single vs double blind experiment

Double-Blind Experimental Study And Procedure Explained

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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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.

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:

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Blinding in Clinical Trials: Seeing the Big Picture

Thomas f. monaghan.

1 Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

Christina W. Agudelo

2 Division of Cardiovascular Medicine, Department of Medicine, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; [email protected] (C.W.A.); [email protected] (J.M.L.)

Syed N. Rahman

3 Department of Urology, Yale University School of Medicine, New Haven, CT 06520, USA; [email protected]

Alan J. Wein

4 Division of Urology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA; [email protected]

Jason M. Lazar

Karel everaert.

5 Department of Human Structure and Repair, Ghent University, 9000 Ghent, Belgium; [email protected]

Roger R. Dmochowski

6 Department of Urological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; [email protected]

Associated Data

Not applicable.

Blinding mitigates several sources of bias which, if left unchecked, can quantitively affect study outcomes. Blinding remains under-utilized, particularly in non-pharmaceutical clinical trials, but is often highly feasible through simple measures. Although blinding is generally viewed as an effective method by which to eliminate bias, blinding does also pose some inherent limitations, and it behooves clinicians and researchers to be aware of such caveats. This article will review general principles for blinding in clinical trials, including examples of useful blinding techniques for both pharmaceutical and non-pharmaceutical trials, while also highlighting the limitations and potential consequences of blinding. Appropriate reporting on blinding in trial protocols and manuscripts, as well as future directions for blinding research, will also be discussed.

1. Introduction

Randomized clinical trials are a gold standard in evidence-based medicine because findings from these studies reflect the highest possible level of evidence which may be garnered from an original research study [ 1 ]. Randomized clinical trials tend to be highly tailored to a specific research question but, for a vast majority of interventions and outcomes, blinding is widely viewed as a core tenet of sound clinical trial study design [ 2 , 3 , 4 ].

Despite exponential growth in the number of clinical trials conducted yearly over the past two decades [ 5 ], multiple authors contend that the methodological quality of clinical trials has remained stagnant or even declined [ 6 , 7 ], such that true practice-guiding evidence on a broad range of medial topics paradoxically lags behind [ 8 , 9 , 10 ]. Blinding is one aspect of clinical trial design that remains particularly underutilized—although this methodological feature is not universally attainable, blinding is still implemented in only a fraction of clinical trials in which it is, in fact, deemed feasible [ 11 , 12 , 13 ]. Accordingly, it stands to reason that greater emphasis on addressing pervasive misconceptions about blinding in medical research is key to reconciling the growing divide between current research trends and actual practice needs [ 14 ]. Furthermore, because blinding is relevant to data analysis in the broadest sense, a sound understanding of blinding should be considered a prerequisite for evidence-based best practice, and thus of equal importance to providers and patients alike [ 15 , 16 ].

This article will review general principles for blinding in clinical trials, including examples of useful blinding techniques for both pharmaceutical and non-pharmaceutical trials, while also highlighting the limitations and potential consequences of blinding. Appropriate reporting on blinding in trial protocols and manuscripts, as well as future directions for blinding research, will also be discussed. Note that this article will focus on blinding in clinical trials, where it is most often discussed, but the relevance of blinding spans the gamut of study designs, from late-stage randomized interventional trials to retrospective observational studies (e.g., blinded outcome assessors) [ 17 , 18 ].

2. What Is Blinding?

In an unblinded, or “open”, study, information about the assigned interventions is available to all people and groups involved in the research. Blinding, or “masking”, is the process by which information that has the potential to influence study results is withheld from one or more parties involved in a research study.

Importantly, the topic of blinding must be distinguished from allocation concealment. Allocation concealment is the process by which investigators and participants enrolled in a clinical study are kept unaware of upcoming group assignments until the moment of assignment [ 19 ]. Allocation concealment is a core tenet of proper study randomization and plays a key role in preventing selection bias [ 20 ]. Blinding, in contrast, refers to the act of withholding information about the assigned interventions from people involved in the trial from the time of group assignment until the experiment is complete. While proper randomization minimizes the differences between treatment groups at the beginning of a trial, it does not prevent differential treatment of study groups during the trial, nor does it prevent differential interpretation and analysis of study outcomes [ 21 ].

3. Why Do We Blind?

We blind because the potential for bias is everywhere. Bias can take numerous shapes and forms when people involved in a research study are privy to information about the assigned interventions [ 22 ]. Participant knowledge of their group allocation can bias expectations, adherence to the trial protocol, treatment-seeking behavior outside the trial, and assessment of the effectiveness of an intervention [ 23 ]. Differential treatment, attention, or attitudes toward subjects by a non-blinded healthcare team or other members of the research staff also pose a major threat to unbiased outcomes [ 24 , 25 ]. Importantly, once bias is introduced from any one of these potential sources, there exist no analytical techniques by which to reliably correct for this limitation [ 21 ].

Several lines of empirical evidence demonstrate the direct effects of non-blinding on clinical trial outcomes. One systematic review from Hróbjartsson et al. concluded that attrition is significantly more frequent among controls versus subjects assigned to the experimental group when participants are not blinded—a phenomenon not common to well-designed participant-blinded trials [ 26 , 27 ]. Moreover, participant-reported outcomes were found to be exaggerated by 0.56 standard deviations overall in trials of non-blinded versus blinded participants, with an even greater discrepancy in trials investigating invasive procedures [ 26 ]. In three separate meta-analyses from Hróbjartsson et al. on observer bias in randomized clinical trials, non-blinded versus blinded outcome assessors were found to generate exaggerated hazard ratios by an average of 27% in studies with time-to-event outcomes [ 28 ], exaggerated odds ratios by an average of 36% in studies with binary outcomes [ 29 ], and a 68% exaggerated pooled effect size in studies with measurement scale outcomes [ 30 ]. Taken together, the four meta-analyses from Hróbjartsson et al. indicate that participant blinding and assessor blinding similarly lend to exaggerated effect sizes, although the three analyses on observer bias collectively suggest that the type of variable assessed influences how large of an effect blinding may have on study results.

The relevance of blinding in mitigating bias is perhaps most easily appreciated in studies involving subjective outcomes. However, many seemingly objective outcomes rely on interpretation of participant data and thus are also characterized by subjective elements (e.g., electrocardiogram scan interpretation for myocardial infarction) [ 31 ]. Further, even unequivocally objective outcomes, such as time to death, can be indirectly affected by factors such as the use of advance directives, concurrent interventions, and follow-up intensity [ 31 ]. Correspondingly, while some meta-analyses have reported more robust evidence of bias with subjective versus objective outcomes [ 32 ], this finding is inconsistent, and multiple other studies have reported no appreciable difference in estimated treatment effect based on the degree of outcome subjectivity [ 29 , 33 ]. Thus, for both subjective and objective outcomes, current evidence suggests that blinding can play a potentially major role in mitigating threats to internal and construct validity [ 34 ].

4. Who and What Do We Blind?

Current literature has identified as many as 11 distinct groups meriting unique consideration when it comes to blinding: (1) participants, (2) care providers, (3) data collectors and data managers, (4) trial managers, (5) pharmacists, (6) laboratory technicians, (7) outcome assessors (study personnel who collect outcome data), (8) outcome adjudicators (personnel who confirm that outcomes meet prespecified criteria), (9) statisticians, (10) members of safety and data monitoring committees, and (11) manuscript writers [ 35 ].

In a blinded clinical study, treatment assignment is the information most frequently withheld from these groups [ 35 ]. However, in many cases, blinding of some of the aforementioned groups to additional information is also feasible. For example, laboratory technicians, outcome assessors, and outcome adjudicators may also be blinded to basic demographic and clinical characteristics of the study population, as well as the overall purpose of the trial [ 35 ].

Consistent with the significant heterogeneity as to “who” and “what” may be blinded, it is important to appreciate blinding on a graded continuum rather than as an all-or-nothing phenomenon, wherein the blinding of some study groups to pertinent information as feasible (i.e., “partial blinding”) can tangibly improve the strength of trial results—even when maximal blinding of all study groups cannot be achieved [ 35 , 36 ].

5. How Do We Blind?

A multitude of techniques have been described for blinding all people and groups involved in clinical trials. The researcher’s specific approach to blinding will ultimately be highly dependent on the specific parties being blinded as well as the research question and intervention at hand. In fact, there exists considerable flexibility in blinding—even beyond the strategies for blinding subsequently highlighted in this section, investigators may feasibly create their own novel blinding technique, so long as (1) the technique successfully conceals pertinent information about the groups and (2) does not impair the ability to accurately assess or adjudicate outcomes [ 37 ].

Boutron et al. systematically reviewed blinding in randomized control trials assessing pharmacologic treatments, organizing their results to provide an excellent inventory of practical methods to (1) establish blinding of participants and providers, (2) maintain blinding (i.e., prevent unblinding), and (3) blind outcome assessors [ 38 ]. Common methods to establish participant/provider blinding include centralized preparation of similar capsules or tablets, bottles, and syringes; flavoring to mask the specific taste of active oral treatments; and double-dummy procedures. (A double-dummy technique is the use of more than one placebo for the maintenance of blinding, particularly in cases when two treatments under investigation cannot be made identical, wherein subjects are assigned to different sets of treatment and more than one group may receive placebo. For example, in a trial designed to compare an oral tablet medication with a medication administered by intramuscular injection, an indistinguishable placebo can be prepared for both the tablet and injection, and one group may receive the active medication tablet and placebo injection, with another group receiving the placebo tablet and active medication injection.) Strategies for reducing the risk of unblinding include centralized dosage adaptation as warranted, centralized evaluation for side effects, partial information about side effects, and use of an “active placebo” (sugar pill which mimics expected side effects of the active treatment). Methods for blinding outcome assessors typically rely on centralized assessment of complementary investigations, clinical examinations, and adjudication of clinical events.

Blinding in non-pharmaceutical trials is undoubtedly faced with several unique challenges related to the complexity and physical component of such interventions, participant and physician acceptance, and broader ethical and safety considerations [ 39 , 40 ]. Accordingly, relative to pharmaceutical trials, blinding is typically implemented even less frequently in those investigating surgical procedures, medical devices, and participative interventions (e.g., rehabilitation) [ 41 ]. Nevertheless, compared to pharmaceutical trials, blinding in these trials is of no less relevance in the pursuit of true practice-shaping evidence [ 41 , 42 ]. In fact, blinded interventional trials are often practical, and may even feasibly involve a placebo group (i.e., “sham procedure”) [ 13 ]. Figure 1 provides examples of sham procedures for surgical interventions and other non-pharmacological clinical trials, as published in a separate systematic review from Boutron and colleagues [ 43 ].

Sham procedure performed according to the category of treatment assessed. Reprinted with permission from ref. [ 43 ]. Copyright 2007 Boutron et al. Full text available from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040061#s5 .

In trials comparing two similar invasive procedures, particularly those performed under general anesthesia or heavy sedation, blinding of participants can be relatively straightforward [ 21 ]. Notably, however, blinding of participants may even be feasible when surgical interventions differ significantly. Namely, there exist several well-described recent examples of investigators devising highly creative solutions to maintain participant blinding in invasive interventional trials, including imitation of the surgical access point, replication of visual, auditory, and physical cues in the operating room, matching the duration of experimental and control procedures, and standardization of additional care (e.g., diagnostic scans, perioperative medical management, etc.) [ 13 ].

Beyond these measures, a handful of studies have even managed to blind surgeons to the intervention being performed. For example, in one randomized control trial of electrothermal therapy for chronic lower back pain, surgeons inserted an intradiscal catheter under fluoroscopic guidance in all cases, at which point an independent technician connected the catheter to a generator and delivered either electrothermal energy (experimental group) or did not (control group) [ 44 ]. A trial on palatal implants for obstructive sleep apnea blinded proceduralists through the use of a manufacturer-preloaded delivery system containing either an implant (active treatment) or no implant (sham) [ 45 ]. While blinding of surgeons is seldom practical, eliminating their role in post-operative care, follow-up, and additional treatment is often feasible for minimizing this potential source of bias [ 46 ].

Even in the absence of surgeon blinding, it is often possible to blind other members of the care team and study staff from information that has the potential to bias study results. Simple measures such as uniform dressings large enough to cover all potential incision sites have been used to successfully blind other members of the post-operative care team [ 21 ]. Blinding of outcome assessors, while uncommonly performed in surgical trials, is frequently practical through simple techniques such as the use of independent assessors, concealed incisions, and blinding of digital images [ 12 ]. Figure 2 depicts methods for blinding key groups in randomized control trials for different non-pharmacological clinical trials from Boutron et al. [ 43 ].

Methods of blinding participants, health care providers, or other caregivers that rely on the category of treatment and comparator assessed. Reprinted with permission from ref. [ 43 ]. Copyright 2007 Boutron et al. Full text available from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0040061#s5 .

6. Limitations of Blinding

Although blinding is generally viewed as a very effective method by which to eliminate bias, blinding does pose some inherent limitations, and it behooves clinicians and researchers to be aware of such caveats. Blinding often requires considerable effort and expense [ 47 ]. Blinding also has a well-established negative impact on study recruitment [ 48 , 49 , 50 ]. Additionally, blinding inherently deviates from real-world experience, making it a hallmark feature of trials which aim to maximize the likelihood of establishing the efficacy of an intervention by testing it in an ideal setting (i.e., “explanatory trials”), but potentially less relevant for trials which aim to generate situations that are as close to routine practice as possible (i.e., “pragmatic trials”) [ 51 ]. Blinding has also been suggested to potentially adversely impact subsequent care after the conclusion of a clinical trial [ 52 ].

In contrast to open-label studies, blinded clinical trials are inherently susceptible to the phenomenon of unblinding (i.e., “code-breaking”). The term “unblinding” is often used to describe the formal process by which subjects and/or investigators are made aware of a participant’s treatment assignment according to prespecified contingencies (e.g., in the case of a medical emergency which compromises a participant’s safety). However, even in the absence of formal unblinding, subjects in either the intervention or control group may feasibly come to suspect their assignment status using more subtle clues, such as the presence (or absence) of signature medication effects or side effects [ 53 , 54 ]. In fact, potential threats to blinding are pervasive and multifaceted—there have been documented cases of researchers intentionally subverting blinding by comparing pills or viewing restricted notes and, more recently, instances of trial participants connecting through social media and collaborating to deduce their treatment allocation [ 55 , 56 , 57 ].

Unblinding has several deleterious effects that can threaten the validity of trial results [ 58 , 59 ]. Subjects in a placebo arm who discover or suspect that they are not receiving active treatment may become upset or uncooperative (i.e., “resentful demoralization”), access interventions outside of the trial (i.e., “compensatory rivalry”, which inherently increases the risk of “contamination” (i.e., when members of the control group are inadvertently exposed to the intervention)), exaggerate negative responses (i.e., “biased event reporting”), or even withdraw from the trial [ 60 , 61 ]. Similarly, empathetic caregivers who know subjects to be controls may provide them with non-study, but effective, interventions (i.e., “cointerventions”) [ 62 ]. Conversely, participants who suspect or know that they are on the “better” treatment may downplay mild side-effects, while clinicians with this information may downplay participants’ symptoms and underreport “soft” clinical findings (also “biased event reporting”, but typically in the opposite direction compared to controls) [ 62 ].

Multiple recent analyses of blinded trials published in high-impact medical journals have concluded that the implementation of blinding is inconsistent and successful in perhaps fewer than 50 percent of cases [ 62 ]. Various efforts have been made to quantitively assess blinding, which most commonly utilize a blinding questionnaire or survey, and ask subjects in both the experimental and control groups to guess their treatment allocation [ 63 ]. Several methods have been employed in analyzing these data, including chi-square and McNemar’s tests, a standard Kappa statistic, and multiple blinding indices [ 63 , 64 , 65 ]. However, it should be noted that an assessment of blinding success is only seldom performed [ 66 , 67 , 68 ], and has even been criticized for the inherent limitations of this process [ 69 ]—many of which centering on the fact that end-of-trial tests for “blindness” cannot be reliably distinguished from hunches about efficacy [ 62 ]. In other words, participant responses to end-of-trial blinding surveys are likely influenced by prior assumptions and expectations regarding treatment efficacy, such that beliefs about allocation may still cause bias even when blinding succeeds in making these beliefs independent of actual allocation [ 70 ]. Citing these reasons, the most recent “Current Consolidated Standards of Reporting Trials” (CONSORT) statement no longer advocates for testing of blinding success, reflecting a major divergence from prior renditions of CONSORT guidelines [ 71 ]. The topic of evaluating and reporting blinding success remains debated and is highly complex, but there does exist a relative consensus regarding the need for a greater understanding of the bias-generating consequences that result from its loss, irrespective of whether they arise from the loss of blindness, per se, or rather from beliefs about allocation or another cause [ 62 ].

The limitations of blinding with respect to recruitment, applicability to routine practice, and analysis have led some authors to challenge the role of participant and clinician blinding as a universal gold standard in evidence acquisition. Anand et al. emphasize that blinding of participants and clinicians requires careful consideration of the negative effects of blinding against its potential benefits, as guided by the following key questions: (1) whether blinding is needed for a scientifically sound result; (2) whether changes in participant or clinician awareness of assignment status will cause a change in behavior that influences results; (3) whether there is a risk of excessive harm with blinding and, if so, whether said risk is justified by the importance of the study findings; and (4) whether the financial cost of blinding compromises spending on other aspects of trial integrity [ 53 ]. Note that recent criticisms of blinding from Anand et al. and others primarily center on the topic of participant and/or clinician blinding—there remains a relative consensus regarding the critical importance of objective outcomes, blinded outcomes assessment, and blinded adjudication of outcomes in mitigating major sources of bias in clinical trials [ 53 ].

7. Blinding: Reporting Responsibly

The terms single-blind, double-blind, and triple-blind are often used to describe studies in which one, two, or three parties, respectively, are blinded to information about the treatment groups. Recall, however, that up to 11 discrete groups merit unique consideration with respect to blinding in clinical trials [ 35 ]. Correspondingly, there has long existed great variability in textbook definitions and clinician interpretations of these terms [ 72 ], which is particularly problematic given that study authors often fail to specify who, exactly, has been blinded [ 73 ]. For example, a sample of randomized clinical trials published in 2001 found that more than half of “double-blind” studies failed to describe the blinding status of any person involved in the trial [ 74 ]. Moreover, on a follow-up survey sent to trial authors, 15 different operational meanings of the term “double-blind” were reported by the investigators, who typically believed that their preferred definition was the most widely used [ 74 ].

In view of the high potential for misinterpretation, authors of the most recent CONSORT (2010) statement instruct researchers to “abandon [the] use” of “double-blind” and related terms [ 71 ]. Instead, the 2010 CONSORT guidelines direct authors to “explicitly report blinding status”, including who is and is not blinded, what information is concealed, and how blinding is performed [ 75 ]. Further, if relevant, authors must provide a description of the similarity of the interventions and procedures used for blinding [ 75 ]. (Specification of how blinding was performed, as well as a description of an intervention’s similarity, were both “noteworthy specific changes” from early renditions of the CONSORT statement [ 75 ], motivated by the need for greater “evidence of the method of blinding” [ 71 ].) The “Standard Protocol Items: Recommendations for Interventional Trials” (SPIRIT) 2013 statement similarly directs authors to specify who will be blinded and how blinding will be accomplished in clinical trial protocols [ 76 ].

Despite these increasingly explicit consensus recommendations, there still exist major discrepancies in how blinding is reported in registered protocols and publications, as evidenced by continued widespread suboptimal adherence to current CONSORT and SPIRIT guidelines [ 77 , 78 , 79 ]. Several strategies have been proposed for improving the quality of reporting on blinding in clinical trials. One practical option recently proposed by Lang et al. is to detail blinding status using a standardized “Who Knew” table [ 35 ]. Although such a practice has not yet gained widespread traction, the author’s table aptly illustrates the extent to which blinding should be described to ensure transparency in research methodology ( Table 1 ).

A standard table for reporting the use of blinding in randomized trials of pharmaceutical interventions.

( a ) Other groups or individuals in a trial that were capable of being blinded should be listed in the table, and whether or not they were blinded in the study should be indicated. Individuals with dual responsibilities, such as caregiving and data collecting, should be identified by combining the entries in the same row heading. ( b ) Although group assignment is the information most commonly withheld in a blinded trial, data assessors, such as pathologists and radiologists, are often blinded to the purpose of the trial, group assignment, and the demographic and clinical characteristics of participants whose biopsy samples or images they are interpreting. ( c ) In many cases, authors should determine before the trial begins whether the method of blinding had a reasonable chance of being effective, including establishing the similarity between active and placebo preparations and the bioequivalent availability for two or more active drugs [ 80 ]. Testing the effectiveness of blinding after the trial has ended is uninformative because the results cannot be separated from pre-trial expectations of the success of the intervention [ 47 ]. ( d ) If blinding has been compromised, authors should report the fact and indicate the potential implications the loss of blinding might have for interpreting the results [ 80 ]. Reprinted with permission from ref. [ 35 ]. Copyright 2020 Lang et al. Full text available from https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-020-04607-5 .

8. Future Directions

Numerous studies have used and not used blinding. Comparatively, however, far fewer papers have attempted to comprehensively review blinding in clinical trials, and several questions remain unanswered. The magnitude of the estimated treatment effect associated with participant blinding status has been shown to vary considerably across different studies [ 29 ]. As detailed previously, the three separate meta-analyses from Hróbjartsson et al. on observer bias collectively suggest that the type of variable also influences the magnitude of the effect which blinding may exert on study results [ 28 , 29 , 30 ]. Further, a subset of studies have found non-blinded assessors to significantly favor control, rather than experimental, interventions, corresponding to a comparable degree of observer bias in the opposite direction, but the reason for this remains unclear [ 30 ]. Moreover, compared to participants and outcome assessors, the impact of blinding of other trial personnel and healthcare professionals on estimated treatment effect is even less well-established [ 32 , 33 ]. Therefore, multiple factors appear to impact the magnitude of bias imposed by a lack of blinding, and recent meta-epidemiological evidence suggests that many relevant study factors remain incompletely characterized in this regard [ 33 ]. The effects of unblinding all above-mentioned study groups on study outcomes likewise remain poorly characterized.

There exist several additional facets of clinical trial study design which also merit greater investigation in relation to blinding status. Historically, placebos constituted the primary comparator arm in most pharmacologic randomized control trials, but trials involving active best-of-care comparator arms and other non-placebo background therapies have grown in popularity in recent years [ 81 , 82 ]. Surgical trials are seemingly even more heterogeneous in this regard, as new surgical interventions may be tested against placebo (i.e., “sham procedure”), but also against a similar surgical/invasive intervention, dissimilar surgical/invasive intervention, pharmacotherapy, participative intervention (e.g., physical therapy), or active surveillance/watchful waiting [ 41 ]. Accordingly, whether specific characteristics of a study’s comparator arm(s) modify the effects of blinding or consequences of unblinding merits further study [ 83 ]. Additionally, although blinding is infrequently incorporated into early-stage clinical trials [ 84 ], we are unaware of studies assessing the effects of blinding as a function of study phase, and it may be revealing to assess the relative effect of blinding in phase 2 versus phase 3 trials—particularly in cases where phase 2 and phase 3 trials show divergent results [ 85 ]. We also advocate for a more simplified and standardized approach to incorporating blinding in power analyses and sample size re-estimation for adaptive trials [ 86 , 87 , 88 ].

Author Contributions

Conceptualization, T.F.M. and R.R.D.; writing—original draft preparation, T.F.M. and R.R.D.; writing—review and editing, T.F.M., C.W.A., S.N.R., A.J.W., J.M.L., K.E., and R.R.D. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

Thomas F. Monaghan has no direct or indirect commercial incentive associated with publishing this article and certifies that all conflicts of interest relevant to the subject matter discussed in the manuscript are the following: Alan J. Wein has served as a consultant for Medtronic, Urovant, Antares, and Viveve, outside the submitted work. Karel Everaert is a consultant and lecturer for Medtronic and Ferring and reports institutional grants from Allergan, Ferring, Astellas, and Medtronic, outside the submitted work. The additional authors have nothing to disclose.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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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Frequently asked questions

What’s 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 analysing the data.

Frequently asked questions: Methodology

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

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

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

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

To define your scope of research, consider the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reproducibility and replicability are related terms.

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

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

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

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

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

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

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

Snowball sampling is best used in the following cases:

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

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

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

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

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

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

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

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

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

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

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

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

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

The two main types of social desirability bias are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

You can use several tactics to minimise observer bias .

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

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

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Overall, your focus group questions should be:

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

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

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

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

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

The four most common types of interviews are:

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

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

Unstructured interviews are best used when:

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

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

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

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

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

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

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

If something is a mediating variable :

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

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

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

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are 4 main types of extraneous variables :

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

The difference between explanatory and response variables is simple:

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

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

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

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

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

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

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

Independent variables are also called:

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

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

In statistics, dependent variables are also called:

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

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

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

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

Deductive reasoning is also called deductive logic.

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

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

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

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

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

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

Here are a few common types:

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

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

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

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

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

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

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

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

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

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

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

There are two subtypes of construct validity.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Advantages:

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

Disadvantages:

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

In a factorial design, multiple independent variables are tested.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Triangulation can help:

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

But triangulation can also pose problems:

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

There are four main types of triangulation :

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

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

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

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

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

These are four of the most common mixed methods designs :

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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

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

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

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

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

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

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Double Blind Studies

The double blind method.

A double blind study is one in which neither the patient nor the physician knows whether the patient is receiving the treatment of interest or the control treatment.

 For example, studies of treatments that consist essentially of taking pills are very easy to do double blind - the patient takes one of two pills of identical size, shape, and color, and neither the patient nor the physician needs to know which is which.

 A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects which reduces the risk of bias, it standardizes the placebo effect which is a further challenge to the validity of a study.

 The placebo effect could be thought of in this way:  

1.      Patients who believe they are receiving a new experimental treatment tend to be more optimistic about the outcome. This means that, when asked, they tend to minimize health problems and give more weight to positive effects. They also tend to take better care of themselves and comply better with the conditions of the experiment. There is also substantial evidence that, independent of all this, patients who have positive beliefs about their treatment do better than patients who do not. In many situations, the placebo effect is at least as strong as any objective effects of the treatment!

2.      Doctors who believe that a patient is receiving a new experimental treatment tend to be more optimistic about that patient's chances, evaluate their state of health more favorably, and communicate positive expectations to the patients, who in turn try to get better so as to prove their doctor right! 

Adapted from SUNY Downside Medical Center/Medical Research Library of Brooklyn .   11 March 2005.   http://library.downstate.edu/ebm/2300.htm   (28 September 2006)

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Chapter 1: Research Methods

Back to chapter, blind procedures, previous video 1.10: the placebo effect, next video 1.12: ethics in research.

Individuals can enter into studies with biases that skew results, portraying a treatment as being more effective than it actually is.

For instance, during an insomnia study, a researcher may place a sleep-aid or placebo tablet in a cup, and disperse the capsules to participants. While the scientist notes who received which type of pill, subjects are oblivious.

Here, the researcher remains biased, anticipating that the sleep-aid will work. Thus, during observation, she may note that those administered the drug fell asleep faster, while in reality there is no difference between groups.

However, participant biases are eliminated. Since subjects don't know if they swallowed the sleep-aid, they lack expectations regarding the efficacy of the pill. Consequently, they’ll accurately state how their insomnia was affected.


This method, where only researchers or participants realize who obtained treatment, is termed a single-blind study . However, this procedure can result in one group—here, the scientist—remaining biased.

To circumvent this, double-blind studies are performed, where both participants and the data-collecting researchers are “blind”. Here, the scientist may give the placebo and sleep-aid to a colleague, who re-labels the pills as “Type Y” and “Type Z” before dispersing them.

This coding system eliminates the researcher’s expectations during assessment—she doesn’t know which participants received the medication, and can’t assume who should sleep better. Consequently, she’ll record if someone appears restless.

As before, subjects will also precisely describe their sleep experience. It’s only after data analysis that everyone learns who was administered the sleep-aid or placebo.

Overall, blind procedures help to minimize biases and produce accurate results, which can be used to assess the efficacy of medication and other treatments.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations.

This text is adapted from OpenStax, Psychology. OpenStax CNX.

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1.4.5 - blinding.

Blinding techniques are also used to avoid bias. In a  single-blind  study the participants do not know what treatment groups they are in, but the researchers interacting with them do know. In a  double-blind  study, the participants do not know what treatment groups they are in and neither do the researchers who are interacting with them directly. Double-blind studies are used to prevent researcher bias. 

Example: Yogurt Tasting Section  

Researchers are comparing a low-fat blueberry yogurt to a high-fat blueberry yogurt. Participants are randomly assigned to receive one type of yogurt. After tasting it, they complete an online survey. The researchers know which yogurt containers are low-fat and which are high-fat, but participants are not told. This is an example of a  single-blind  study because the researchers know which participants are in the low- and high-fat groups but the participants do not know. A double-blind study may not be necessary in this case since the researchers have only minimal contact with the participants. 

Example: Caffeine Energy Study Section  

Researchers want to know if adult males who consume high amounts of caffeine interact more energetically. They obtain a representative sample and randomly assign half of the participants to take a caffeine pill and half to take a placebo pill.  The pills are randomly numbered and coded so at the time the researchers do not know which participants have been given caffeine and which have been given the placebo. All participants are told that they may have been given a caffeine pill. After taking the pill, researchers observe the participants interacting with one another and rate the interactions in terms of level of energy. 

This is a  double-blind  study because neither the researchers nor the participants know who is in which group at the time the data are collected. After the data are collected, researchers can look at the pill codes to determine which groups the participants were in to conduct their analyses. A double-blind study is necessary here because the researchers are observing and rating the participants. If the researchers know who is in the caffeine group they may be more likely to rate their levels of energy as very high because that is consistent with their hypothesis. 

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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."

What Is A Single Blind Study? Single Blind vs Double Blind Studies

Clinical trials usually follow one of two models: single blind and double blind trials. We examine the differences and when each type is used.

Written by Nazar Hembara , PhD

Published 20 December 2023

Clinical trials are an essential component of medical science, helping to discover new methods and treatments to help people manage their diseases and conditions, as well as finding potential cures.

They work by assessing human participants who are given experimental drugs and treatments to evaluate the effects, with monitoring conducted to ensure the safety of everyone involved. To guarantee the validity of the trial’s results, there are most commonly two types of trials used - a single or double blind study .

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What are blind studies?

Blind studies can sometimes be referred to as masked studies and involve the research of human participants in a clinical trial , with some of its critical aspects not disclosed to participants or researchers (or both) to avoid any bias. This improves the accuracy and reliability of the study’s results.

‘Blinding’ ensures greater objectivity and avoids any preconceived expectations, preferences, or beliefs from interfering with the study. This makes it possible to assess the effectiveness of medical treatment in the fairest way.

What are single blind studies?

In a single blind study, patients are not informed which study group they will be designated to, but the researchers will be aware. For example, they could be given either the experimental drug or a placebo.

This type of trial is designed so that the participant is not aware of what treatment is being studied, helping to avoid any bias that may influence the outcome of the clinical trial.

What are double blind studies?

A double blind study on the other hand is a trial where neither the participants nor the researchers know which study group each individual has been designated to. This helps to eliminate all bias, with some researchers perhaps having a certain level of loyalty to a drug being studied, or an invested interest in the medical intervention being approved.

This ensures more credible and valid clinical trials.

The key differences between single blind vs double blind studies

There are a number of key differences between a single and double blind trial that you should be aware of relating to the knowledge of the participant and the researchers.

In a single blind study, the participants are completely unaware whether they will receive a new, experimental drug or be given a placebo. However, the researchers and those administering the treatment will know which type of treatment is being given to each study group.

Meanwhile, in a double blind study, neither the participants nor researchers know what treatment is being given.

The benefits of single blind vs double blind studies

Both single blind and double blind studies offer unique advantages in scientific and clinical research.Their unique benefits is what makes them more or less applicable to certain types of studies.

Bias control and credibility

As the participants in single blind studies aren’t aware of the treatment they will be receiving, but are aware that the researchers know which group they have been assigned to and might see this as being unfair, especially if the study involves a group of participants receiving a placebo. This can have an impact on their expectations of the study, sometimes in a negative way. Some participants may feel they will receive no benefit from participating in the trial, which could result in them failing to complete the trial or affect the reliability of the outcomes they report.

In terms of researchers, their bias may come from an eagerness to see a medication approved by the FDA, or preferring a certain type of medical intervention over another.

In a double blind study, there is more control when it comes to avoiding bias, with both participants and researchers in the dark about what treatments are being assigned. This ensures the reporting of outcomes is more accurate based on the experience of the participant.

Objective data collection

In a single blind study, the data collection process can lose credibility due to potential bias, because the researcher has knowledge regarding each group assignment. This is why a double blind study is often preferred as it is more objective, as neither the participants nor researchers know what treatment has been issued.

Scientific validity

From a scientific perspective, there is a significant difference between single-blind and double-blind studies. In single-blind studies, where only the participants are unaware of the treatment they are receiving, the research can be susceptible to research bias. This bias can potentially affect the reliability and validity of the study's findings, as the researchers beliefs or expectations might inadvertently influence the study outcome

On the other hand, double-blind studies, where both the participants and the researcher are unaware of the treatment assignments, are generally considered to be more reliable and valid. This is because double-blind studies offer a higher level of control over biases, thereby providing more accurate and trustworthy results. By eliminating both participant and experimenter biases, double-blind studies ensure that the outcomes are solely a result of the intervention, making them the preferred method in many scientific research and clinical trials.

Cost-effectiveness

The more logistically complex a trial is, the more expensive it becomes as it requires additional time and resources to ensure the success of the study.

The cost-effectiveness of single blind studies is one of the key benefits compared to a double blind study. This is because blinding key aspects of a clinical trial from both participants and researchers requires more administrative resources.

Practicality

Single blind studies are often more practical when compared to their double blind counterparts. This is because double blind trials can sometimes make it difficult for researchers and administrators to manage the trial effectively, especially for complex trials that involve a large number of participants.

Furthermore, ensuring the study remains blinded for its full duration can also provide a challenge when a large number of individuals are involved.

When a single blind study might be chosen vs a double blind study

Many factors can dictate when a trial sponsor chooses to conduct a single blind or double blind study, including the type of treatment that is being administered, expected bias, and also ethical considerations.

When is a single blind study used?

Single blind studies are often chosen because:

• Resource constraints such as those limited by time, finances, and personnel could have an impact on why a single blind study will be chosen over a double blind.
• The practicality and feasibility of a study can also influence the design of a trial, as blinding researchers and treatment administrators could create too many challenges in terms of the logistics of a clinical trial, making it infeasible.
• Ethical considerations also need to be taken into account as a single-blinded trial may be deemed sufficient in regard to protecting the participant’s well-being and their rights, while a double-blinded trial may not.
• There may also be certain behavioral and observational studies that do not require double-blinding. For example, this could be because the researcher's knowledge of the group assignment may have no impact on the accuracy and reliability of the data collected during the trial.

When is a double blind study used?

Double blind studies may be preferred because:

• There may be a need to minimize bias as much as possible to ensure the results of the trial are deemed valid.
• Any possible placebo effect may also need to be controlled to avoid bias which is made easier by double blinding.
• When trialing a new drug or treatment, the safety and efficacy of the intervention need to be maintained to meet regulatory requirements. In many cases, a double blinded study is preferred by regulatory bodies to meet these needs.
• For more complex medical interventions, there may be a series of studies conducted that assess a range of conditions and their outcomes. Double blinded trials can ensure fairer comparisons and result in more accurate evaluations of each intervention.
• Double blinding helps to remove bias from data collection and analysis. This is especially the case in clinical trials where researchers play a pivotal role and may be invested in the success of the treatment being administered.

How to get involved in a single blind or double blind study

If you are interested in getting involved in a single blind or double blind clinical study then there are a range of avenues open to explore. Below are three options a person can consider to learn more about participating in a study.

Identify research opportunities by directly speaking to research institutions, universities, hospitals, and governmental health agencies and speak with them directly. Or you can contact clinical trial providers online to discuss the requirements of future trials.

Explore online databases and directories that specialize in listing clinical trials. These directories include ClinicalTrials.gov or the World Health Organization's International Clinical Trials Registry Platform .

Speak with your doctor, regular health provider, or a local clinic to discuss clinical trials that may be taking place in the near future. They may have knowledge of ongoing studies that you may be eligible for or can refer you to relevant research centers and institutions.

Choosing whether a clinical study will apply a single blind and double blind approach depends on a number of factors. These may include the type of clinical trial, the practicality of using single vs double blinding, ethical considerations, and the need to minimize any bias.

Although double blinded studies are typically the preferred option when designing a trial due to the degree of control they provide in terms of limiting bias, there are more challenges to overcome when compared to single blind studies. Single blind studies are often more cost-effective as they generally require fewer resources and can be much more practical.

This is why researchers and clinical trial sponsors carefully assess a range of factors when designing a trial, ensuring the results are as accurate as possible, while also maintaining a level of safety to meet regulations.

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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.

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What Is The Difference Between Single Blind And Double Blind Clinical Trials?

When undertaking a clinical trial, the two major models that one can use are the single blind and double blind trials. Selecting the right trial is important since it can affect the outcome of the trial or introduce errors. The ideal model should be selected based on the type of trial and other variables. For any clinical trial, there are usually two groups of people who are experimented on. Members of one group are given a placebo, and the members of another group are given the treatment that is being studied. This is so as to compare the effectiveness of the treatment to placebo.

In a single blind study, the participants in the clinical trial do not know if they are receiving the placebo or the real treatment. This is done to reduce the risk of errors, since some participants might produce spurious results if they know that they are taking the placebo or medication. In this model, the experimenter monitoring the participants knows which individuals received the placebo and which ones got the treatment under examination.

In a double-blind study, both the participants and the experimenters do not know which group got the placebo and which got the experimental treatment. This is considered to be the superior model of clinical research since it eliminates outcomes that are produced due to placebo effect, as well as observer bias by the experimenter. The fact that the experimenter does not know which group received the placebo or the experimental drug means that the risk of conscious and unconscious observer bias is reduced, making the study more accurate.

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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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Who knew? The misleading specificity of “double-blind” and what to do about it

• Thomas A. Lang   ORCID: orcid.org/0000-0002-7482-7727 1 &
• Donna F. Stroup   ORCID: orcid.org/0000-0003-1699-4671 2  

Trials volume  21 , Article number:  697 ( 2020 ) Cite this article

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In randomized trials, the term “double-blind” (and its derivatives, single- and triple-blind, fully blind, and partially blind or masked) has no standard or widely accepted definition. Agreement about which groups are blinded is poor, and authors using these terms often do not identify which groups were blinded, despite specific reporting guidelines to the contrary. Nevertheless, many readers assume—incorrectly—that they know which groups are blinded. Thus, the term is ambiguous at best, misleading at worst, and, in either case, interferes with the accurate reporting, interpretation, and evaluation of randomized trials. The problems with the terms have been thoroughly documented in the literature, and many authors have recommended that they be abandoned.

We and our co-signers suggest eliminating the use of adjectives that modify “blinding” in randomized trials; a trial would be described as either blinded or unblinded. We also propose that authors report in a standard table which groups or individuals were blinded, what they were blinded to, how blinding was implemented, and whether blinding was maintained. Individuals with dual responsibilities, such as caregiving and data collecting, would also be identified. If blinding was compromised, authors should describe the potential implications of the loss of blinding on interpreting the results.

“Double blind” and its derivatives are terms with little to recommend their continued use. Eliminating the use of adjectives that impart a false specificity to the term would reduce misinterpretations, and recommending that authors report who was blinded to what and how in a standard table would require them to be specific about which groups and individuals were blinded.

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Background: problems with the term “double-blind”

The single biggest problem in communication is the illusion that it has taken place. George Bernard Shaw

In reports of randomized trials, the use of the term “double-blind” and its derivatives (single- triple-blind, fully blind, and partially blind or masked) is commonly understood to indicate that two groups participating in the trial are kept unaware of which participants are receiving the experimental intervention and which are receiving the control intervention [ 1 , 2 , 3 , 4 , 5 , 6 ].

Despite its long and widespread use, however, the term has several problems.

It is ambiguous

Agreement about which groups are blinded in a double-blind trial is poor [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. For example, in one study, 91 physicians reported 17 unique combinations of groups (often more than two) that they believed were blinded in a double-blind trial (Table  1 ), and 25 textbooks contained 9 unique combinations [ 1 ]. Another study of 25 “double-blind trials” published in 16 leading journals identified 5 different combinations of participants, assessors, caregivers, and statisticians as being blinded [ 14 ]. Identifying groups in general terms (e.g., investigators, caregivers) is also ambiguous [ 4 ], especially when individuals have dual roles, such as collecting data and assessing outcomes [ 2 , 4 , 5 , 6 ].

It is often uninformative

Even when using the term in an article, many authors do not identify which groups were blinded or how blinding was implemented [ 1 , 2 , 3 , 4 , 5 , 6 , 9 , 11 , 12 , 14 , 16 , 17 ]. Among 83 published trials reported as being double-blind, 41 did not identify any group as being blinded [ 9 ]. Without this information, “readers should remain skeptical about [blinding’s] effect on bias reduction.” [ 2 ].

It can be misleading

Many readers assume—incorrectly—that they know which groups are blinded in a double-blind trial (Table  1 ) [ 2 , 3 , 4 , 5 , 11 , 15 , 16 ]. Unfortunately, grossly inadequate reporting allows this assumption to go unchallenged when the article is read. (However, several studies have found that many published trials do not include the details of blinding, even when blinding was adequately implemented [ 4 ].) In 88 (70%) of 126 registered anesthesia trials, the groups or individuals reported to be blinded in the published results differed from those listed in the corresponding protocols [ 16 ].

It is inadequate

The suggestion to establish explicit definitions for the term [ 7 , 18 ] is complicated by the fact that several groups or individuals can be blinded. Limiting “double-blind” to trials in which only 2 specific groups are blinded leaves other combinations without an equivalent term.

It is often confused with allocation concealment

In randomized trials, the allocation schedule (the list indicating the group to which the next participant will be assigned, in random order) has to be kept secret to prevent group assignment from being manipulated. That is, allocation concealment minimizes selection bias before participants have been assigned to experimental groups, whereas blinding minimizes surveillance, expectation, and ascertainment bias after group assignment. Many readers are not aware of this difference [ 2 , 5 , 6 , 8 , 12 , 13 , 15 , 18 , 19 , 20 ], perhaps because the terms “allocation” and “blinding” indicate neither the similarities nor the differences between the concepts.

It is often mistakenly believed to be required in a randomized trial and to be essential to the trial’s validity [ 1 , 2 , 5 , 11 , 13 , 15 , 16 , 19 , 20 , 21 ]

“A randomised trial can be methodologically sound and not be double blind or, conversely, double blind and not methodologically sound.” [ 2 ]. Said another way, “Let us examine the placebo somewhat more critically, however, since it and ‘double blind’ have reached the status of fetishes in our thinking and literature. The Automatic Aura of Respectability, Infallibility, and Scientific Savoir-faire which they possess for many can be easily shown to be undeserved in certain circumstances.” [ 21 ].

In some situations, it can be confused with the condition of being without sight [ 2 , 5 , 12 , 20 , 22 , 23 ]

Some authors prefer “masking” to “blinding,” although the meaning of either term in a clinical trial may not be readily apparent to nonnative English speakers [ 18 , 22 ]. Further, some authors use the terms interchangeably [ 5 , 6 , 7 , 10 , 11 , 12 , 15 , 18 , 24 , 25 ], others insist that only masking be used [ 17 , 20 , 23 ], and still others insist that only blinding be used [ 2 , 5 , 22 ]. In addition, masking is sometimes used to describe how treatments are made indistinguishable [ 18 , 19 , 25 , 26 ], whereas blinding usually indicates which groups are unaware of treatment assignment [ 1 , 2 , 3 , 4 , 5 , 6 ]. Finally, searching the literature for “blinded,” “partially blind,” or “fully blind” randomized trials also identifies dozens of unwanted citations to the condition of being without sight.

It is unrealistic

The problem with trying to identify in a single term the groups who are blinded in a trial is that the number of pairs is potentially large. The literature identifies 11 groups or individuals who could be blinded: participants, care providers, data collectors and managers, trial managers, pharmacists [ 27 ], laboratory technicians [ 1 ], outcome assessors (who collect data on outcomes), outcome adjudicators (who confirm that an outcome meets established criteria), statisticians [ 2 , 4 , 6 , 11 , 12 , 13 ], and sometimes even members of data monitoring and safety committees [ 1 , 3 , 4 , 6 , 11 , 17 ] and manuscript writers [ 3 , 6 , 11 , 16 , 17 ]. These 11 groups can form 55 unique pairs. Even limiting the possibilities to 5 groups commonly recommended for blinding [ 15 , 28 ]—participants, care providers, data collectors, outcome assessors, and statisticians—leaves 10 possible combinations.

Proposed solutions

As near as we can tell, despite the above problems and several calls to abandon the term [ 1 , 5 , 6 , 9 , 11 , 12 , 16 , 28 ], only one substitute for double-blinding has been proposed in the literature: “subject- and assessor-blind” [ 29 ]. Aside from being somewhat awkward, the term assumes that double-blinding applies only to subjects and assessors, which, although reasonable, is not uniformly accepted.

The terms “fully blinded” or “partially blinded” do appear in the literature, but not as substitutes for substitutes for double-blinding or single-blinding [ 27 ]. Although both are used in randomized trials, they involve randomly assigning treatments, not groups, and can be applied to subsets of individuals within groups. For example, participants who could receive either an active drug or a placebo would be “fully blinded,” whereas participants who know they are receiving an active drug but not which one, would be “partially blinded.”

We considered blinding “assignment concealment [ 24 ]” because it accurately indicates that group assignment is what is hidden. It does not imply which groups are involved and has no history of doing so. It also eliminates the blinding-masking controversy and is not associated with other, less-technical meanings. Further, the relationship between blinding and “allocation concealment” is not apparent, but allocation concealment and assignment concealment are two sides of the same coin: they clearly indicate that two different components of the trial are concealed: the allocation schedule and group assignment, one term indicating group concealment before assignment and one after.

However, assignment concealment does not work well as a label. We concluded that “a concealed assignment trial” was unlikely to replace “a blinded trial.” Likewise, its use can be awkward: “group assignment was concealed from participants” was unlikely to replace “participants were blinded to treatment.” Further, as noted above, for better or worse, the mere use of the term “blinding” is widely considered to indicate study quality, and we concluded that authors would be unwilling to give up using this prized and familiar term. Finally, many people believed that “concealment” should be reserved for, or would be confused with, allocation concealment.

The term “blinding” is so firmly established that a simple substitute term, even if we could find one, is unlikely to be acceptable. Instead, we propose two changes in reporting trials described as blinded.

Our first proposal is to eliminate the use of adjectives that modify “blinded”: single-, double-, triple-, observer-, personnel-, rater-, observer-, fully or partially blinded, or any other qualifier that would make “blinded” seem more specific than it is. A trial would be described as either blinded or unblinded. Using “blinding” as a verb in a sentence would also be helpful. Such use encourages specificity by requiring a noun, usually which groups were blinded: “We blinded caregivers and data assessors” or “caregivers and data assessors were blinded.”

We wholeheartedly endorse the near-universal recommendation that authors report whether or not the trial was blinded [ 4 , 10 , 14 , 15 , 16 ], who was blinded [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 19 , 20 , 22 , 30 , 31 ], how they were blinded [ 2 , 4 , 5 , 6 , 12 , 13 , 19 , 20 , 26 , 30 , 31 ], and whether the method of blinding was likely to be successful [ 28 , 32 ], including the degree of similarity between the experimental and control interventions [ 31 ].

Accordingly, our second proposal is to have all trials described as blinded include the details in a standard “Who Knew” table (Table  2 ). This table has two parts: a required part and a supplemental part. The required part would indicate whether each of the 6 groups most commonly blinded (the person assigning participants to groups, participants, caregivers, data collectors and managers, outcome assessors, and statisticians) was or was not blinded , what information they were blinded to, how blinding was implemented, and whether blinding was maintined during the trial. The supplemental part, used when necessary, would present the same data for any other group or individual who was blinded. Individuals with dual responsibilities, such as caregiving and data collecting, would be identified in the same row heading. If blinding was compromised, authors should report the fact in the table and indicate in the text the potential implications that loss of blinding might have for interpreting the results.

Conclusions

“Blinding” as a concept to reduce bias has been used for more than 200 years [ 34 ], and “double-blind” as a term has been used in clinical trials for 70 years [ 35 ]. Even with the substantial support in the literature for abandoning its use, finding a simple, acceptable replacement seems unlikely. Instead, eliminating the use of adjectives that impart a false specificity to the term would reduce misinterpretations, and recommending that authors report who was blinded to what and how in a standard table would require them to be more specific about which groups and individuals were blinded.

Thomas A. Lang, MA

Principal, Tom Lang Communications and Training International

Adjunct Instructor, Medical Writing and Editing Program, University of Chicago Professional Education

Senior Editor, West China Hospital/Sichuan Medical School, Chengdu, China

Donna F. Stroup, PhD, MSc

Principal, Data for Solutions, Inc.

Co-signers (in alphabetical order):

Matthias Egger, MD, MSc, FFPH : Professor of Epidemiology and Public Health and former Director, Institute of Social and Preventive Medicine, University of Bern, and President, National Research Council, Swiss National Science Foundation. Former co-editor, International Journal of Epidemiology

Forough Farrokhyar, MPhil, PhD : Professor and Research Director, Department of Surgery, Department of Health, Evidence and Impact, McMaster University

Robert Fletcher, MD : Professor Emeritus of Population Medicine, Harvard Medical School; founding Co-Editor, Journal of General Internal Medicine ; former Co-Editor-in-Chief, Annals of Internal Medicine ; founding member, Word Association of Medical Editors (WAME); member, International Advisory Board, The Lancet

Suzanne W. Fletcher, MD : Professor Emerita of Population Medicine, Harvard Medical School; founding Co-Editor, Journal of General Internal Medicine ; former Co-Editor-in-Chief,  Annals of Internal Medicine ; National Academy of Medicine; former member, American Board of Internal Medicine; founding member, US Preventive Services Task Force

R Brian Haynes, OC, MD, PhD, FRCPC : Professor Emeritus of Clinical Epidemiology and Biostatistics; Professor of Medicine, McMaster University; co-founder, Evidence-Based Medicine movement; founder, Health Information Research Unit; founding Editor, ACP Journal Club ; lead developer of the structured abstract

Anne Holbrook, MD, PharmD, MSc, FRCPC : Professor, Department of Medicine, and Director, Division of Clinical Pharmacology & Toxicology, McMaster University; leading Canadian drug policy advisor and research lead for evidence-based therapeutics

Eileen K Hutton, RM, PhD, DSc (HC) : Professor Emerita and former Assistant Dean, Faculty of Health Sciences, and former Director of Midwifery, McMaster University; Professor of Midwifery Science, Vrije University, Amsterdam; and Fellow, Canadian Academy of Health Sciences

Alfonso Iorio, MD, PhD, FRCPC : Professor, Department of Health Research Methods, Evidence and Impact; Bayer Chair for Clinical Epidemiology Research and Bleeding Disorders; Chief, Health Information Research Unit and Hamilton-Niagara Hemophilia Program, McMaster University

Richard L. Kravitz, MD, MSPH : Professor, Internal Medicine; Former Director, Center for Health Services Research in Primary Care, University of California, Davis; former co-Editor-in-Chief, Journal of General Internal Medicine ; Director, UC Center Sacramento, a program providing leadership training in politics and relevant evidence for policymakers

José Florencio F. Lapeña Jr., MA, MD, FPCS : Professor of Otolaryngology; former Vice-Chancellor, University of the Philippines; Editor-in-Chief, Philippine Journal of Otolaryngology Head and Neck Surgery ; Charter President, Philippine Association of Medical Journal Editors; Past President, Asia Pacific Association of Medical Journal Editors (APAME); Secretary and Past Director, World Association of Medical Editors (WAME)

Maria del Carmen Ruiz-Alcocer, MD : Senior Medical Editor, Intersistemas Publishers; Former President, Mexican Association of Biomedical Journal Editors (AMERBAC); Past Director, World Association of Medical Editors (WAME); member, European Association of Science Editors (EASE)

Roberta Scherer, PhD : Senior Scientist, Clinical Trials and Evidence Synthesis, Johns Hopkins Bloomberg School of Public Health; former Associate Director, USA Cochrane Center; Adjunct Assistant Professor, Epidemiology & Public Health, University of Maryland School of Medicine

Christopher H. Schmid, PhD : Professor and Chair of Biostatistics and founding member and former Co-Director of the Center for Evidence Synthesis in Health in the Brown University School of Public Health; founding Co-Editor of Research Synthesis Methods ; helped develop Institute of Medicine national standards for systematic reviews

Thomas A. Trikalinos, MD : Associate Professor of Health Services, Policy, and Practice; Director, Center for Evidence Synthesis in Health, School of Public Health, Brown University

Junmin Zhang, MD, PhD : Professor and Managing Director, Journal of Capital Medical University , Medical Education Management , Journal of Translational Neuroscience , Capital Medical University, Beijing, China

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Lang, T.A., Stroup, D.F. Who knew? The misleading specificity of “double-blind” and what to do about it. Trials 21 , 697 (2020). https://doi.org/10.1186/s13063-020-04607-5

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Single and Double Blind Designs

Travis Dixon October 24, 2016 Research Methodology

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How are single blind  and double-blind techniques used in experiments?

Before understanding about single and double-blind techniques, it is important that you understand the amazing power of the placebo effect . This is an interesting film about placebos and their effect.

Single Blind:  A single-blind design is when the participant doesn’t know if they are in the “treatment group” or the “control (e.g. placebo) group”.

The treatment group in an experiment is the group that experiences the factor that the researchers hypothesize will have an effect. The control does not receive this treatment, but something in its place. In many studies the control group will receive a placebo.

For example, let’s say a new drug is designed to cure cancer (we wish!). The researchers want to see if the drug actually works. Because of the power of the placebo effect the researchers want to give half the participants the real drug and the other half a placebo pill. A single blind design would mean that the participants wouldn’t know which group they’re in. It should be easy to see why this is important in such studies that test the effects of a particular treatment; if participants know they’re not getting the actual treatment then the chances it’ll work are reduced.

Double Blind:  In a double blind design neither the participant  nor  the person gathering the dependent variable data knows which group the participant is in. Of course at least one person knows, but this is not the person gathering the data. To use the cancer pill example above, they could do something like keep a secret file of who is receiving which treatment and this is not revealed to the Doctors who are monitoring the effects the pill is having on the cancer.

The double-blind technique is valuable to reduce researcher bias . It is only natural that a researcher would want to prove their own hypothesis right. When data is highly subjective there is the potential for researcher bias to affect results. Not knowing which treatment the participant is receiving is one way of reducing the chances researcher bias will influence the experiment’s results.

In animal experiments obviously the animals don’t have the cognitive capacity to figure out which condition they’re in, or that they’re even in an experiment. But the researchers will often not know. There’s no need to state that it’s a double-blind because it’s redundant and a single-blind is inaccurate. In this cause it can simply be called a “blind study” or “blind design”. 

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

16 Advantages and Disadvantages of a Double-Blind Study

A double-blind study uses a format where neither the participants nor the researchers know who receives a specific treatment. This procedure is useful because it prevents bias from forming in the achievable results. It is used most often when there is a direct need to understand the benefits of demand characteristics against the placebo effect.

What is unique about the placebo effect is that a person receives an inert substance that has no medical benefit. Participants believe that it is real medicine because a double-blind study wouldn’t inform anyone who gets the actual drug being studied. Researchers don’t receive that information either.

That means the results between the two groups can get compared to see if the effects of the drug are better than that of the placebo. It can also be a way to check for the development of side effects.

Several double-blind study advantages and disadvantages are worth reviewing when considering this format.

List of the Advantages of a Double-Blind Study

1. Three groups are typically part of a double-blind study. The typical double-blind study project will involve three groups of participants. You’ll have the treatment group, the placebo, group, and a control group. The first two receive the item in question based on their name, although only the administrator knows for certain who is getting what since researchers are kept in the dark. The control group doesn’t receive anything because it serves as the baseline against which the other two sets of results get compared.

When people in the placebo group improve more than the control group, then it shows a belief that the product works. If the treatment group shows better results than those who receive a placebo, then you know the medication worked.

2. It avoids deception in the research process. One of the criticized shortcomings of this approach is the fact that no one knows if the items they take or use is real or a placebo. The solution is to create two placebo subgroups where one is told that it is real medicine and the other is told it isn’t, which means researchers would need to deceive one set of participants. That process would violate the principles of informed consent.

The double-blind structure avoids this issue by providing complete information to all participants without letting on who receives the actual product getting studied.

3. It reduces the issue of experimenter bias. Using double-blind procedures can minimize the potential effects of research bias when collecting data. This issue often occurs when experimenters knowingly or unknowingly influence the results during information gathering or product administration during the project. There can also be subjective feelings that drive specific decisions that would occur if less information was present in the study.

By limiting the potential influences that could impact the collected data, the final results produced by the research or experiment has more validity.

4. The results of a double-blind project can get duplicated. One of the reasons why a double-blind study is considered a best practice is because the results offer the potential for duplication. Other researchers can follow the same protocols for administering placebos and the item being examined against a control group. If the results are similar, then it adds even more validity to the ability of a product or service to provide benefits. When duplication doesn’t happen, then the information from both studies can get compared to see what may have created a divergence in the data.

5. Double-blind assignment factors are randomized. No one knows who is going to be part of what group at the beginning of a double-blind study. The only participant group that knows they aren’t part of the placebo or target group are those who provide the control baselines. When looking at an intervention-based process, the fact that random assignment occurs for willing participants works to reduce the influence of confounding variables in the material.

6. High levels of control are part of the research process. The context of a double-blind research study allows administrators to manipulate variables so that the setting allows for direct observation. Control factors that could influence the environment can get added or removed to assist with the limitation of outside factors that would potentially change the data. This process allows for an accurate analysis of the collected data to ensure the authenticity of the results gets verified.

7. It is a process that’s usable in multiple industries. The double-blind study might be used primarily by the pharmaceutical industry because it can look directly at the impact of medication, but any field can use the processes to determine the validity of an idea. Agriculture, biology, chemistry, engineering, and social sciences all use these structures as a way to provide validation for a theory or idea.

List of the Disadvantages of a Double-Blind Study

1. It doesn’t reflect real-life circumstances. When a patient receives a pill after going to the doctor, they are told that the product is actual medicine intended to provide specific results. When participants receive something in a double-blind placebo study, then each person gets told explicitly that the item in question might be real medicine or a placebo. That leads to a different set of expectations that can influence the results of the work in adverse ways.

These artificial environments can cause an over-manipulation of the variables to produce circumstances that fall outside of the study’s parameters. When situations don’t feel realistic to a participant, then the quality of the data decreases exponentially.

2. Active placebos can interfere with the results. Double-blind studies respond to the objections of researchers unintentionally when communicating information about the results of a pill being authentic or a placebo. Objections to the pill offering this information don’t exist with this structure. Although both items look identical, the real medication provides biological effects. Even if the results aren’t measurable, the individuals can feel the impact of the medicine on their bodies.

This outcome may cause them to conclude that they are in the treatment group. That means some participants have a higher positive expectancy than those who don’t feel those effects. It is a disadvantage that can lead to a misinterpretation of the results being experienced in real-time.

3. It is not always possible to complete a double-blind study. There are times when a double-blind study is not possible. Any experiments that look at types of psychotherapy don’t benefit as an example because it would be impossible to keep participants in the dark about who receives treatment and who didn’t get the stated therapy. It only works when there is a way to provide two identical processes without clear communication about who receives the authentic item and who receives the placebo.

4. We do not fully understand the strength of the placebo effect. Research published by Science Translational Medicine in 2014 found that the simple act of taking a pill can establish a placebo effect for people. A migraine was being tested in this study. The control group took nothing, while the placebo group took a medication clearly labeled as “placebo.” Then one group took a migraine drug labeled with its name. Those who took the placebo had results that were 50% effective when reducing pain during a migraine effect.

The placebo effect can stimulate the brain into believing that the body is being healed, creating a natural mechanism that encourages better health. The presence of this effect doesn’t indicate the success or failure of a medication or another process in a double-blind study. It may be an indication that the group receiving the placebo has a powerful internal mechanism that provides self-healing.

5. Some people can have a negative response to a placebo. There can be times when an individual doesn’t have a response to the placebo at all. When that outcome occurs, then the effects of a process or medication can receive a direct comparison to see if the real product is useful. Some people can have an adverse reaction to the placebo, even producing unwanted side effects as if they were taking a real medication. It all depends on how each person feels.

A study involving people with asthma showed that using a placebo inhaler caused patients to do no better on breathing tests than sitting and doing nothing. When researchers asked how they felt about using the product, they reported that the placebo was just as effective as the regular medicine they used.

6. Randomization must use a structured process to be useful. The most common example of using randomization when assigning people to a group in a double-blind study is to flip a coin. It is an action that’s random and cannot be predicted, which means it is likely to be a 50/50 scenario over time as it gets tossed frequently. Assigning people who come to a specific location based on a day of the week can influence the results of the study unintentionally because there are other dynamics that control the behavior. That bias would be in the data without anyone recognizing its presence since it was placed there in the initial design.

7. Most double-blind studies are too small to provide a representative sample. Winchester Hospital, which is a division of Beth Israel Lahey Health in Massachusetts, says that a good double-blind study should enroll at least 100 individuals, “preferably as many as 300.” Effective treatments can prove themselves in small trials, but research requires more people to establish patterns so that results can be verified. Even when you have hundreds, or sometimes thousands, of participants in this work, the results might not extrapolate to the general population.

There were more than 4,100 trials in progress for pain treatments in 2011, but the only new approvals given were for formulations or updated dosages for existing medications. Even when drugs get into the third phase of testing, the product only has a 60% chance to continue moving forward. Divergent results often create failure.

8. It doesn’t work well for functional disorders. The highest response rates for a placebo occur when researchers are looking into functional disorders like Irritable Bowel Syndrome. It also happens when there are imprecise endpoint measurements, as with Crohn’s disease. People who have other immune-response conditions like rheumatoid arthritis. The FDA even notes that the placebo response is steadily growing in the general population.

This disadvantage creates another limitation where the structure of a double-blind study may not provide useful information.

9. Double-blind studies are an expensive effort to pursue. A double-blind study takes several months to complete so that researchers can look at each possible variable. It may be necessary to complete several efforts using different groups to collect enough data. When corporations look at the cost of these efforts, it can be an expense that reaches several million dollars before its completion. Government studies can quickly reach $1 billion or more, depending on the extent of the work and the industry or product under consideration.

When the Tufts Center for the Study of Drug Development looked at the cost of creating and bringing a new drug to the market, the expense was pegged at $2.6 billion. That’s why new prescription medicines are so expensive. Even the clinical trials for FDA approval have an average cost of $19 million.

Double-blind placebo studies are often called the gold standard for testing medications. This description is at its most powerful when studying new psychiatric medications since the placebo effect is a psychological benefit. It is a process that improves on the experiments that compare the response of someone taking a pill with those who do not.

Since no one knows who is getting what in a double-blind study, the danger of a researcher accidentally communicating non-verbally about the expectation of an item to work or not gets eliminated.

When reviewing these double-blind study advantages and disadvantages, the benefits that come from this process can only be achieved when structures that counter the potential negatives are in place. It gives us a baseline from which to work, but there are no guarantees that results are achievable.