding

One of the most common methods of blinding in RCTs is the use of seemingly identical medications; one ‘active’ pill and one ‘placebo’ pill. As they are physically identical, it is impossible for patients and researchers to discern which pill is the active one based on appearance alone . This is an example of robust blinding. If it is not robust, there is a risk of un-blinding. This is demonstrated well by a study of lavender and its relation to falls in the elderly  [3] . Those receiving the lavender had a patch with the scent whereas controls were given a placebo patch. However, there is a risk that the patients themselves and their caregivers would also detect the lavender scent of those receiving the active intervention. This shatters the blinding process and hence, bias is then introduced either consciously or subconsciously.

Reports have found that less than 25% of RCTs adequately report blinding  [4] . Moreover, the terms used for blinding have many different interpretations and are not universally defined  [5] . Therefore, the CONSORT guidelines for the optimal reporting of RCTs recommends that blinding should not only be robust, but that the manuscript must explicitly state the method of blinding used as well as which parties in the trial were blinded  [6,7] . It is not enough to simply state the trial is ‘double-blinded’ without further elaboration.

It is notoriously difficult to use blinding in surgical RCTs. This is because the patient is likely to know whether they have been cut open or not. To avoid this, often the tested intervention is an internal surgical technique or the wound is covered using the same large dressing and hence the patients are unaware whether or not they have received an active intervention.

However, it can be difficult to blind the surgeon to the tested intervention as they must perform the procedure. This still remains a challenge in medical research. (Although it is not impossible to blind surgeons: have a look at this  for more information).  It is recommended that the groups are treated as equally as possible, blinding should be attempted in other areas of the study and that any lack of blinding should be recognised in the limitations section [8]. This allows clinicians and other decision-makers to accurately appraise the study when deciding whether to use its results to inform their medical practice.

Why is blinding important?

Blinding is important for the validity of RCTs. Without it there are number of biases that are unwillingly introduced. It has been shown in an assessment of 33 meta-analyses, encompassing 250 RCTs, that without blinding, odds ratios were exaggerated by up to 17% (P=0.01) . This highlights the importance of not just reading medical literature, but appraising it with a critical lens  [9] . This is supported by other analyses of an array of methodological aspects of RCTs  [10] .

Knowledge of group allocations may affect behaviour in the trial. For example, researchers may assess patients who are receiving the active intervention more closely than those receiving the control and hence be more likely to pick up on beneficial and/or adverse effects. Similarly, if patients are aware that they are receiving the active intervention they may be more likely to check for side effects and be more likely to drop out.

Furthermore, there is a possibility that if the data collectors or statisticians are aware of treatment allocation then they may also provide a biased assessment of the group outcomes. Even if these people are trustworthy, differences can occur subconsciously, as part of human nature. To eliminate this and ensure independence, it is best that these people are also blinded in an RCT. The trend of going beyond ‘double-blinding’ to ‘triple-blinding’ is becoming more popular, as awareness of the methodological pitfalls in clinical research is improving.

Although more logistically challenging, it is best to blind participants, clinicians/researchers, data collectors and statisticians/data analysts . Ensuring everyone is unaware of treatment allocations minimises bias.

What about when blinding is not possible?

In cases where blinding is not possible or feasible, the outcome measures must be objective! If you are reading a study that is un-blinded, with subjective outcome measures, then you may as well stop reading it and move on. This is because, if a patient is aware they are receiving the active intervention and the outcome measure is subjective, such as ‘how much pain they are experiencing’, their reporting is likely to be biased. Knowledge of the group assignment can consciously or subconsciously cause the patient to feel better and report improved subjective pain tolerance. This is not a reliable study design and the results should not be interpreted with any certainty.

It should be emphasised that the use of objective outcome measures are not a replacement for robust blinding in clinical trials. Blinding should be used wherever possible.

Allocation concealment

Allocation concealment is ensuring that the person(s) randomising participants does not know what the next treatment allocation must be.  This is an often underappreciated aspect of many trials that may lead to significant selection bias and invalid conclusions if not implemented  [6,7].

CONSORT guidelines recommend that all RCTs have a robust method of randomisation to ensure its validity and minimise bias  [6,7] . Even otherwise well-designed studies can be undermined if proper allocation concealment is not performed. If the randomisation sequence is widely available to the researchers then this can influence who is recruited.

For example, if the doctor is in clinic, they may believe a patient may not perform well with the experimental drug. As a result, the clinician may ‘skip’ this patient and not recruit them. Similarly, if the clinician knows a particular patient that they think will be ‘good’ for the experimental drug group, and the allocation sequence is known, they may ‘skip’ the prior patients so that the patient they want is assigned to the experimental drug. These scenarios compromise the randomisation process and introduce bias.

The traditional method of using sealed envelopes for allocation is not robust. It has been documented that researchers have used a bright light to see through these envelopes, or that the envelopes have been opened prior to a clinic starting. Instead, best-practice recommends the use of external randomisation sequencing and delivery of the allocation via telephone. This prevents researchers from potentially influencing the allocations.

• Blinding is an important foundation for ensuring internal validity and reducing observer bias
• Blinding must be robust and methods of blinding should be reported in detail
• Where possible, RCTs should be at least double-blinded, and should have more blinding where possible (this includes: patients, clinicians/researchers, data collectors and statisticians)
• Where blinding is not feasible, this should be recognised as a limitation and blinding should be attempted in other areas of the trial
• Outcome measures should ideally be objective, particularly when blinding is not possible
• Allocation concealment is important to prevent allocation bias
• Allocation concealment should ideally be performed using an external, independent telephone system

References (pdf)

Saul Crandon

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No Comments on Blinding: A detailed guide for students

This is such a great post

thanks for share your valuable posting.

The article was very interested .

Hello mr crandon. Is it feasible we set up a before- after clinical trial which we blinde the researcher not patient(single blind) and our control consume traditional medication and case get new medication? Will this study valid or we must blind both of patients and researcher? Best regard.

Dear Ali. I’m sorry for the delay in replying to you. One of my colleagues has suggested this article: Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700. doi:10.1016/s0140-6736(02)07816-9 as being a useful guide for you. It is, unfortunately, behind a paywall, but I hope you have access through your institution. I hope this provides some clarity to your situation. Many thanks. Emma.

The article was very easy to read and easily understood. I appreciate that because it makes the learning enjoyable as well. Very interesting article

please very interested in all biostats topics, keep me updated

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• Clinical Research Explained

Double-Blind Study

• 8. February 2024

In the realm of clinical research, a double-blind study is a critical method used to eliminate bias and ensure the validity of results . This method involves both the researcher and the participant being unaware of the treatment or intervention being administered. This glossary entry will delve into the intricate details of double-blind studies, their importance, how they are conducted, and their advantages and disadvantages.

Understanding the concept of double-blind studies is crucial for anyone involved in or studying clinical research. It forms the backbone of many studies and trials, particularly in the field of medicine. This entry aims to provide comprehensive knowledge about double-blind studies, making it an invaluable resource for students, researchers, and professionals alike.

Definition of Double-Blind Study

A double-blind study is a type of research design where both the investigator and the participant are unaware of the treatment or intervention being given. The term ‘double-blind’ refers to the fact that both the participant (first ‘blind’) and the researcher (second ‘blind’) do not know which treatment is being administered. This is done to prevent bias in the results.

The double-blind study is a gold standard in clinical research, particularly in randomized controlled trials (RCTs). It is often used in drug testing to ensure that the results are due to the effectiveness of the drug and not influenced by the placebo effect or the expectations of the researcher or participant.

Origin of Double-Blind Studies

The concept of double-blind studies originated in the mid-20th century . The need for such a study design arose from the understanding that bias could significantly affect the results of a study. The double-blind method was seen as a way to control for these biases and ensure more accurate and reliable results.

Over time, the double-blind study design has become a cornerstone of clinical research. It is now widely accepted as the most reliable method of testing the efficacy of new treatments and interventions.

Importance of Double-Blind Studies

Double-blind studies play a vital role in clinical research. They help to eliminate bias, which can significantly influence the results of a study. By keeping both the researcher and participant unaware of the treatment being administered, the study can focus solely on the effects of the treatment itself.

Without double-blind studies, the results of clinical research could be skewed by factors such as the placebo effect, where a participant’s belief in the treatment can influence their perception of its effectiveness. Similarly, researcher bias could also affect the results, as the researcher may unconsciously influence the participant or interpret the results in a way that favors their hypothesis.

Elimination of Bias

One of the main reasons for conducting double-blind studies is to eliminate bias. Bias can come from various sources and can significantly affect the results of a study. By keeping both the researcher and participant blind to the treatment, double-blind studies help to control for these biases.

For example, if a participant knows they are receiving a placebo, they may report fewer improvements due to their belief that the treatment is not real. Similarly, if a researcher knows which participants are receiving the treatment, they may unconsciously influence the participants or interpret the results in a way that supports their hypothesis.

Enhancement of Credibility

Double-blind studies also enhance the credibility of clinical research . By eliminating bias, these studies produce results that are more likely to be accurate and reliable. This increases the trustworthiness of the research and makes it more likely to be accepted by the scientific community and the public.

Furthermore, double-blind studies can also increase the likelihood of a study being published in a peer-reviewed journal. These journals often require rigorous methodology, and double-blind studies are seen as a strong indicator of high-quality research.

Conducting a Double-Blind Study

Conducting a double-blind study involves several steps. First, the participants are randomly assigned to either the treatment group or the control group. The treatment group receives the intervention being tested, while the control group receives a placebo or standard treatment.

Next, both the researcher and the participant are kept unaware of the group assignment. This is usually done using coded or numbered treatments that are identical in appearance. Only a third party, not involved in the data collection or analysis, knows the code.

Randomization

Randomization is a crucial step in conducting a double-blind study. It involves randomly assigning participants to the treatment or control group. This ensures that any differences in outcomes between the groups are due to the treatment and not other factors.

Randomization helps to control for confounding variables, which are factors other than the treatment that could influence the results. By randomly assigning participants to groups, these variables are equally distributed between the groups, reducing their potential impact on the results.

Blinding is the process of keeping the researcher and participant unaware of the treatment being administered. This is usually done using coded or numbered treatments that look identical. The code is kept by a third party and is only revealed after the data has been collected and analyzed.

Blinding helps to prevent bias in the results. If the researcher or participant knows which treatment is being given, they may be influenced by their expectations or beliefs about the treatment. By keeping them blind to the treatment, the study can focus solely on the effects of the treatment itself.

Advantages and Disadvantages of Double-Blind Studies

Double-blind studies offer several advantages. They help to eliminate bias, increase the credibility of the research, and produce more accurate and reliable results. However, they also have some disadvantages, such as the potential for unblinding and the difficulty and cost of implementing the double-blind design.

Despite these disadvantages, double-blind studies are still considered the gold standard in clinical research. The benefits they offer in terms of eliminating bias and enhancing credibility far outweigh the potential drawbacks.

The main advantage of double-blind studies is their ability to eliminate bias. By keeping both the researcher and participant unaware of the treatment, these studies help to control for biases that could influence the results. This leads to more accurate and reliable results, increasing the validity of the research.

Double-blind studies also enhance the credibility of the research. By using a rigorous methodology, these studies produce results that are more likely to be accepted by the scientific community and the public. This can increase the impact of the research and lead to greater recognition and funding for the researcher.

Disadvantages

One of the main disadvantages of double-blind studies is the potential for unblinding. This occurs when the researcher or participant becomes aware of the treatment being administered. Unblinding can introduce bias into the study and undermine the validity of the results.

Another disadvantage is the difficulty and cost of implementing the double-blind design. Creating identical treatments, coding them, and ensuring that the code is kept secret can be challenging and expensive. However, many researchers believe that the benefits of double-blind studies outweigh these potential drawbacks.

Double-blind studies are a crucial part of clinical research. They provide a rigorous methodology that helps to eliminate bias and increase the credibility of the research. Despite some potential disadvantages, they are widely accepted as the gold standard in clinical research.

Understanding the concept of double-blind studies is essential for anyone involved in or studying clinical research. This glossary entry has provided a comprehensive overview of double-blind studies, including their definition, importance, how they are conducted, and their advantages and disadvantages. It is hoped that this entry will serve as a valuable resource for students, researchers, and professionals alike.

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• Blinding in clinical...

Blinding in clinical trials and other studies

• Related content
• Peer review
• Simon J Day a , manager, clinical biometrics ,
• Douglas G Altman b , professor of statistics in medicine
• a Leo Pharmaceuticals, Princes Risborough, Buckinghamshire HP27 9RR
• b ICRF Medical Statistics Group, Institute of Health Sciences, Oxford OX3 7LF
• Correspondence to: S J Day

Human behaviour is influenced by what we know or believe. In research there is a particular risk of expectation influencing findings, most obviously when there is some subjectivity in assessment, leading to biased results. Blinding (sometimes called masking) is used to try to eliminate such bias.

It is a tenet of randomised controlled trials that the treatment allocation for each patient is not revealed until the patient has irrevocably been entered into the trial, to avoid selection bias. This sort of blinding, better referred to as allocation concealment, will be discussed in a future statistics note. In controlled trials the term blinding, and in particular “double blind,” usually refers to keeping study participants, those involved with their management, and those collecting and analysing clinical data unaware of the assigned treatment, so that they should not be influenced by that knowledge.

The relevance of blinding will vary according to circumstances. Blinding patients to the treatment they have received in a controlled trial is particularly important when the response criteria are subjective, such as alleviation of pain, but less important for objective criteria, such as death. Similarly, medical staff caring for patients in a randomised trial should be blinded to treatment allocation to minimise possible bias in patient management and in assessing disease status. For example, the decision to withdraw a patient from a study or to adjust the dose of medication could easily be influenced by knowledge of which treatment group the patient has been assigned to.

In a double blind trial neither the patient nor the caregivers are aware of the treatment assignment. Blinding means more than just keeping the name of the treatment hidden. Patients may well see the treatment being given to patients in the other treatment group(s), and the appearance of the drug used in the study could give a clue to its identity. Differences in taste, smell, or mode of delivery may also influence efficacy, so these aspects should be identical for each treatment group. Even colour of medication has been shown to influence efficacy. 1

In studies comparing two active compounds, blinding is possible using the “double dummy” method. For example, if we want to compare two medicines, one presented as green tablets and one as pink capsules, we could also supply green placebo tablets and pink placebo capsules so that both groups of patients would take one green tablet and one pink capsule.

Blinding is certainly not always easy or possible. Single blind trials (where either only the investigator or only the patient is blind to the allocation) are sometimes unavoidable, as are open (non-blind) trials. In trials of different styles of patient management, surgical procedures, or alternative therapies, full blinding is often impossible.

In a double blind trial it is implicit that the assessment of patient outcome is done in ignorance of the treatment received. Such blind assessment of outcome can often also be achieved in trials which are open (non-blinded). For example, lesions can be photographed before and after treatment and assessed by someone not involved in running the trial. Indeed, blind assessment of outcome may be more important than blinding the administration of the treatment, especially when the outcome measure involves subjectivity. Despite the best intentions, some treatments have unintended effects that are so specific that their occurrence will inevitably identify the treatment received to both the patient and the medical staff. Blind assessment of outcome is especially useful when this is a risk.

In epidemiological studies it is preferable that the identification of “cases” as opposed to “controls” be kept secret while researchers are determining each subject's exposure to potential risk factors. In many such studies blinding is impossible because exposure can be discovered only by interviewing the study participants, who obviously know whether or not they are a case. The risk of differential recall of important disease related events between cases and controls must then be recognised and if possible investigated. 2 As a minimum the sensitivity of the results to differential recall should be considered. Blinded assessment of patient outcome may also be valuable in other epidemiological studies, such as cohort studies.

Blinding is important in other types of research too. For example, in studies to evaluate the performance of a diagnostic test those performing the test must be unaware of the true diagnosis. In studies to evaluate the reproducibility of a measurement technique the observers must be unaware of their previous measurement(s) on the same individual.

We have emphasised the risks of bias if adequate blinding is not used. This may seem to be challenging the integrity of researchers and patients, but bias associated with knowing the treatment is often subconscious. On average, randomised trials that have not used appropriate levels of blinding show larger treatment effects than blinded studies. 3 Similarly, diagnostic test performance is overestimated when the reference test is interpreted with knowledge of the test result. 4 Blinding makes it difficult to bias results intentionally or unintentionally and so helps ensure the credibility of study conclusions.

• De Craen AJM ,
• de Vries AL ,
• Schulz KF ,
• Chalmers I ,
• Lijmer JG ,
• Heisterkamp S ,
• Bonsel GJ ,
• van der Meulen JH

Frequently asked questions

What is the difference between single-blind, double-blind and triple-blind studies.

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

Frequently asked questions: Methodology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Snowball sampling is best used in the following cases:

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

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

Reproducibility and replicability are related terms.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are two subtypes of construct validity.

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

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

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

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

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

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

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

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

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

Overall, your focus group questions should be:

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

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

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

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

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

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

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

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

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

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

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

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

Unstructured interviews are best used when:

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

The four most common types of interviews are:

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

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

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

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

Deductive reasoning is also called deductive logic.

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

Here are a few common types:

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

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

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

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

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

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

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

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

Triangulation can help:

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

But triangulation can also pose problems:

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

There are four main types of triangulation :

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

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

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

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

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

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

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

These are four of the most common mixed methods designs :

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

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

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

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

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

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

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

Correlation coefficients always range between -1 and 1.

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

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Systematic error is generally a bigger problem in research.

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

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

Random and systematic error are two types of measurement error.

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

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

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

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

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

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

The difference between explanatory and response variables is simple:

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

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

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

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

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

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

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

In a factorial design, multiple independent variables are tested.

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

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

Advantages:

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

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

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

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

If something is a mediating variable :

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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

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

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

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

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

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

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

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

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

There are five common approaches to qualitative research :

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

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

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

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

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

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

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

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

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

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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

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

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

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

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

When designing the experiment, you decide:

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

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

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

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

The validity of your experiment depends on your experimental design .

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

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

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

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

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

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

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

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

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

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

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

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What Is a Double Blind Experiment?

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In many experiments, there are two groups: a control group and an experimental group . The members of the experimental group receive the particular treatment being studied, and the members of the control group do not receive the treatment. Members of these two groups are then compared to determine what effects can be observed from the experimental treatment. Even if you do observe some difference in the experimental group, one question you may have is, “How do we know that what we observed is due to the treatment?”

When you ask this question, you are really considering the possibility of lurking variables . These variables influence the response variable but do so in a way that is difficult to detect. Experiments involving human subjects are especially prone to lurking variables. Careful experimental design will limit the effects of lurking variables. One particularly important topic in the design of experiments is called a double-blind experiment.

Humans are marvelously complicated, which makes them difficult to work with as subjects for an experiment. For instance, when you give a subject an experimental medication and they exhibit signs of improvement, what is the reason? It could be the medicine, but there could also be some psychological effects. When someone thinks they are being given something that will make them better, sometimes they will get better. This is known as the placebo effect .

To mitigate any psychological effects of the subjects, sometimes a placebo is given to the control group. A placebo is designed to be as close to the means of administration of the experimental treatment as possible. But the placebo is not the treatment. For example, in the testing of a new pharmaceutical product, a placebo could be a capsule that contains a substance that has no medicinal value. By use of such a placebo, subjects in the experiment would not know whether they were given medication or not. Everyone, in either group, would be as likely to have psychological effects of receiving something that they thought was medicine.

Double Blind

While the use of a placebo is important, it only addresses some of the potential lurking variables. Another source of lurking variables comes from the person who administers the treatment. The knowledge of whether a capsule is an experimental drug or actually a placebo can affect a person’s behavior. Even the best doctor or nurse may behave differently toward an individual in a control group versus someone in an experimental group. One way to guard against this possibility is to make sure that the person administering the treatment does not know whether it is the experimental treatment or the placebo.

An experiment of this type is said to be double blind. It is called this because two parties are kept in the dark about the experiment. Both the subject and the person administering the treatment do not know whether the subject in the experimental or control group. This double layer will minimize the effects of some lurking variables.

Clarifications

It is important to point out a few things. Subjects are randomly assigned to the treatment or control group, have no knowledge of what group they are in and the people administering the treatments have no knowledge of which group their subjects are in. Despite this, there must be some way of knowing which subject is in which group. Many times this is achieved by having one member of a research team organize the experiment and know who is in which group. This person will not interact directly with the subjects, so will not influence their behavior.

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Double-Blind Study – Definition and Examples

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Bias is one of the greatest barriers to effective research, so double-blind study designs have become a core part of gold-standard trials in methodology . This design blinds the researcher and the subject until the end of the trial, thereby preventing certain types of research bias in either party. This article will cover any important information about double blinding, its types, uses, and effects.

Inhaltsverzeichnis

• 1 In a Nutshell: Double-Blind Study
• 2 Definition: Double-Blind Study
• 3 Types of blind studies
• 4 The importance of a double-blind study

In a Nutshell: Double-Blind Study

A double-blind study is a study where neither the participants nor the researcher knows who is in the treatment group and who is in the control group.

Definition: Double-Blind Study

The double-blind study design masks information from both researchers and subjects to reduce the odds of bias . Blinding reduces the chance of test subjects developing placebo effects, while preventing researchers from consciously or unconsciously altering their participants’ perceptions. This way, studies an be replicated easily without losing validity.

Masking also reduces the chance of test subjects developing placebo and nocebo effects, while preventing researchers from consciously or unconsciously altering their participants’ perceptions.

Even so, double-blind study procedures aren’t the zenith of masking. Trials can be triple or even quadruple-blind.

Types of blind studies

Generally, there are three common types of blinding applied in research:

• In single-blind studies , only the participants do not know which group they are in . The researcher or doctor in clinical studies knows which treatment you are receiving.

An example of a single blinded study is used mainly when it involves only a one-time event, like a vaccination. The risk of the researcher or doctor influencing the participants is rather low due to the rare contact between them.

• Double-blind studies are the most common variation, meaning that neither the participants nor the researcher or doctor know who is in which group. Only the data analysts will know.

In another study, participants might be monitored over a longer period of time and with closer contact with the researcher or doctor. This increases the risk of the researcher giving away information about the treatment through his words or behavior, or he will interpret symptoms differently due to his knowledge about the treatment. This is why studies like this are mostly double-blinded.

• In a triple-blinded study , the participants, researchers, and data analysts do not know who is treated how. Participants are assigned into groups 1 and 2 and the data is analyzed obliviously. This type of study is used very rarely due to its complicated procedure, but it may help in cases, where data analysts might be influenced by bias or prejudices.

The importance of a double-blind study

Double-blind studies are important in order to avoid research bias and unblinding in a study. When neither the researcher nor the participants know which treatment they are receiving, the inner validity of the study is protected.

They can also lead to biased reporting and alter the way doctors perform disease assessments.

Bias can be both intentional and accidental, so pure motives aren’t enough. Double blinding protects the integrity of the researcher and lends weight to their results, providing a smoother peer review process .

Impossible blinding

While it can be difficult to conduct a double-blind study, if the experiment is only single blinded, it is more likely that participants will find out about their assigned group and thus alternate their behavior. If the participant finds out about receiving the real treatment or fake treatment, this is called “unblinding”.

If your participant experiences symptoms that could be side effects of the medication, then they might be actively unblinded to protect them.

It is more common, however, that the researcher themselves gives away the information by, for example, showing his expectations through his behavior. If the researcher of a medical study might ask the experimental group more thoroughly about possible side effects and those participants talk to the control group, they might get a hint at who is receiving the real treatment.

In other cases, however, blinding might be impossible, especially in physical treatment . You may be able to give placebo sugar pills to participants, but you cannot fake physical exercises or treatments without affecting the individuals in any way.

A toothpick is often used instead of an acupuncture needle. This leaves the participant’s blind intact but makes the researcher’s blind impossible.

What are the types of blind studies?

Besides the double-blind study, where neither the researcher nor the participants know who is in which group, there are also the single-blind study and the triple-blind study. In a single-blind study, only the participants are blinded while in a triple-blind study, even the data analysts do not know about the treatment each person receives.

Who knows about the treatment of each group in a double-blind study?

In a double-blind study, only the director of the research and the independent data analysts know who receives which treatment.

Where is it impossible to conduct a double-blind study?

While double-blind studies are widely used in research, in cases where there is physical treatment involved or in psychological studies where the researcher bases his data on the personal interaction with participants, double-blinding is hardly possible.

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Experimental vs Observational Studies: Differences & Examples

Understanding the differences between experimental vs observational studies is crucial for interpreting findings and drawing valid conclusions. Both methodologies are used extensively in various fields, including medicine, social sciences, and environmental studies. 

Researchers often use observational and experimental studies to gather comprehensive data and draw robust conclusions about their investigating phenomena. 

This blog post will explore what makes these two types of studies unique, their fundamental differences, and examples to illustrate their applications.

What is an Experimental Study?

An experimental study is a research design in which the investigator actively manipulates one or more variables to observe their effect on another variable. This type of study often takes place in a controlled environment, which allows researchers to establish cause-and-effect relationships.

Key Characteristics of Experimental Studies:

• Manipulation: Researchers manipulate the independent variable(s).
• Control: Other variables are kept constant to isolate the effect of the independent variable.
• Randomization: Subjects are randomly assigned to different groups to minimize bias.
• Replication: The study can be replicated to verify results.

Types of Experimental Study

• Laboratory Experiments: Conducted in a controlled environment where variables can be precisely controlled.
• Field Research : These are conducted in a natural setting but still involve manipulation and control of variables.
• Clinical Trials: Used in medical research and the healthcare industry to test the efficacy of new treatments or drugs.

Example of an Experimental Study:

Imagine a study to test the effectiveness of a new drug for reducing blood pressure. Researchers would:

• Randomly assign participants to two groups: receiving the drug and receiving a placebo.
• Ensure that participants do not know their group (double-blind procedure).
• Measure blood pressure before and after the intervention.
• Compare the changes in blood pressure between the two groups to determine the drug’s effectiveness.

What is an Observational Study?

An observational study is a research design in which the investigator observes subjects and measures variables without intervening or manipulating the study environment. This type of study is often used when manipulating impractical or unethical variables.

Key Characteristics of Observational Studies:

• No Manipulation: Researchers do not manipulate the independent variable.
• Natural Setting: Observations are made in a natural environment.
• Causation Limitations: It is difficult to establish cause-and-effect relationships due to the need for more control over variables.
• Descriptive: Often used to describe characteristics or outcomes.

Types of Observational Studies: 

• Cohort Studies : Follow a control group of people over time to observe the development of outcomes.
• Case-Control Studies: Compare individuals with a specific outcome (cases) to those without (controls) to identify factors that might contribute to the outcome.
• Cross-Sectional Studies : Collect data from a population at a single point to analyze the prevalence of an outcome or characteristic.

Example of an Observational Study:

Consider a study examining the relationship between smoking and lung cancer. Researchers would:

• Identify a cohort of smokers and non-smokers.
• Follow both groups over time to record incidences of lung cancer.
• Analyze the data to observe any differences in cancer rates between smokers and non-smokers.

Difference Between Experimental vs Observational Studies

Choosing Between Experimental and Observational Studies

The researchers relied on statistical analysis to interpret the results of randomized controlled trials, building upon the foundations established by prior research.

Use Experimental Studies When:

• Causality is Important: If determining a cause-and-effect relationship is crucial, experimental studies are the way to go.
• Variables Can Be Controlled: When you can manipulate and control the variables in a lab or controlled setting, experimental studies are suitable.
• Randomization is Possible: When random assignment of subjects is feasible and ethical, experimental designs are appropriate.

Use Observational Studies When:

• Ethical Concerns Exist: If manipulating variables is unethical, such as exposing individuals to harmful substances, observational studies are necessary.
• Practical Constraints Apply: When experimental studies are impractical due to cost or logistics, observational studies can be a viable alternative.
• Natural Settings Are Required: If studying phenomena in their natural environment is essential, observational studies are the right choice.

Strengths and Limitations

Experimental studies.

• Establish Causality: Experimental studies can establish causal relationships between variables by controlling and using randomization.
• Control Over Confounding Variables: The controlled environment allows researchers to minimize the influence of external variables that might skew results.
• Repeatability: Experiments can often be repeated to verify results and ensure consistency.

Limitations:

• Ethical Concerns: Manipulating variables may be unethical in certain situations, such as exposing individuals to harmful conditions.
• Artificial Environment: The controlled setting may not reflect real-world conditions, potentially affecting the generalizability of results.
• Cost and Complexity: Experimental studies can be costly and logistically complex, especially with large sample sizes.

Observational Studies

• Real-World Insights: Observational studies provide valuable insights into how variables interact in natural settings.
• Ethical and Practical: These studies avoid ethical concerns associated with manipulation and can be more practical regarding cost and time.
• Diverse Applications: Observational studies can be used in various fields and situations where experiments are not feasible.
• Lack of Causality: It’s easier to establish causation with manipulation, and results are limited to identifying correlations.
• Potential for Confounding: Uncontrolled external variables may influence the results, leading to biased conclusions.
• Observer Bias: Researchers may unintentionally influence outcomes through their expectations or interpretations of data.

Examples in Various Fields

• Experimental Study: Clinical trials testing the effectiveness of a new drug against a placebo to determine its impact on patient recovery.
• Observational Study: Studying the dietary habits of different populations to identify potential links between nutrition and disease prevalence.
• Experimental Study: Conducting a lab experiment to test the effect of sleep deprivation on cognitive performance by controlling sleep hours and measuring test scores.
• Observational Study: Observing social interactions in a public setting to explore natural communication patterns without intervention.

Environmental Science

• Experimental Study: Testing the impact of a specific pollutant on plant growth in a controlled greenhouse setting.
• Observational Study: Monitoring wildlife populations in a natural habitat to assess the effects of climate change on species distribution.

How QuestionPro Research Can Help in Experimental vs Observational Studies

Choosing between experimental and observational studies is a critical decision that can significantly impact the outcomes and interpretations of a study. QuestionPro Research offers powerful tools and features that can enhance both types of studies, giving researchers the flexibility and capability to gather, analyze, and interpret data effectively.

Enhancing Experimental Studies with QuestionPro

Experimental studies require a high degree of control over variables, randomization, and, often, repeated trials to establish causal relationships. QuestionPro excels in facilitating these requirements through several key features:

• Survey Design and Distribution: With QuestionPro, researchers can design intricate surveys tailored to their experimental needs. The platform supports random assignment of participants to different groups, ensuring unbiased distribution and enhancing the study’s validity.
• Data Collection and Management: Real-time data collection and management tools allow researchers to monitor responses as they come in. This is crucial for experimental studies where data collection timing and sequence can impact the results.
• Advanced Analytics: QuestionPro offers robust analytical tools that can handle complex data sets, enabling researchers to conduct in-depth statistical analyses to determine the effects of the experimental interventions.

Supporting Observational Studies with QuestionPro

Observational studies involve gathering data without manipulating variables, focusing on natural settings and real-world scenarios. QuestionPro’s capabilities are well-suited for these studies as well:

• Customizable Surveys: Researchers can create detailed surveys to capture a wide range of observational data. QuestionPro’s customizable templates and question types allow for flexibility in capturing nuanced information.
• Mobile Data Collection: For field research, QuestionPro’s mobile app enables data collection on the go, making it easier to conduct studies in diverse settings without internet connectivity.
• Longitudinal Data Tracking: Observational studies often require data collection over extended periods. QuestionPro’s platform supports longitudinal studies, allowing researchers to track changes and trends.

Experimental and observational studies are essential tools in the researcher’s toolkit. Each serves a unique purpose and offers distinct advantages and limitations. By understanding their differences, researchers can choose the most appropriate study design for their specific objectives, ensuring their findings are valid and applicable to real-world situations.

Whether establishing causality through experimental studies or exploring correlations with observational research designs, the insights gained from these methodologies continue to shape our understanding of the world around us. 

Whether conducting experimental or observational studies, QuestionPro Research provides a comprehensive suite of tools that enhance research efficiency, accuracy, and depth. By leveraging its advanced features, researchers can ensure that their studies are well-designed, their data is robustly analyzed, and their conclusions are reliable and impactful.

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What is a Double-Blind Trial?

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When drugs or vaccines are being trialed for their effectiveness, there are typically several stages. Double-blind trials are seen as the most reliable type of study because they involve neither the participant nor the doctor knowing who has received what treatment. The aim of this is to minimize the placebo effect and minimize bias.

How they work

In double-blind trials, the treatment patients have is unknown to both patients and doctors until after the study is concluded. This differs from other types of trials, such as simple blind trials where only the patients are unaware of the treatment they are receiving, whereas the doctors know.

Double-blind trials are a form of randomized trials and can be ‘upgraded’ to triple-blind trials, in which the statisticians or data clean-up personnel are also blind to treatments.

To be effective, it is generally recommended that double-blind trials include around 100-300 people. If treatments are highly effective, smaller numbers can be used but if only 30 or so patients are enrolled the study is unlikely to be beneficial.

The assignment of patients into treatments is typically done by computers, where the computer assigns each patient a code number and treatment group. The doctor and patients only know the code number to avoid bias, hence allowing the study to be double-blind.

Double-blind trials can come in different varieties. Double-blind, placebo-controlled studies involve no one knowing the treatment assignments to remove the chance of placebo effects. In a double-blind comparative trial, a new treatment is often compared to the standard drug. This allows researchers to compare an established drug to a new one to establish which one is more advantageous.

However, unlike double-blind, placebo-controlled trials, they are not very good at statistically evaluating if a treatment is effective overall.

Benefits of double-blind trials

Double-blind trials remove any power of suggestion, as no one involved knows the treatment patients receive. This means that doctors carrying out the study do not know and cannot accidentally tip off participants. Similarly, the doctors not being aware of the treatments means they do not unconsciously bias their interpretation of the study results.

The main principle behind double-blind and randomized trials, as opposed to simple blind trials, is to avoid bias in the treatment or experimental set-up. For example, if researchers are aware of the different treatment groups are getting, they may avoid assigning more unwell patients to the treatment group. Therefore, any effect seen by the treatment may have been related to how unwell a patient was to start with, rather than the efficacy of the drug.

COVID-19 and double-blind trials

Double-blind trials are usually needed for drugs and treatments to get approval to be used in many countries. However, good, comprehensive double-blind trials take time and require many participants. This has been especially problematic during the COVID-19 pandemic, as the world has searched for pharmaceutical treatment options to improve survival and for vaccines to prevent the spread of this virus.

In terms of treatment, many drugs have been tested in double-blind trials. The antiviral nucleoside analog remdesivir has been tested in several double-blind trials and was the first drug to gain full FDA approval for use against COVID-19 in October 2020.

However, the results of trials have been conflicting, and some experts remained unconvinced of its benefits. In November 2020, the World Health Organization recommended against the use of the drug for COVID-19 and a global randomized trial came to the conclusion in February 2021 that remdesivir has little to no effect when used on hospitalized COVID-19 patients. The drug is still used in the US.

Multiple candidates for a COVID-19 vaccine have been identified and moved on to phase II and phase III trials, which often involve double-blind methods. These need to be conducted over meaningful timeframes to ensure any initial differences between the control and the treatment groups last in the long term.

Several different vaccines are now available (March 2021) due to mixed approval and emergency approval by governments and organizations. This has been an exceptional time for vaccine trials as the typical course of development has been sped up. What would usually take years has taken months.

Many countries have given limited or early approval to vaccines for emergency use before detailed phase III data has been publicized, based on preliminary evidence of effectivity and safety. This comes with some risks.

Another topic of discussion that has come about as a result of COVID-19 is the ethics of keeping patients blind during the trial as vaccine effectivity is supported. Whilst keeping the blind aspect is essential to achieving valuable and reliable information about long-term effects, there is an argument that blind participants who have received a placebo should be able to receive a vaccine as more become available.

• Cancer Research UK. 2019. Randomized Trials . [online] Available at: <https://www.cancerresearchuk.org/find-a-clinical-trial/what-clinical-trials-are/randomised-trials> [Accessed 25 July 2020].
• European Centre for Disease Prevention and Control. 2020. Vaccines And Treatment Of COVID-19 . [online] Available at: <https://www.ecdc.europa.eu/en/covid-19/latest-evidence/vaccines-and-treatment> [Accessed 25 July 2020].
• Misra, S., 2012. Randomized double-blind placebo control studies, the "Gold Standard" in intervention-based studies. Indian Journal of Sexually Transmitted Diseases and AIDS , 33(2), pp. 131.
• The New York Times. 2021. Coronavirus Drug and Treatment Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-drugs-treatments.html [Accessed 11 March 2020]
• The New York Times. 2021. Coronavirus Vaccine Tracker [online] Available at https://www.nytimes.com/interactive/2020/science/coronavirus-vaccine-tracker.html [Accessed 11 March 2020]
• Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., Fu, S., Gao, L., Cheng, Z., Lu, Q., Hu, Y., Luo, G., Wang, K., Lu, Y., Li, H., Wang, S., Ruan, S., Yang, C., Mei, C., Wang, Y., Ding, D., Wu, F., Tang, X., Ye, X., Ye, Y., Liu, B., Yang, J., Yin, W., Wang, A., Fan, G., Zhou, F., Liu, Z., Gu, X., Xu, J., Shang, L., Zhang, Y., Cao, L., Guo, T., Wan, Y., Qin, H., Jiang, Y., Jaki, T., Hayden, F., Horby, P., Cao, B. and Wang, C., 2020. Remdesivir in adults with severe COVID-19: a randomized, double-blind, placebo-controlled, multicentre trial. The Lancet , 395(10236), pp. 1569-1578.
• Winchesterhospital.org. 2020. Double-Blind Study . [online] Available at: <https://www.winchesterhospital.org/health-library/article?id=21861> [Accessed 25 July 2020].
• WHO Ad Hoc Expert Group on the Next Steps for COVID-19 Evaluation. 2021. Placebo-Controlled Trials of Covid-19 Vaccines — Why We Still Need Them. N Engl J Med, 384:e2.

Last Updated: Mar 19, 2021

Sara Ryding

Sara is a passionate life sciences writer who specializes in zoology and ornithology. She is currently completing a Ph.D. at Deakin University in Australia which focuses on how the beaks of birds change with global warming.

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Examples of a Double Blinded Study Experiment

A double-blind study is a study in which both the person implementing the experiment and the participant(s) are not aware of which individual is receiving the experimental treatment. The purpose of a double-blind experiment is to ensure that the results are not biased. This approach is frequently used in the research field by not only scientists and psychologists but also in the legal process. The benefits of this type of study is the increase in reliability and validity of the experiment.

Medication Experiments

A double-blind experiment is beneficial when testing a specific medication. Half of the participants are given the medication, and the remaining participants are given a placebo. A placebo is an inactive substance such as a sugar pill that looks identical to the medication. By utilizing a placebo, the study remains free from being altered as neither the participants nor the researcher knows who is receiving the medication. This provides valid and reliable results.

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Is moonlight strong enough to power solar panels, what is an instrument that measures the pressure of a gas or vapor, what is the next step if an experiment fails to confirm your hypothesis, science experiments on which colors of candles burn the fastest, the difference between a true experiment & a correlational study in psychology, taste testing.

We have all seen the commercials where the individual is asked to determine which beverage tastes better. Even given the best controls on the experiment, the experimenter might push one soda closer to the participant, or make sure that one drink is colder than the other. By completing these types of moves, the participant may be skewed which can cause invalid results. Using the double-blind approach, none of the individuals involved in the study are told which cup contains the targeted soda.

Computer Generated Survey

A survey is another method utilized by researchers when preforming experiments. Computer-generated surveys are double-blind experiments as the participant is completing the survey online and unaware of the researcher's targeted hypothesis. The researcher is not aware of who is participating in the experiment. In this scenario, since the participant is unknown, the study is free from interviewer bias.

Forensic Application

In police work, the officer will show a set of photos to the witness and ask that the witness identify the suspect. This is referred to as a single-blind test. The issue with this approach is the officer may have subtly influenced the witness' identification. Some law enforcement agencies are implementing a double-blind procedure to ensure identification accuracy. In this approach, both the officer and the witness do not know who the suspect is during the identification process.

• Education: Reference: Double Blind Experiments
• CBS Baltimore: Double Blind Photo Lineups To Curb False Identification in Baltimore City
• Columbia University: Glossary: Observation Bias

Susan Henrichon has more than 25 years of experience in education. She has taught special education and possesses administrative experience in the public school setting. She holds a Master of Education in special education from Westfield State University and a Certificate of Advanced Graduate Study in educational administration from the University of Massachusetts.

Everything You Need to Know About Double Blind Study

Subjects in experimental research are randomly given to either the control or treatment group. A double blind study has group assignments of every subject from the participant and the researcher experimenting.

If the former is informed of which group they’re assigned, there’s a potential risk of their behavioural change, which will influence the results. On the other hand, if the latter knows which group the former has been assigned, their actions might disclose the task or directly affect the results.

A double blind study protects against such risks, guaranteeing that any significant or insignificant difference between different groups can be assigned to any of the treatments.

Different Blinding Types with Examples

Blinding refers to the withholding group assigned to each participant. Studies can be single binding study (occurs in different study kinds), double binding study or triple blinding study (used mainly in medical research).

• Single Blinding Study : In such a study, participants lack knowledge on which group they’ve been put under until the completion of the experiment to prevent behaviour change. Following is an example of a single blinding study:
• You have come up with a flu vaccine to validate your new treatment’s effectiveness. So, you conduct an experiment, providing the vaccine to half of the participants and the fake vaccine to the remaining that will not be effective.
• If participants realize they’ve got the unreal vaccine and are at risk of the flu, their behaviour might modify. This behaviour change can narrow the loopholes in illness rates among the groups mentioned above, thereby making the flu vaccine less effective.
• So, to achieve uninfluenced and correct research result, you should hide which vaccine is authentic and which is fake.
• Double Blind Study : Researchers carrying out an experimental treatment know every participant’s group. They might inadvertently treat the control group as compared to the treatment group. This can reveal the group assignment to participants or directly influence their outcome. So, the assignment assigned to the group is anonymous to the researcher and the participant. A paradigm of double blind study on the flu vaccine:
• You recruit various experimenters for administering vaccines and evaluate the results of participants. If the experimenters know whether the vaccine is genuine or fake, they may accidentally disclose it to the participants. Thereby directly influence their behaviour and the generated results indirectly as well.
• For example, if experimenters anticipate diminishing the flu symptoms through the vaccine, they may end up evaluating symptoms accidentally incorrectly, perhaps a more effective vaccine. Participants and experimenters are uninformed to group assignments to prevent any anticipation and evaluation on their end. Thus, a smooth double blind study can be carried out on the vaccines.
• Triple Blinding Study : A triple blind study happens when the assignment allocated to the group is unknown to administrators, participants, and those given a task to analyze the information after concluding the experiment.
• Researchers can expect a particular result and analyze the information in various ways once they reach their expected result. An example of a triple blinding study on the flu vaccine:
• In your study on the flu vaccine, you appointed assistants to analyze the information gathered on the rates of infection caused by the flu. You chose to keep group assignments hidden from the involved administrator, participants, and data analyzers – a triple-blind study.
• You must assign participants to group one or group two but don’t provide insights into which group bears which number to those analyzing the data to obtain triple blinding.

Significance of Blinding

The blinding ensures the internal validity of a study or makes you confident enough that any argument, finding or discussion on your flu vaccine will not change the result.

It is unlike a non-blinded study, as in the latter, participants can modify their behaviour or researchers find non-existent effects. Blinding, thus is important and helps avoid biases in scientific research.

Risk Associated with Unblinding

Unblinding happens when researchers blind experimenters or participants but still they gain information on which treatment (fake or real vaccine as in the case of flu vaccine study) is given to them before the completion of an experiment. Thus, unblinding results in indistinguishable outcomes. A suitable paradigm of unblinding:

You’re studying the influence of a recently developed instruction program in your school to better students’ comprehension skills.

You arbitrarily assign this new program to a few students while other students are guided with the standard program. You utilize a single blinding study: you don’t inform students which one out of the two instruction programs they are receiving to keep their behaviour constant. Students in a standard program might put more effort into their reading ability to prepare themselves and for not enrolling in the newly introduced program, or put less effort as they may believe that other students will excel at a much faster rate as compared to them.

Thus, the outcomes can be fallacious unless if you don’t unblind.

• Failure to Blind : Double blind study or triple blinding is usually not possible. However, medical experiments often utilize unreal treatment or a placebo for blinding, while in other research types, the treatment usually can’t be veiled from the experimenter or participant. For instance, physical therapists perform many treatments that cannot be imitated. In such scenarios, you should focus on other methodologies to minimize bias.
• Run a single-blind study instead of a double blind study and triple-blind study : Hiding subjects from participants or experimenters may not always be possible. You can stop them from knowing which group they belong to – the control group or treatment group. It is a convenient study and can be used in any type of study other than non-medical studies that don’t allow the use of a placebo in the control group.
• Use objective measures over subjective measures : Participants can hardly influence objective measures over subjective ones; for example, measure fever instead of reporting pain. This reduces the possibility of result influence by experimenters.
• Register data analysis methods in advance : This can stop researchers from implementing various analysis measures until they find the answer they are expecting.

Frequently Asked Questions

What is blinding.

Blinding refers to hiding from the control group and the treatment group about what treatment has been assigned to them to gain accurate research results.

How many types of blinding studies are there?

Blinding studies are of three types – single, double, and triple binding study. In the former only participants are unaware of the treatment group and control group assigned to them. In the next, both experimenters and participants are unaware. In the last, participants, experimenters, and researchers are unaware.

What is a double blind study?

A double blind study is a study in which experimenters and participants are uninformed of the treatment and control group.

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• Published: 05 September 2024

Cocos nucifera and glycerine afterwork moisturizers for secondary prevention of hand dermatitis among fabric worker: a randomized, double-blind, cross over trial

• Windy K. Budianti 1 ,
• Retno W. Soebaryo 1 ,
• Muchtaruddin Mansyur 2 ,
• Franciscus D. Suyatna 3 ,
• Minarma Siagian 4 ,
• Joshita Djajadisastra 5 &
• Cita R. S. Prakoeswa 6  

Scientific Reports volume  14 , Article number:  20702 ( 2024 ) Cite this article

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• Drug discovery
• Health occupations
• Medical research

The use of skin barrier-enhancing topical medication is a favorable approach for the treatment of occupational hand dermatitis (OHD). Cocos nucifera or coconut oil is one of the best sources of lipid enriched with laurate acid, and glycerin is a well-known humectant that improves skin hydration. This study is aimed is to evaluate the effectiveness of C. nucifera and glycerin for secondary prevention of OHD among batik (Indonesian traditional fabric) workers. In a randomized, double-blind, crossover trial, the effect of glycerine– C. nucifera cream versus glycerin-only was considered with multiple afterwork applications of moisturizer over a 2-week period on batik workers with OHD. Assessment of trans-epidermal water loss (TEWL), skin capacitance, and a clinical assessment using the Hand Eczema Severity Index (HECSI) were carried out at day 0 and 14. The results show thirty-two batik dyeing and/or rinsing workers were enrolled in the study with mild to moderate OHD. Clinical improvement was demonstrated by 20% decrease in HECSI and TEWL, and 20% increase in skin capacitance. Both moisturizers were equally effective for the secondary prevention of OHD. As a conclusion, glycerine– C. nucifera and glycerin-only cream are equally effective for secondary prevention for OHD among batik worker to reduce the prevalence of hand dermatitis.

Introduction

Batik is well-known as an intangible cultural heritage in Indonesia, claimed by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2009 1 . Batik is handwriting, a way of decorating by covering a part of the clothing with a coat of wax, followed by a dyeing process. Batik has become more popular over the years due to the dynamic development in the technological, esthetical, functional, and economic aspects 1 . The process of batik manufacturing is similar in different areas, but each region has its own characteristic of pattern and colour. Batik processing requires some materials that act as irritants or allergens, which can cause occupational hand dermatitis (OHD) 2 . The hazards exposure includes excessive water, caustic agents, detergents as irritants and synthetic dyes (naphtol) as allergens 2 .

Physical personal protective equipment (PPE) was not standardised and often locally modified by the workers 2 . There are not many studies reported regarding the use of moisturizers specific to a particular job 2 . The goal of treatment for hand dermatitis is to improve the skin barrier function with appropriate moisturizers 3 , 4 . Various studies have demonstrated that regular use of hand lotions or creams can increase skin hydration and treat allergic or irritant contact dermatitis 5 , 6 . Moisturizers have many effects on the skin 7 , 8 . They may form a form of coating, which acts as a barrier for chemicals from the exterior and restricts the loss of water and other important substances from the interior. Moisturizers are used as an adjuvant besides physical PPE and its limitations 4 . Nevertheless, evidence supporting the superiority of moisturizers containing lipids over the more traditional moisturizers is still lacking 2 .

Because OHD may be concerned, not many studies with repeated usage and application of moisturizer have been done 6 , 9 . Nowadays, studies regarding herbal products and its efficacy in dermatologic diseases is evolving 10 . This study aimed to examine whether the regular use of a moisturizer containing laurate acid from Cocos nucifera and glycerin cream in batik workers with OHD would prevent hand dermatitis from worsening. Improvement of the proportion of patients with OHD is deemed to be the goal of this study. This study was set in a real work situation as the batik workers still work under the evaluation of the moisturizer's effectiveness, and clinical effects were determined by using Hand Eczema Severity Index (HECSI), transepidermal water loss (TEWL), and skin capacitance 11 , 12 .

Materials and methods

Study design and subjects.

This was a randomized, double-blind, crossover, clinical trial done in few centres of batik enterprises in Indonesia. The ethical protocol was approved by The Ethics Committee for Health Research Faculty of Medicine Universitas Indonesia (Protocol number 16178) and registered to clinicaltrials.gov with ID (number: NCT03617068, registration date: 06/08/2018). Subjects enrolled in this study were batik workers involved in the dyeing and rinsing process, male, and aged 25–60 years, with mild-moderate OHD suitable to Mathias' criteria and investor global assessment for assessing the severity (Fig.  1 ). They were required to stop applying topical products on their hands and arms other than those given during this study 13 , 14 . The study was performed in accordance with relevant guidelines and informed consent was obtained from all subjects. Exclusion criteria included individuals with a history of allergy or irritation to moisturizers or coconut oil, severe dermatitis or sign of infection, and subjects undergoing treatment with topical or systemic immunosuppressant agent.

Flow of research study.

Cocos nucifera–glycerin and glycerin cream were obtained from Faculty of Pharmacy, Universitas Indonesia, packaged into uniform opaque plastic pots, and physico-chemically tested to show no differences between both creams in colour, viscosity, and scent.

Local produced coconut oil was chosen after a gas chromatography testing for five sources of coconut oil from different islands in Indonesia, and all were found to have identical compositions and characteristics. Sterile laboratory conditions and good manufacturing practices were ensured so that the cream was manufactured safely 15 .

Randomization, treatment allocation, and blinding

A list of random numbers was generated by the study statistician. The investigator and subjects were blinded to the codes, treatments were allocated randomly, and the packaged pots were dispensed appropriately. The codes were not disclosed to the investigators until the end of the study.

Study intervention

Before initiation of the study, all participants were asked to stop using of topical treatments and moisturizing cream for a 1-week wash-out period. Thirty-two participants were randomized into 2 groups: 16 subjects were asked to use C. nucifera–glycerin (product A) cream and 16 subjects were asked to use glycerin-only (product B) twice daily for two weeks each group, and between crossover a wash-out period for eight weeks was implemented. Aside from topical treatments used in the study, no other topical treatment was allowed. All subjects were requested to complete daily journals to record the frequency of the products were used each day and to refrain from using other products on their hands. Each subject was given one pot (10 g) of C. nucifera –glycerin cream or glycerine-only cream for 2 days and was instructed to apply the cream to both hands and arms after bath.

Clinical assessment

Clinical evaluation of the effect of moisturizer containing coconut oil and glycerin application over a 2-week period was compared with a glycerin-only moisturizer over the same period. Secondary endpoints included the percentage changes from baseline in HECSI values, TEWL, skin capacitance, and the adverse events during the study. The study endpoints and digital photographs were obtained at every visit.

The clinical severity of OHD was determined using a validated scoring system, Hand Eczema Severity Index (HECSI) 9 . This severity grading accounts for the extent and intensity of the lesion. TEWL and skin capacitance were measured on the anterior and posterior aspects of the dominant hand using a tewameter model TM300 and corneometer model CM825 (Courage & Khazaka Electronic GmbH) from Germany, respectively 16 .

The study was terminated for those who had severe adverse events, and dropouts were defined as subjects who did not attend for follow-up within two weeks and whose outcomes were unknown by the end of the study period.

Sample size

The sample size was calculated based on a crossover study and would give a power of 80% to detect at least 20% difference in the mean TEWL assuming an SD of 3.0, using a paired t-test with a 5% two-sided significance level. A sample of 32 subjects would be needed, assuming 10% dropout rate (Table 1 ).

Thirty-two batik workers with hand dermatitis were enrolled in the study after informed consent was obtained (Table 1 ). The flow of subject enrolment is shown in Fig.  1 . The patients were recruited from several traditional batik enterprises in Yogyakarta, Central Java, Indonesia.

In the initial period, one subject did not come at the second visit, while at the second period, one subject did not come at both visits. Both subjects denied protocol or side effects of the study as the reason for absence. Before data analysis, data clearance was conducted, and 1,875% missing value was found. However, the data was still fit to be processed using SPSS 22, and the distribution test was done with the Shapiro–Wilk normality test.

Clinical evaluation

Prior to the study, clinical characteristics of 32 subjects based on location, type of dermatitis, and nail discolouration were observed. The lesions occurred on the dorsum aspect of the hand of 2 subjects (6.3%), palmar of 17 subjects (53.1%), the finger of 10 subjects (31.3%), and ginger tips of 3 subjects (9.4%). For the types of hand dermatitis, there was no chronic hand eczema (0%), 2 subjects had recurrent vesicular hand eczema (6.3%), 18 subjects had hyperkeratotic palmar eczema (56.3%). Three subjects had pulpitis (9.4%), and 5 subjects had interdigital eczema (15.6%), while 4 had nummular hand eczema (12.5%). In this study, the predominant location of hand dermatitis was the palmar aspect, with hyperkeratotic palmar eczema as the most prevalent type. Most subjects had nail discolouration (96.97%), while only one subject had no nail discolouration. The following table presents the baseline conditions of the subjects based on the treatment group (Table 2 ) (Supplemantary file 1 ).

Hand eczema severity index

HECSI score was determined (ranging 0–360) based on observation of morphological lesions. Common morphologies were erythema, infiltration, vesicles, fissures, squamous, and edema. During the first period, the lowest baseline HECSI score was 10, and the highest was 54. During the second period, the lowest baseline was 7 and the highest was 54. Compared to the baseline, both groups showed a decrease in the HECSI score. The change in median HECSI score after treatment between periods 1 and 2, in the A-B treatment sequence, was not statistically significant ( p  = 0.477). Likewise, the median change in the HECSI score after treatment in periods 1 and 2, in the B-A treatment sequence, was not statistically significant ( p  = 0.166) (Table 3 ) (Supplementary file 3 ). A positive change from baseline indicates a decrease in HECSI and, therefore, an improvement in the skin conditions. Both moisturizers showed a clinically significant treatment effect on HECSI from baseline to day 14.

Effect of treatment on trans-epidermal water loss

The mean value of TEWL for dorsum A-B, and B-A after receiving coconut oil and glycerine-only cream is presented in Fig.  2 . In the AB period 1 sequence group, the first group to get coconut oil moisturizing cream, the final TEWL value was 29.6 (9.03) g/m 2 /h after application of product A, and 18.1 (4.71) g/m 2 /h after application of product B. The inter-individual difference is 11.5 (9.45) g/m 2 /h. In the BA group, the mean final TEWL value of the hand dorsum was 18.8 (7.79) g/m 2 /h after application of product A and 25.9 (6.11) g/m 2 /h with inter-individual differences of − 7.1 (8.51) g/m 2 /h (Supplementary file 2 ).

Comparison of mean Dorsum and Palmar TEWL values between periods 1 and 2 for each treatment sequence.

The final skin capacitance value of the subject's hand in the AB treatment sequence group after the use of product A was 70.77 (28.01) AU, whereas for product B it was 60.52 (10.33) AU, with inter-individual differences of 10, 3 (27.39) AU. Whereas in the B-A treatment sequence group, the final TEWL value on the dorsum of the subject's hand was 67.52 (16.47) AU after using product A, and 78.47 (26.39) AU after application of product B (Fig.  3 ).

Comparison of mean dorsum and palmar SCap values between periods 1 and 2 for each treatment sequence.

Based on the difference in mean HECSI score, the TEWL value and skin capacitance of dorsum hand and palmar skin capacitance between before and after the treatment shown in Table 4 , established a difference in batik workers who received coconut oil-glycerin moisturizing cream (product A) and glycerin only (product B). It was determined that the difference is considered meaningful if there is a decrease in the HECSI score of 20%, a decrease in TEWL of 20%, and an increase in skin capacitance of 20%. A 10% decrease in HECSI value was found in 93.8% of subjects, as was the case with the product B group. A decrease in TEWL greater than 20% in the dorsum of the hand was more notable in product B group (81.3%) compared to the product A group (65.6%), and an increase from the baseline was found in 5 subjects for each treatment group (Table 4 ).

Of the 32 subjects, no adverse effects were found due to the use of product A and glycerine-only cream. Among 32 subjects, there were 27 (84%) subjects who stated that they liked the distinctive smell of coconut oil in the test cream. Both were given coconut oil aroma for blinding purposes. All subjects felt comfortable using the cream after bathing and do not feel the work is interrupted by the use of both test creams and stated the skin is softer after the use of both test creams.

The effectiveness of moisturizers is determined by various components, including active ingredients, vehicle, penetration ability, ease of application, cosmetics acceptance, comfort, and minimal intersection effects 17 . In making moisturizers specifically for occupational fields, it is also necessary to consider the reactions that can occur between the material found in moisturizers and exposure to the workplace environment 18 . The use of moisturizers is expected to not facilitate the penetration of irritant or allergen material, which exacerbates the occurrence of occupational dermatitis 3 . Moisturizing texture needs to be designed so that it does not interfere with work. Adherence to the use of moisturizers is very important to note, workers need to understand the importance of using moisturizers for prevention of dermatitis, as well as the correct use, both frequency and volume of moisturizers 4 , 8 .

The lipid concentration used, the amount and type of emulsion, humectant, and preservative materials need to be considered, including the pH of the moisturizer 3 , 8 , 15 . The active ingredients used in this study were 30% coconut oil and 20% glycerin, as well as the excipients that matched the base cream to obtain oil emulsions in water (o/w). With this composition moisturizer give an occlusive effect and humectant, so that it effectively protects the skin barrier.

This study used 20% glycerin as a humectant in both test creams. Glycerin is an effective humectant and is able to activate transglutaminase in the stratum corneum, accelerate the maturity of corneocytes and act as corneodesmolytics in the process of desquamation, as well as esterification of ceramide 19 . Glycerin also has unique characteristics because it is capable of maintaining moisture of the skin for quite some time even though the material is undetectable on the skin. The effectiveness of glycerin improves skin hydration is closely related to the presence of AQP-3 or aqua-glycoprotein, a membrane protein expressed most in the basal stratum and serves to facilitate the transport of water in the epidermis ( Supplementary file ).

The most type of hand dermatitis in this study was hyperkeratotic hand eczema. Pro-inflammatory cytokines namely interleukin (IL)-1a, IL-1b, and TNF-a (7), and the chemokine CCL21 that hones naıve T-lymphocytes to the skin, shown to be elevated in the skin during irritant contact dermatitis 19 . Other hand dermatitis found in batik artisans was the interdigital type of eczema in 15.0% of subjects, nummular hand eczema 12.5% of subjects, 9.4% of pulpitis subjects, and recurrent vesicular hand eczema types were found at least. In interdigital eczema type hand dermatitis papule lesions, vesicles, plaques, and squares are found with signs of inflammation 20 , 21 . The lateral side of the finger is the transition zone between palmar and dorsum of the hand. There is a progressive evolution between the two-thirds anterior part and the posterior third of the finger. The suspected hand dermatitis is greater if there is involvement of the anatomic location because it is a location that is prone to irritant or allergic contact dermatitis. Nail discoloration was found in 96.9% worker, Discoloration of nails occurs because the gloves used are degraded so that the color material can penetrate the glove and then penetrate into the nails.

The normal TEWL value for the palmar area was 41.69–46.37 g/m 2 /h and hand dorsum were 10.00–13.16 g/m 2 /h. After the use of moisturizer for 2 weeks, in both groups there was a decrease in TEWL on the dorsum hand and palmar ompared to the baseline. Decreasing the TEWL value of the dorsum of the hand and palmar after administration of product B in the A-B sequence turned out to be greater than the administration of coconut oil moisturizing cream. Possibly the conditions in the second period of the subject have not returned to the baseline due to the carry-over effect being removed with a long wash out period of 8 consecutive weeks. Unlike the case with the B-A group who received product B first, both the decrease in TEWL value of the dorsum of the hand and the palmar was greater in giving product A than product B, but the therapeutic effect on TEWL of the hand dorsum and palmar after the administration of coconut oil or product B moisturizing cream is similar. Clinically, the decrease in palmar TEWL values in the two treatment groups was not as large as the decrease in TEWL value on the dorsum of the hand. Only 46.9% experienced a decrease in TEWL of more than 20% in the product A group, and 43.8% in the product B group. The palmar location is the area most experienced by workers in the form of hyperkeratotic lesions. This can affect the penetration of coconut oil and product B moisturizing creams, making it less effective in reducing TEWL values.

This study was carried out on the actual conditions of Batik artisans, who continue to work with exposure to irritants and allergens as usual. Thus, the results obtained can reflect the real situation and are expected to contribute to improving the health of batik workers. The limitations of this study are the work environment and surrounding factors, temperature and humidity at work, and the workload per day cannot be controlled from time to time. This condition can affect the value of TEWL and skin capacitance during the use of the test cream.

According to the specific aim of this study, it can be concluded that both interventions group showed decrease in HECSI more than 20% for about 93.8% of the subjects. The treatment effects of product A and B was found to be exactly same. Most common hand dermatitis type was palmar hyperkeratosis (56.5%) found at 90.63% subjects, as well as nail discoloration at 31% of the subjects (96.97%). Decrease of TEWL score for more than 20% at dorsal and palmar parts of the hand after 14 days of product A application compared to product B. The treatment effects of product A and the product B was found to be exact. Increase in skin capacitance for more than 20% at dorsal and palmar parts of the hand after 14 days of product A application compared to product B. There were no side effects occurring due to product A nor the product B found among the 32 workers, after subjective and objective evaluations. There were no signs and symptoms of poor interactions between moisturizers and exposure to materials used at work. A total of 27 (84%) subjects stated that they liked the distinctive smell of product A. All subjects did not feel work was interrupted by the use of product A and B.

According to the result of this study, several factors need to be implemented such as: recommending the use of coconut oil containing moisturizers and glycerine among Batik worker in the rinsing and dyeing sections, that support the occupational safety health program.

Both glycerine– C. nucifera combination and glycerin-only formulation cream are equally effective for secondary prevention for OHD among fabric workers in Indonesia. Afterwork moisturizers as secondary prevention of hand dermatitis among fabric workers should be encouraged as means to reduce disease prevalence.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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This study was funded by the final assignment grant for doctoral students of Universitas Indonesia “Hibah TADOK Universitas Indonesia”.

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Windy K. Budianti & Retno W. Soebaryo

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Muchtaruddin Mansyur

Clinical Pharmacology Department, Faculty of Medicine, Universitas Indonesia, Depok, Indonesia

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I as the corresponding author declare each of the author's roles in this manuscript. dr. Windy Keumala Budianti (Me, as first author) created the main concept, collecting the data, editing, writing, analyzing, and reviewing the manuscript. As for the the other author's roles mentioned in this manuscript are as follow; Prof. Retno contributed in the concept making in dermatological field by her expertise Prof. Muchtaruddin contributed the concept making in occupational field as well as statistical analyzing Prof. Fransciscus D Suyatna contributed in the active ingredients preparation and the sample method Prof. Cita contributed in the editing and writing of the manuscript dr. Minarma contributed in reviewing the manuscript Dr. Joshita contributed in the active ingredients preparation as a pharmacist expert.

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Budianti, W.K., Soebaryo, R.W., Mansyur, M. et al. Cocos nucifera and glycerine afterwork moisturizers for secondary prevention of hand dermatitis among fabric worker: a randomized, double-blind, cross over trial. Sci Rep 14 , 20702 (2024). https://doi.org/10.1038/s41598-024-72010-0

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DOI : https://doi.org/10.1038/s41598-024-72010-0

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Bioavailability of liposomal vitamin c in powder form: a randomized, double-blind, cross-over trial.

1. Introduction

2. materials and methods, 2.1. study materials, 2.2. preparation of liposomal vitamin c in powder form, 2.3. verification of the process of obtaining liposomal powder formulation, 2.3.1. scanning electron microscopy (sem), 2.3.2. cryogenic transmission electron microscopy (cryo-tem), 2.3.3. measurement of particle size distribution (psd) of the liposomal powder and liposomes, 2.3.4. measurement of zeta potential, 2.4. in vitro bioavailability study, 2.4.1. cell culture, 2.4.2. permeability across human intestinal epithelial cells monolayer (caco-2 cells), 2.4.3. evaluation of vitamin c concentration, preparation of buffer and wash solutions, method optimization, uplc analysis, 2.4.4. bioavailability expression, 2.5. clinical study, 2.5.1. ethical approval, 2.5.2. trial registration, 2.5.3. study design, 2.5.4. primary and secondary endpoints, 2.5.5. methodology, 2.6. statistical analysis, 3. results and discussion, 3.1. properties of liposomal vitamin c in powder form, 3.2. comparison of bioavailability of non-liposomal and liposomal ascorbic acid in an in vitro cell model, 3.3. clinical trial results, 3.3.1. study population, 3.3.2. results of analysis of pharmacokinetic properties of the tested formulations, 4. limitations, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Żmuda, P.; Khaidakov, B.; Krasowska, M.; Czapska, K.; Dobkowski, M.; Guzowski, J.; Kowalczyk, P.; Lemke, K.; Folwarski, M.; Foryś, A.; et al. Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial. Appl. Sci. 2024 , 14 , 7718. https://doi.org/10.3390/app14177718

Żmuda P, Khaidakov B, Krasowska M, Czapska K, Dobkowski M, Guzowski J, Kowalczyk P, Lemke K, Folwarski M, Foryś A, et al. Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial. Applied Sciences . 2024; 14(17):7718. https://doi.org/10.3390/app14177718

Żmuda, Przemysław, Barbara Khaidakov, Maria Krasowska, Katarzyna Czapska, Michał Dobkowski, Julian Guzowski, Paulina Kowalczyk, Krzysztof Lemke, Marcin Folwarski, Aleksander Foryś, and et al. 2024. "Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial" Applied Sciences 14, no. 17: 7718. https://doi.org/10.3390/app14177718

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