single blind assignment

Frequently asked questions

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

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

Frequently asked questions: Methodology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Snowball sampling is best used in the following cases:

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

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

Reproducibility and replicability are related terms.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are two subtypes of construct validity.

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

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

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

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

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

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

In statistics, dependent variables are also called:

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

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

Independent variables are also called:

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

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

Overall, your focus group questions should be:

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

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

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

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

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

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

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

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

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

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

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

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

Unstructured interviews are best used when:

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

The four most common types of interviews are:

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

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

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

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

Deductive reasoning is also called deductive logic.

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

Here are a few common types:

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

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

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

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

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

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

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

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

Triangulation can help:

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

But triangulation can also pose problems:

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

There are four main types of triangulation :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

These are four of the most common mixed methods designs :

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

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

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

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

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

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

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

Correlation coefficients always range between -1 and 1.

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

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Systematic error is generally a bigger problem in research.

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

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

Random and systematic error are two types of measurement error.

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

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

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

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

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

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

The difference between explanatory and response variables is simple:

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

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

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

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

There are 4 main types of extraneous variables :

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

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

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

In a factorial design, multiple independent variables are tested.

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

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

Advantages:

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

Disadvantages:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

If something is a mediating variable :

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There are five common approaches to qualitative research :

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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

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

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

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

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

When designing the experiment, you decide:

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

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

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

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

The validity of your experiment depends on your experimental design .

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

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

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

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

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

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

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

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

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

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

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

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What Is A 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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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.

What Is a Single-Blind Study?

Categories Research Methods

In psychology, a  single-blind study is a type of experiment or clinical trial in which the experimenters are aware of which subjects are receiving the treatment or independent variable, but the participants of the study are not.

A study in which both the experimenters and participants are unaware of who is receiving the independent variable and who is not is known as a double-blind study

Table of Contents

Reasons to Conduct a Single-Blind Study

There are a number of reasons why researchers might opt to use a single-blind study. In many cases, it can be a way to prevent participants from changing their behavior.

Study participants will sometimes change how they behave simply because they are part of an experiment. Sometimes participants in a study will try to guess what a study is about or what they think the experimenters are hoping to find.

This can lead them to change their behavior, which can ultimately skew the results. A single-blind study can help prevent this or minimize the effects of such demand characteristics.

Demand characteristics are cues that might help participants guess the purpose of an experiment or study.

Example of a Single-Blind Study

For example, imagine that researchers are performing a study to determine if a certain type of medication causes people to feel more alert. If participants knew that the researchers were testing a hypothesis that the drug increased alertness, they might start acting more alert after ingesting the medication.

By using a single-blind procedure and not telling the participants what they are looking for, people are less likely to inadvertently bias the results by changing their behavior.

Potential Problems

Research has shown that not using sufficient patient blinding in research can lead to significant bias in the results.

There are also times when unblinding may occur, which involves the participants becoming aware of their treatment condition or of the intentions of the experiment. Sometimes this happens when a participant is debriefed following an experiment, but it can also happen before a study ends, which can create problems with results.

Studies have found that nearly around 75% of participants in antidepressant studies are correctly able to guess their treatment condition. This can lead to bias, and in some cases, may exaggerate the effectiveness of the medications.

Moncrieff J, Wessely S, Hardy R. Meta-analysis of trials comparing antidepressants with active placebos .  The British Journal of Psychiatry . 1998;172(3):227-231. doi:10.1192/bjp.172.3.227

Perlis RH, Ostacher M, Fava M, Nierenberg AA, Sachs GS, Rosenbaum JF. Assuring that double-blind is blind . AJP. 2010;167(3):250-252. doi:10.1176/appi.ajp.2009.09060820

U.S. Department of Health and Human Services. Single-blind study .

Definition: The Single-Blind Research Method is a type of experimental design in which the participants or subjects in a study are unaware of certain key details or conditions, while the researchers conducting the study are aware of these details.

• The Purpose of Single-Blind Research Method:

Single-blind research is primarily employed to minimize bias and increase the objectivity of the study. By keeping the participants unaware of certain information, the researchers aim to reduce both conscious and unconscious biases that may influence the results. This method allows for a more accurate assessment of the true effects of the independent variable on the dependent variable.

• The Process of Single-Blind Research Method:

In single-blind studies, participants are typically kept unaware of various aspects, such as the specific treatment they are receiving, the presence or absence of a placebo, or group assignments. However, the researchers are aware of these details, which allows them to accurately monitor and measure the effects of the independent variable. In this way, the impact of participant bias or expectations can be minimized.

• Examples of Single-Blind Research Method:

An example of single-blind research is a medication trial, where participants are given either the experimental drug or a placebo. In this case, the participants are unaware of which group they belong to, but the researchers know which participants are receiving the actual drug and which ones are given the placebo. Another example can be found in educational research, where students are randomly assigned to different groups or teaching methods, without their knowledge of the purpose or intent of the study.

• Advantages of Single-Blind Research Method:

Single-blind research provides several advantages. It helps minimize bias and increase the internal validity of a study. By keeping participants blind to certain conditions, it reduces the likelihood of their behavior or responses being influenced by their expectations, beliefs, or preconceived notions. This enhances the accuracy and reliability of the study’s findings.

• Disadvantages of Single-Blind Research Method:

One of the main disadvantages of single-blind research is that it may not completely eliminate biases. Participants may still unconsciously pick up cues or indications regarding the treatment they are receiving, potentially affecting their behavior or responses. Additionally, single-blind studies may be more susceptible to placebo effects, as participants may form expectations about the intervention due to subtle hints or clues.

• Comparison to Double-Blind Research Method:

In contrast to single-blind research, double-blind research involves both the participants and the researchers being unaware of certain key details or conditions. Double-blind studies further reduce potential biases, as neither the participants nor the researchers are privy to the specific assignments or treatments. This method is particularly useful in studies where the likelihood of conscious or unconscious bias is high.

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

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

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

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

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

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

Table of contents

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

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

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

Single blinding

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

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

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

Double blinding

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

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

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

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

Triple blinding

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

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

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

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

Risk of unblinding

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

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

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

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

Inability to blind

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

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

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

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

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

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

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

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What is a single blind trial?

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There is no official definition of many terms used in randomised trials, including double blind, single blind, intention to treat, and so on. The term randomised does have precise technical meaning but it is often misused. Labels are valuable only if they have a unique meaning and are only used in the correct way.

John Williams queries the definition of a single blind trial. One publication in the BMJ states that in a single blind trial “either only the investigator or only the patient is blind to the allocation”.[1] The term is thus unhelpful without clarification. Double blind trials are just as confusing as single blind trials. A survey of physicians and a review of textbooks and reports revealed numerous interpretations of the designation “double-blind.”[2] Of key importance in both single and double blind trials is whether the outcome assessor is blinded.

Hence the CONSORT Statement avoids labels and asks for specific information: “Whether or not participants, those administering the interventions, and those assessing the outcomes were blinded to group assignment. If done, how the success of blinding was evaluated.”[3]

Likewise, in an article in which we tried to clarify the various “terminological tangles” associated with blinding, we wrote: “we urge that authors explicitly state what steps were taken to keep whom blinded. If they choose to use terminology such as single-, double-, or triple- blinding in reporting randomized controlled trials, they should explicitly define those terms.”[4]

Arguments about the correct meaning of “single-blind” are pointless.

1 Day SJ, Altman DG. Blinding in clinical trials and other studies. BMJ 2000;321:504.

2 Devereaux PJ, Manns BJ, Ghali WA, Quan H, Lacchetti C, Montori VM, et al. Physician interpretations and textbook definitions of blinding terminology in randomized controlled trials. JAMA 2001;285:2000-3.

3 Moher D, Schulz KF, Altman D for the CONSORT Group. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. JAMA 2001;285:1987-91. [see also www.consort-statement.com]

4 Schulz KF, Chalmers I, Altman DG. The landscape and lexicon of blinding in randomized trials. Ann Intern Med 2002;136:254-9.

Competing interests: No competing interests

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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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What is a Blinded Study?

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

Double-Blind Studies

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

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

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

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

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

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

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

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

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

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

Example Double-Blind Studies

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

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

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

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

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

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

Double-Blinding Procedure

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

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

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

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

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

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

For Example

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

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

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

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

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

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

Reduces risk of bias

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

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

Results can be duplicated

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

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

It tests for three groups

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

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

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

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

Applicable across multiple industries

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

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

Disadvantages

Inability to blind.

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

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

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

Small Sample Size

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

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

Negative Reaction to Placebo

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

It doesn’t reflect real-life circumstances

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

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

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

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

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

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

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

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

Are double-blind studies ethical?

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

What is the purpose of randomization using double blinding?

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

Why are double-blind experiments considered the gold standard?

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

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

Can blinding be used in qualitative studies?

Yes, blinding is used in qualitative studies .

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

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

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

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

StatPearls [Internet].

Double-blind study.

Sharoon David ; Paras B. Khandhar .

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Last Update: July 17, 2023 .

• Definition/Introduction

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

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

• Issues of Concern

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

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

• Clinical Significance

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

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

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

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

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

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page David S, Khandhar PB. Double-Blind Study. [Updated 2023 Jul 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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A chlorin e6 derivative-mediated photodynamic therapy for mild to moderate acne: A prospective, single-blind, randomized, split-face controlled study

Affiliations.

• 1 Institute of Photomedicine, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, PR China.
• 2 Department of Nursing, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, PR China. Electronic address: [email protected].
• 3 Institute of Photomedicine, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, PR China. Electronic address: [email protected].
• PMID: 39226754
• DOI: 10.1016/j.pdpdt.2024.104304

Background: Acne vulgaris is a chronic inflammatory skin disease involving the pilosebaceous unit.

Objective: To assess the efficacy and safety of a chlorin e6 derivative-mediated photodynamic therapy (STBF-PDT) in the treatment of mild to moderate acne patients.

Methods: In this prospective patient single-blind randomized split-face controlled study, patients diagnosed with mild to moderate acne were treated with four sessions of STBF-PDT on one-half of the face, while the other half were treated with the same dose of red-light treatment without photosensitizer. Follow-up assessment including the skin lesion clearance rate, facial fluorescence scattering spots on VISIA Porphyrins mode, and skin physiological parameters was conducted before and after treatment as well as 2 and 4 weeks after the final treatment.

Results: A total of 26 patients were recruited, of which 22 patients completed this study. STBF-PDT is significantly effective in improving lesions in patients with acne. The clearance rate of total lesions was 67.42±8.51 % in the STBF-PDT group and 41.05±11.97 % in the control group 4 weeks after the treatment (P < 0.001). The average clearance rate of inflammatory lesions was 84.41±7.13 % in the STBF-PDT group and 50.10±13.91 % in the control group, with a statistically significance (P < 0.0001). The skin sebum of the STBF-PDT side was significantly lower than that on the control side. There was no obvious adverse reaction especially no pain or reactive acne.

Conclusion: STBF-PDT may be a safe and effective treatment for mild to moderate acne and can significantly inhibit sebum secretion.

Keywords: Acne; Chlorin e6 derivative; Photodynamic therapy; Skin sebum.

Copyright © 2024. Published by Elsevier B.V.

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Declaration of competing interest None declared.

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Liposomal delivery enhances absorption of vitamin C into plasma and leukocytes: a double-blind, placebo-controlled, randomized trial

• Original Contribution
• Open access
• Published: 06 September 2024

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• Martin Purpura   ORCID: orcid.org/0000-0003-2591-5453 1 ,
• Ralf Jäger 1 ,
• Ashok Godavarthi 2 ,
• Dhananjaya Bhaskarachar 3 &
• Grant M. Tinsley 4  

L-Ascorbic acid (vitamin C) is an essential water-soluble vitamin that plays an important role in various physiological functions, including immune health. The stability of vitamin C in the gastrointestinal tract its bioavailability is limited. This study aimed to investigate if a liposomal form of vitamin C can increase absorption compared to standard vitamin C.

In a randomized, double-blind, placebo-controlled, crossover fashion, 19 males and 8 females ( n  = 27; 36.0 ± 5.1 years, 165.0 ± 6.9 cm, 70.6 ± 7.1 kg) ingested a single-dose of placebo (PLA), 500 mg vitamin C (VIT C), and 500 mg liposomal vitamin C (LV-VIT C, LipoVantage ® , Specnova, LLC, Tyson Corner, VA, USA). Venous blood samples were collected 0, 0.5-, 1-, 1.5-, 2-, 3-, 4-, 6-, 8-, 12-, and 24-hours after ingestion and were analyzed for plasma and leukocyte vitamin C concentration.

VIT C and LV-VIT C demonstrated significantly greater Cmax and AUC 0 − 24 in plasma and in leukocytes compared to placebo ( p  < 0.001). Additionally, LV-VIT C had significantly higher Cmax (plasma + 27%, leukocytes + 20%, p  < 0.001) and AUC 0 − 24 (plasma + 21%, leukocytes + 8%, p  < 0.001) values as compared to VIT C.

Liposomal formulation of vitamin C increases absorption into plasma and leukocytes.

Trial Registration

Clinical Trials Registry - India (CTRI/2023/04/051789).

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Introduction

L-Ascorbic acid (vitamin C) is a water-soluble vitamin and essential for human health due to its pleiotropic functions related to its ability to donate electrons [ 1 ]. As a co-factor it facilitates collagen biosynthesis, carnitine, and catecholamine metabolism, and enables dietary iron absorption [ 2 ]. Vitamin C is also essential for wound healing and for the repair and maintenance of cartilage, bones, and teeth [ 3 ]. In addition, vitamin C plays a major role in immunity while protecting immune cells against oxidative stress generated during infections [ 4 , 5 ].

For proper function as an effective antioxidant, vitamin C must be retained in the body at relatively high levels. Compared to many mammals which can easily produce vitamin C, humans do not have the capacity due to lacking the enzyme gulonolactone oxidase, which is required for vitamin C biosynthesis [ 6 ]. Therefore, humans must acquire vitamin C through diet. Dietary sources of vitamin C are fresh fruits and vegetables, like citrus fruits, berries, tomatoes, potatoes, bell peppers, and cruciferous leafy vegetables [ 7 ]. However, the actual dietary intake is lower than estimated due to the instability of vitamin C during cooking, storage, and food processing [ 8 , 9 ]. The average adult stores 1.2–2.0 g of vitamin C within the body and at a daily consumption of approximately 140 mg the total body pool of vitamin C will be saturated [ 10 ]. The average half-life of vitamin C of 10–20 days results in a turnover of 1 mg/kg body weight. Consequently, vitamin C must be regularly consumed through diet or dietary supplementation to maintain the vitamin C pool in the body.

The pharmacokinetics of vitamin C in humans is highly complex in contrast to other low molecular weight ingredients [ 11 ]. The sodium-dependent vitamin C transporters (SVCTs) are responsible for the majority of intestinal uptake, tissue distribution, and renal reuptake. Plasma concentrations peak within 120 to 180 min after ingestion [ 12 , 13 , 14 ]. Vitamin C is absorbed and retained in cells and tissues, such as leukocytes. Therefore, due to its correlation with dietary intake of vitamin C, both plasma and leukocyte vitamin C concentrations have been identified as efficacy markers for its bioavailability [ 15 ].

The greatest challenge in the use and application of dietary supplements or food products with vitamin C is to maintain stability. Degradation processes of vitamin C easily take place in aqueous medium, at high pH, in the presence of oxygen and metal ions. Moreover, for sufficient oral supplementation of vitamin C, it must be protected against potential reduction due to degradation in the gut. Liposomes with one or more lipid bilayers using a hydrophilic–hydrophobic interface, can be used to reduce the degradation of vitamin C [ 16 ]. Liposomes are spherical vesicles of a bilayer of amphiphilic molecules like phospholipids. Since their first discovery in the 1960s by Alec Bangham, this process usually creates multilamellar vesicles (MLVs), which are concentric lipid bilayers separated by aqueous compartments [ 17 , 18 ]. The first formulations were composed solely of natural lipids; at present they can include natural and/or synthetic lipids and surfactants. Since the 1960s, numerous methods have been developed to produce unilamellar liposomes of different sizes [ 19 , 20 ]. The size of these nearly spherical lipid vesicles can range from a few nanometers to several micrometers. However, liposomes applied to medical use range between 50 and 450 nm [ 21 ]. Previous studies indicated potentially improved absorption of liposomal vitamin C compared to standard vitamin C; however, those studies were either not randomized nor double-blind, lacked a placebo group, used unpractical, extremely high amounts of vitamin C (4–36 g), or were of short duration (6–8 h) [ 16 , 22 , 23 , 24 ].

The present study assessed the bioavailability of a single dose of 500 mg vitamin C in plasma and leukocytes following oral administration of vitamin C, liposomal vitamin C or placebo in male and female subjects over a period of 24 h. We hypothesized that vitamin C in liposomal form has better bioavailability in plasma and leukocytes compared to vitamin C and placebo.

Materials and methods

Participants.

The study was approved by the Shetty’s Hospital Ethics Committee (registration number: ECR/918/Inst/KA/2017/RR-20) on April 6, 2023 (CL/VT/01/BA/2023) and the study was registered with the Clinical Trials Registry - India (CTRI/2023/04/051789). The study was conducted under the ICH guidelines for Good Clinical Practice at the Shetty’s Hospital, Department of Medicine, Bommanahalli, Bengaluru 560 068, Karnataka, India, following the ethical principles of the Declaration of Helsinki. The study was initiated on April 24, 2023, and study was completed on May 12, 2023.

Twenty-seven subjects were screened for this study. The sample size was determined based on previous studies of liposomal vitamin C studies [ 16 ]. Subjects enrolled in the study needed to meet the following inclusion parameters: 18–45 years of age, weighing at least 50 kg; have not been consuming any vitamin C-containing supplements or foods for 24 h prior to testing. Only healthy subjects were enrolled with no evidence of underlying disease during the pre-study screening, determined by medical history and physical examination through a physician, performed within 7 days prior to the commencement of the study. Exclusion criteria included: participants who are allergic to vitamin C; participants with resting hypertension (> 140/90 mmHg) and pulse rate below 50/min or more than 100/min; participants with or a prior history or presence of significant cardiovascular, pulmonary, hepatic, renal, hematological, gastrointestinal, endocrine, immunologic, dermatologic, neurological, musculoskeletal or psychiatric disease; participants who has been hospitalized or underwent surgery within the last 4 weeks; participants with a history of myocardial infarction, stroke, peripheral arterial disease, gastrointestinal bleeding, hepaticimpairment, asthma, renal impairment, epilepsy and intracranial hemorrhage; participants who have taken over the counter or prescribed medications including any enzyme modifying drugs within the last 14 days prior to the study; participants who have a history of alcoholism, drug abuse or smoking; and participants who have difficulty with donating blood or a history of difficulty in swallowing.

Study materials

Vitamin C (VIT C) capsules, liposomal vitamin C capsules (LV-VIT C, LipoVantage ® , Specnova, LLC, Tyson Corner, VA, USA) and placebo (PLA, maltodextrin) were acquired from Molecules Food Solutions Pvt Ltd, Kerala, India. Subjects ingested optically identical 1 hard gel capsules of each of the study materials per setting each yielding 500 mg of vitamin C or placebo. The vitamin C content was confirmed by an independent third-party analysis (Interfield Laboratories, Kochi, India; VIT C: 507 mg vitamin C per capsule, Certificate of Analysis # KH 96018/2023, July 26, 2023; LV-VIT C: 505 mg vitamin C per capsule, Certificate of Analysis # KH 96017/2023, July 26, 2023). The liposomal form of vitamin C consisted of ascorbic acid, sunflower lecithin with a proprietary ratio of phospholipids, gum arabic and alginate as sources of polysaccharides that make up the polar core of the liposome and are also located on the outside of the liposome. The liposomal structure was confirmed using transmission electron cryomicroscopy (CryoTEM).

Study procedure

Prior to testing, each subject underwent screening and the consent visit to ensure eligibility and voluntary willingness to participate. After the written informed consent was obtained from the participants, their demographic data as well as medical history were recorded. A detailed physical examination including assessment of vital signs / parameters was done. All participants also underwent ECG and chest x-ray (PA view).

All participants provided blood for testing for hematological parameters such as hemoglobin, total leukocyte count, RBC count, platelet count, differential counts of neutrophils, lymphocytes, eosinophils, monocytes, basophils and ESR. All participants also underwent screening of biochemical parameters including renal function tests (blood urea nitrogen, serum uric acid, and serum creatinine), liver function tests (serum bilirubin, AST, (SGOT), ALT (SGPT), serum alkaline phosphatase, and serum albumin) and lipid profile (total cholesterol, HDL, LDL and VLDL). All participants also underwent testing of fasting blood glucose levels and routine urinalysis (color; appearance; specific gravity; pH; and presence of glucose, protein, bile salts, bile pigments, pus cells, epithelial cells, RBCs, casts, crystals, and bacteria). Serological screening included screening for HIV (HIV-1 and HIV2) and hepatitis B (HBsAg). Participants who were healthy without any signs or symptoms of any illness were enrolled in the study. Following enrollment, each volunteer completed 3 trials with 11 blood draws each in a randomized, double-blinded order separated by 3 days. Participants were not allowed any OTC medicines, herbal combinations, or prescription medications for at least 7 days prior to the study drug administration until the study completion period. Participants were also restricted from consuming alcohol, citrus fruits, smoking cigarettes, chewing tobacco, or consuming any caffeine containing product within 36 h of in-housing until all blood draws were completed. The diet was restricted such that no vitamin C containing products were consumed for at least 24 h prior to administration of the investigational products until all blood draws were completed. Participants were also restricted from donating blood from the day of the study enrolment until the end of the study; and from carrying out any strenuous physical exercise during the study period. During each trial, each volunteer reported to the laboratory in the morning following a 12‒hour overnight fast (except for water). The participants were admitted in-house into the study center after they underwent successful screening. The study materials were administered under supervision and blood was drawn at pre-determined intervals during the in-house admission. The participants were allowed to go out of the in-house facility during the washout periods and the participants again were admitted in-house for second testing where they again received the study materials according to the sequence of allocation. The participants then underwent a second washout period where they again were allowed to go outside of the in-house facility. The participants again were in-housed for the third time where they received the study materials as per the schedule and finally the participants finished the study after blood was withdrawn as per the study protocol.

This study was conducted as a randomized, double-blind, placebo-controlled crossover study. All eligible participants were equally randomized into three groups of 9 participants each by simple randomization into group 1, group 2 and group 3. Participants in group 1 were sequentially allotted to treatment A, B and C, participants in group 2 sequentially were allotted to treatment B, C and A; and participants in group3 sequentially were allotted to treatment C, A and B (see Fig.  1 ). To maintain blindness of the study treatments, all the test products were identical in appearance. Study materials were coded centrally with randomization numbers as per the computer-generated randomization schedule.

Subjects were not allowed to drink water between 1 h before to 1 h after the ingestion of the Investigational products, except while dosing. The test materials were administered orally in sitting posture, with 240 mL of water at ambient temperature, as a single dose, as per the randomization code list. Participants were checked-in to the clinical facility the evening prior to the administration of the test materials (Day 0) and were given standardised meal for dinner consisting of chapathi made of wheat flour, 1 bowl of rice (200 g), fried vegetable (50 g), sambar (stew of lentil) and water (500 mL). On Day 1, food was provided at 1-hour (breakfast: idly, made of rice flour and black lentil (200 g), chutney, made of grated coconut and peanuts, and water (150 mL)); 4-hours (snack: coffee or tea (one cup: 75 mL), biscuit (50 g) and water (150mL)); 6-hours (lunch: 1 bowl of rice (300 g), fried vegetable (50 g), Sambar (stew of lentil), and water (250 mL)); 8-hours (snack: coffee or tea (one cup: 75 mL), biscuit (50 g) and water (150 mL)); and 13-hours (dinner: chapathi, made of wheat flour, 1 bowl of rice (200 g), friend vegetable (50 g), Sambar (stew of lentil) and water (250 mL)) post administration. On Day 2, the final blood draw was performed by 24-hours after the dosing followed by standard breakfast (Idly made of rice flour and Black lentil (200 g), chutney made of grated coconut and peanuts and water (250 mL)) before the participant left the clinical facility.

During each visit, the subjects were seated comfortably while a catheter was introduced into a forearm vein by a qualified phlebotomist. After equilibration, a baseline blood sample was collected, and one of three treatment dosages (LV-VIT C, VIT C, or PLA) was administered with water. Blood samples were then drawn at 0.5-, 1-, 1.5-, 2-, 3-, 4-, 6-, 8-, 12-, and 24-hours intervals following test product administration. Each subsequent trial was separated by at least 3 days as a washout period and followed identical study procedures, except for the consumption of a different vitamin C or placebo formulation (see Fig.  1 ).

Schematic overview of research design

Adverse event/safety monitoring

During the entire period, the participants were monitored for any adverse events. Vital signs: blood pressure (sitting or semi-supine position); radial pulse rate; temperature; respiratory rate was assessed on admission, and at different intervals: prior to dosing at 0 h, 15 min, 30 min, 1 h, 2 h, 4 h, 8 h, 12 h, and 24 h prior to the time of participant checkout. Vital signs were also recorded 2 h prior to the administration of the study materials. A window period of ± 15 min from the scheduled time point was allowed for post dose recording of vital signs. Physical examination was performed at admission and 1, 2, 4, 8, 12 and 24 h after intake of the study materials.

Sample collection

During each timepoint, 10 mL of blood were drawn off the catheter into vacutainer tubes. Blood tubes were centrifuged at 5000×g for 15 min. Following centrifugation, plasma and buffy coat (leukocytes) were separated into labeled microcentrifuge tubes using micropipette as individual aliquots. The samples were stored in a ‒80 °C freezer until analysis and thawed only once prior to the respective analysis to avoid degradation.

Sample preparation and analysis

Samples were prepared for HPLC/MS/MS analysis by using Solid Phase Extraction (SPE) method. At the time of analysis, the samples were removed from the deep freezer and kept in the room temperature and allowed it to thaw. SOLA CX SPE Columns C18-(50 μm, 70 A) solid phase extraction cartridge was conditioned with methanol and water sequentially. To this, 250 µL aliquot of plasma containing vitamin C was pipetted and the SPE cartridge was loaded. The cartridge was washed with 1.0 mL of methanol. The drug was eluted from the cartridge using water and 300 µL of mobile phase. The samples were injected into the HPLC system for analysis with UV absorbance at 243 nm. The following parameters were assessed: area under the curve (AUC 0 − 24 ), maximum observed concentration (Cmax), time of maximum concentration (Tmax), mean and percentage changes of vitamin C from baseline (0 h) in plasma and leukocytes.

Statistical analysis

Cmax and AUC 0 − 24 data were analyzed using one-way analysis of variance with repeated measures, with condition specified as the within-subjects factor. Data were grouped by condition and inspected for extreme outliers (i.e., values above Q3 + 3 x IQR or below Q1–3 x IQR), normality, and sphericity. Outliers were detected only in the PLA condition ( n  = 1 for leukocyte Cmax, n  = 2 for leukocyte AUC and n  = 1 for plasma AUC). As these points were only present in PLA and were not influential in the overall outcomes, all data points were retained in the analysis. Normality was examined via quantile-quantile plots grouped by condition, and data were approximately normally distributed. When sphericity was violated based on Mauchly’s test, the Greenhouse-Geisser correction was applied. Following a statistically significant effect of condition, post hoc pairwise t-tests were performed, using the Bonferroni correction for multiple comparisons. The generalized eta-squared (η2G) effect size was calculated to accompany the one-way analysis of variance test, and Cohen’s d effect sizes were calculated to accompany each pairwise t-test. Due to the nature of the data, Tmax values were analyzed using the non-parametric Friedman test, accompanied by Kendall’s W effect size. Following a statistically significant effect of condition, post hoc Wilcoxon signed-rank tests were performed. For all tests, statistical significance was accepted at p  < 0.05. Data are presented as mean ± SD unless otherwise noted. Data were analyzed in R software v. 4.3.1 [ 25 ] with the rstatix package v. 0.7.2 [ 26 ].

Twenty-seven participants (8 F, 19 M) completed the present study ([mean ± SD] age 36.0 ± 5.1 y, height 165.0 ± 6.9 cm, weight 70.6 ± 7.1 kg, BMI 25.9 ± 1.6 kg/m 2 , systolic blood pressure 120.0 ± 7.1 mmHg, diastolic blood pressure 77.0 ± 7.8 mmHg) (Fig.  2 ).

CONSORT flow diagram

Adverse events

None of the participants reported any adverse events during or after the study period. All the participants tolerated the investigational products equally well without any evidence of any abnormality of vital signs or any abnormality during physical examination, which were assessed at different time intervals after administration of the study medications.

Plasma Cmax significantly differed between conditions (LV-VIT C 8,610 ± 182 ng/mL, VIT C 6,271 ± 444, PLA 379 ± 59; p  < 0.001, η2G = 0.99; Fig.  3 A). Additionally, each condition significantly differed based on post hoc t -tests ( p  < 0.001 for each comparison; Fig.  4 A). Cohen’s d effect sizes were 13.2 for VIT C v. PLA, 46.0 for LV-VIT C v. PLA, and 4.9 for LV-VIT C v. VIT C. Expressed as percentages, plasma Cmax was 2,174% higher with LV-VIT C than PLA and 1,556% higher with VIT C than PL, on average. The percent difference between LV-VIT C and VIT C was 27%, with higher Cmax values in the LV-VIT C condition.

Effects of liposomal delivery on vitamin C absorption. Raw concentrations of vitamin C in plasma ( A ) and leukocytes ( B ) are displayed for 24 h following ingestion of liposomal vitamin C (LV-VIT C), standard vitamin C (VIT C), or placebo (PLA)

For plasma Tmax, a significant effect of condition was observed ( p  < 0.001, Kendall’s W = 0.40) For LV-VIT C and VIT C, all participants demonstrated a Tmax value of 4 h. For PLA, Tmax values were 3 ± 1 h (median ± IQR). Post hoc tests indicated significant differences between LV-VIT C and PLA ( p  = 0.048) and between VIT C and PLA ( p  = 0.048).

Plasma AUC 0 − 24 significantly differed between conditions (LV-VIT C 72,358 ± 4,044 ng/mL * 24 h, VIT C 57,152 ± 4,846, PLA 4,242 ± 486; p  < 0.001, η2G = 0.98), with post hoc t -tests indicating a difference between each comparison ( p  < 0.001 for each; Fig.  4 B). Cohen’s d effect sizes were 10.9 for VIT C v. PLA, 16.2 for LV-VIT C v. PLA, and 2.8 for LV-VIT C v. VIT C. Expressed as percentages, LV-VIT C and VIT C exhibited values that were 1,605% and 1,247% higher than PL, on average, with a 21% difference between LV-VIT C and VIT C conditions.

Leukocyte Cmax significantly differed between all conditions (LV-VIT C 6,369 ± 179 ng/mL, VIT C 5,088 ± 267, PLA 315 ± 29; p  < 0.001, η2G = 0.99; Fig.  3 B). Additionally, each condition significantly differed based on post hoc t -tests ( p  < 0.001 for each comparison; Fig.  4 C). Cohen’s d effect sizes were 17.7 for VIT C v. PLA, 33.0 for LV-VIT C v. PLA, and 6.2 for LV-VIT C v. VIT C. Expressed as percentages, leukocyte Cmax was 1,922% higher with LV-VIT C than PLA and 1,515% higher than VIT C than PL, on average. The percent difference between LV-VIT C and VIT C was 20%, with higher Cmax values in the LV-VIT C condition.

For leukocyte Tmax, no significant effect of condition was observed ( p  = 0.72, Kendall’s W = 0.01) For LV-VIT C and VIT C, all participants demonstrated a Tmax value of 4 h. For PLA, Tmax values were 4 ± 0.5 h (median ± IQR).

Leukocyte AUC 0 − 24 significantly differed between conditions (LV-VIT C 53,277 ± 1,738 ng/mL * 24 h, VIT C 48,922 ± 2,548, PLA 3,439 ± 789; p  < 0.001, η2G = 0.99), with post hoc t -tests indicating a difference between each comparison ( p  < 0.001 for each; Fig.  4 D). Cohen’s d effect sizes were 17.8 for VIT C v. PLA, 26.2 for LV-VIT C v. PLA, and 1.6 for LV-VIT C v. VIT C. Expressed as percentages, LV-VIT C and VIT C exhibited values that were 1,449% and 1,323% higher than PL, on average, with an 8% difference between LV-VIT C and VIT C conditions.

Pharmacokinetic comparison of vitamin C absorption. In plasma ( A , B ) and leukocytes ( C , D ), a significant benefit of liposomal delivery (LV-VIT C) on vitamin C maximal concentrations (Cmax) and area under the curve over 24 h (AUC 0 − 24 ) was observed. #=significant difference compared to placebo (PLA). *=significant difference compared to standard vitamin C (VIT C)

This randomized, double-blind, placebo-controlled, crossover study examined the bioavailability of liposomal and standard vitamin C following a single oral administration compared to placebo into plasma and leukocytes. Liposomal vitamin C significantly increased maximum plasma (+ 27%) and leukocytes (+ 20%) concentrations compared to non-liposomal vitamin C over a period of 24 h.

A first pilot study investigating phosphatidylcholine-based liposomal vitamin C used a single blind design, measuring plasma levels in just two subjects. Following oral single ingestion of a high dose (5 g, 20–36 g) of vitamin C, blood draws were taken over 6 h. At the 5 g dose, the liposomal and standard formulation showed similar plasma responses. Liposomes are absorbed from the gut into the liver, before being released into the blood resulting in a slower onset of peak levels in the 20 g dose [ 23 ].

Orally administered vitamin C is less bioavailable compared to infusing vitamin C. Encapsulating vitamin C in liposomes increases absorption of a single 4 g dose of vitamin C compared to standard vitamin C, however, bioavailability is still less than intravenous administration [ 24 ]. A high dose (10 g of vitamin C) of phosphatidylcholine-based liposomal vitamin C, with a concentration of 20% by weight, administered as a liposomal suspension showed increased absorption compared to an aqueous solution of vitamin C [ 16 ]. High-dose (5 g of vitamin C) phosphatidylcholine-based liposomal vitamin C, with a concentration of 66% by weight, showed a significant increase in plasma vitamin C levels in an open-label study [ 27 ]. Our study used a low dose of vitamin C (500 mg) compared to the previous studies investigating liposomal vitamin C formulations (4–36 g), an amount that is more in line with the generally recommended intake of vitamin C for healthy people, and for groups that are more likely to be at risk of vitamin C inadequacy. In addition, this is the first study with liposomal vitamin C following rigorous scientific standards for clinical studies, specifically a randomized, double-blind, placebo-controlled design.

Vitamin C is linked to immune health which depends on the incorporation of vitamin C into leukocytes. Our study showed an increase in leukocyte vitamin C concentration with a liposomal form of vitamin C in addition to increases in plasma. Previously, 1,000 mg of a non-liposomal commercial form of vitamin C that contained calcium ascorbate and vitamin C metabolites, including dehydroascorbate, calcium threonate, and 4-hydroxy-5-methyl-3(2 H)-furanone was shown to increase vitamin C levels in leukocytes from baseline values; however, similar increase in vitamin C levels was noted from standard vitamin C and placebo. In addition, the formulation failed to increase plasma concentrations compared to standard vitamin C [ 28 ].

Conclusions

Liposomal vitamin C formulation (LipoVantage ® ) significantly increased plasma and leukocyte vitamin C levels compared to standard non-liposomal vitamin C and placebo. This may be due to increased absorption of encapsulated vitamin C from the gastrointestinal tract, protection against enzymatic degradation in the gastrointestinal tract and the plasma and enhanced penetration and retention by the leukocytes.

Data availability

The datasets generated during the current study are available upon request.

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Acknowledgements

This study was conducted by Radiant Research Services Pvt. Ltd. (Bangalore, India).

Financial support was given by Specnova, LLC, Tyson Corner, VA, USA.

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Energy Balance & Body Composition Laboratory, Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX, USA

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Contributions

Conceptualization: RJ, MP, AG; project management: AG; principal investigator: DB; writing—original draft preparation: MP, and RJ; writing—review and editing: RJ, GMT, MP, DB and AG; statistical analysis was performed by GMT.

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Correspondence to Martin Purpura .

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Purpura, M., Jäger, R., Godavarthi, A. et al. Liposomal delivery enhances absorption of vitamin C into plasma and leukocytes: a double-blind, placebo-controlled, randomized trial. Eur J Nutr (2024). https://doi.org/10.1007/s00394-024-03487-8

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Received : 07 December 2023

Accepted : 26 August 2024

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DOI : https://doi.org/10.1007/s00394-024-03487-8

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