difference between simple sampling and random assignment

Frequently asked questions

What’s the difference between random assignment and random selection.

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.

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.

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

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

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

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

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

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

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

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

If something is a mediating variable :

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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• An Overview

Simple Random Sampling

Stratified random sampling, key differences, advantages and disadvantages, the bottom line.

• Marketing Essentials

Simple Random Sample vs. Stratified Random Sample: What’s the Difference?

Thomas J Catalano is a CFP and Registered Investment Adviser with the state of South Carolina, where he launched his own financial advisory firm in 2018. Thomas' experience gives him expertise in a variety of areas including investments, retirement, insurance, and financial planning.

Simple Random Sample vs. Stratified Random Sample: An Overview

In statistical analysis, the population is the total set of observations or data that exists. However, it is often unfeasible to measure every individual or data point in a population.

Instead, researchers rely on samples. A sample is a set of observations from the population. The sampling method is the process used to pull samples from the population.

Simple random samples and stratified random samples are both common methods for obtaining a sample. A simple random sample is used to represent the entire data population and randomly selects individuals from the population without any other consideration. A stratified random sample , on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics. Therefore, a stratified sampling strategy will ensure that members from each subgroup are included in the data analysis.

Key Takeaways

• Simple random and stratified random samples are statistical measurement tools.
• A simple random sample takes a small, basic portion of the entire population to represent the entire data set.
• Stratified random sampling divides a population into different groups based on certain characteristics, and a random sample is taken from each.

Simple random sampling is a statistical tool used to describe a very basic sample taken from a data population. This sample represents the equivalent of the entire population.

The simple random sample is often used when there is very little information available about the data population, when the data population has far too many differences to divide into various subsets, or when there is only one distinct characteristic among the data population.

For instance, a candy company may want to study the buying habits of its customers in order to determine the future of its product line. If there are 10,000 customers, it may use 100 of those customers as a random sample. It can then apply what it finds from those 100 customers to the rest of its base.

Statisticians will devise an exhaustive list of a data population and then select a random sample within that large group. In this sample, every member of the population has an equal chance of being selected to be part of the sample. They can be chosen in two ways:

• Through a manual lottery, in which each member of the population is given a number. Numbers are then drawn at random by someone to include in the sample. This is best used when looking at a small group.
• Computer-generated sampling. This method works best with larger data sets, by using a computer to select the samples rather than a human.

Using simple random sampling allows researchers to make generalizations about a specific population and leave out any bias. This can help determine how to make future decisions. That way, the candy company from the example above can use this tool to develop a new candy flavor to manufacture based on the current tastes of the 100 customers.

However, keep in mind that these are generalizations, so there is room for error. After all, it is a simple sample. Those 100 customers may not have an accurate representation of the tastes of the entire population.

Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets. These groups are based on certain criteria, then samples are randomly chosen from each in proportion to the group’s size vs. the population.

This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population. However, the researchers must ensure that the strata do not overlap. Each point in the population must only belong to one stratum so that each point is mutually exclusive . Overlapping strata would increase the likelihood that some data are included, thus skewing the sample.

The candy company may decide to use the random stratified sampling method by dividing its 100 customers into different age groups to help make determinations about the future of its production.

Portfolio managers can use stratified random sampling to create portfolios by replicating an index such as a bond index.

The simple random sample is often used when:

• Very little information is available about the data population.
• The data population has too many differences to divide into various subsets.
• Only one characteristic is distinct among the data population.

Stratified random samples are used with populations that can be easily broken into different subgroups or subsets based on certain criteria. Samples are randomly chosen from each proportional to the group’s size vs. the population.

Stratified random sampling offers some advantages and disadvantages compared to simple random sampling. Because it uses specific characteristics, it can provide a more accurate representation of the population based on what’s used to divide it into different subsets. This often requires a smaller sample size, which can save resources and time. In addition, by including sufficient sample points from each stratum, the researchers can conduct a separate analysis on each individual stratum.

But more work is required to pull a stratified sample than a random sample. Researchers must individually track and verify the data for each stratum for inclusion, which can take a lot more time compared with random sampling.

How Does Simple Random Sampling Work?

Simple random sampling is used to describe a very basic sample taken from a data population. This statistical tool represents the equivalent of the entire population.

How Does Stratified Random Sampling Work?

Stratified random samples are used with populations that can be easily broken into different subgroups or subsets based on certain criteria. Samples are then randomly chosen from each in proportion to the group’s size vs. the population.

How Do Simple Random and Stratified Random Sampling Benefit Researchers?

Simple random sampling lets researchers make generalizations about a specific population and leave out any bias. This can help determine how to make future decisions.

Stratified random sampling lets researchers make selections from each subgroup, the size of which is based on its proportion to the entire population. However, the researchers must make sure that the strata do not overlap.

Simple random samples and stratified random samples are both common methods for obtaining a sample. A simple random sample represents the entire data population and randomly selects individuals from the population without any other consideration. A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics—thus ensuring that members from each subgroup are included in the data analysis.

ScienceDirect. “ Simple Random Sample .”

Qualtrics XM. “ Stratified Random Sampling: Definition & Guide .”

Finance Train. “ Stratified Random Sampling .”

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Video: Random Sample vs Random Assignment

Additional resources.

- Students often have difficulty grasping the  difference between random sample and random  assignment. This is completely understandable  because both words have the word random in it.  However, understanding the difference  between the two concepts is essential.  This video will address exactly this  issue. By the end of this video,  you should be able to distinguish the difference  between a random sample and a random assignment.  Say we want to run an experiment to see  whether a treatment we're interested in  has some effect. As an example, let's say  that a politician wants to publish an ad,  and we want to know whether the ad  will increase support among voters  for that politician. The first step is  defining the population. In this case, the  population would be all voters. The second step  is drawing a random sample from the population.  Drawing a random sample assures that we have a  sample that is representative of the population.  Remember that this allows us to make inferences  about the population from our sample.  Third, to find out if a treatment has an effect,  we want to randomly assign some units to the  control group and some units that the treatment  group. The module on experiments explains  why randomizing the treatment is desired.  But just to recap, it's because, as can  be seen here, it creates a treatment  and a control group that are  very similar to each other.  If the treatment has any effect, we  can be fairly sure that it's due to  the treatment itself and not because the  treatment and control groups are different.  Once we have our treatment and control groups,  we want to give the treatment group the  treatment, in this case show them the ad,  and not give the control group the treatment, so  not show them the ad. After this has been done,  we want to measure support for this politician  in both the treatment and the control group.  When we compare the outcomes of the treatment  and control groups, if we find any difference  in support for this politician we can be  fairly confident that the ad had this effect.  So notice that a random sample are the  units that we want to collect data on  while random assignment decides which of  these units get the treatment and which don't.

Previous: Knowledge Check: Population and Samples, Margin of Error

Next: quick recap: random sample vs random assignment.

Difference Between Random Sampling and Random Assignment?

• Post author: Peter
• Post published: September 26, 2022
• Post category: Uncategorized

Random assignment is used in experimental research to place participants into different treatment groups.

Random sampling is a method of selecting people for a study. Random assignment splits the sample participants into two groups: control and experimental.

What is the difference between sampling and random sampling?

A representative sample is a group or set of factors or instances that adequately replicates the larger group according to whatever characteristic or quality is under study.

What is an example of random assignment?

Imagine if a researcher wanted to know if drinking a cup of coffee before an exam would improve test performance. Each person is assigned to either the control group or the experimental group after being randomly selected from a pool of participants.

Why are random sampling and random assignment used?

Random sampling and random assignment improve internal and external validity of your study.

What is the purpose of random assignment in an experiment?

Random assignment is a procedure used to create multiple study groups that include participants with similar characteristics so that the groups are the same at the beginning of the study. The procedure involves randomly assigning people to an experimental treatment or program. In studies that involve random assignment, participants will usually get a new treatment or program, or nothing at all. Random assignment doesn’t allow the researcher or participant to choose the group to which they are assigned.

What is random sampling in research?

Simple random sampling is a type of sampling where the researcher randomly selects a group of people. The members of the population have the same chance of being selected. As much data as possible is collected from the random subset.

Is random sampling and selection the same?

Random assignment and random selection are both used interchangeably, though the terms refer to entirely different processes. Sample members are selected from the population for inclusion in the study through random selection. Random assignment is an aspect of experimental design in which study participants are assigned to a treatment group.

How do you know if you should use a random sample or random assignment?

It’s acceptable for further analysis when the random variation between groups is very low. When you have a large sample, this is especially true. When it is ethically possible, you should always use random assignment in studies.

Is random assignment or random selection more important?

The researcher can use the results of the study to generalize to the larger population if random selection is used. Nonrandom assignment leads to groups that are not equivalent, meaning that the effect of the treatment might be different at the beginning than at the end. A strong research design will use both random selection and random assignment to ensure both internal and external validity, as the consequences of random selection and random assignment are very different.

What is an example of random selection?

An example of a random sample would be the names of 25 employees from a company with 250 employees. The sample is random because each employee has an equal chance of being chosen, and the population is all 250 employees. Random sampling can be used in science to conduct tests.

Which happens first a random sample or a random assignment?

Random selection is the process of drawing a sample of people. Random assignment is the process of assigning a sample to different groups in a study.

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• Open access
• Published: 24 August 2024

The impact of religious spiritual care training on the spiritual health and care burden of elderly family caregivers during the COVID-19 pandemic: a field trial study

• Afifeh Qorbani 1 ,
• Shahnaz Pouladi 2 ,
• Akram Farhadi 3 &
• Razieh Bagherzadeh 2  

BMC Nursing volume  23 , Article number:  584 ( 2024 ) Cite this article

68 Accesses

Metrics details

Family caregiving is associated with many physical and psychological problems for caregivers, but the effect of spiritual support on reducing their issues during a crisis is also the subject of research. The study aims to examine the impact of religious spiritual care training on the spiritual health and care burdens of elderly family caregivers during the COVID-19 pandemic.

The randomized controlled field trial involved 80 Iranian family caregivers in Bushehr City, who were selected by convenience sampling based on the inclusion criteria and divided into experimental (40 people) and control (40 people) groups by simple random sampling in 2021 and 2022. Data collection was conducted using spiritual health and care burden questionnaires using the Porsline software. The virtual intervention included spiritual and religious education. Four virtual sessions were held offline over two weeks. The first session was to get to know the participants and explain the purpose, The second session focused on the burden of care, the third on empowerment, and the fourth on mental health and related issues. In the control group, daily life continued as usual during the study.

Mean changes in existential health (3.40 ± 6.25) and total spiritual health (5.05 ± 11.12) increased in the intervention group and decreased in the control group. There were statistically significant differences between the two groups for existential health (t = 3.78, p  = 0.001) and spiritual health (t = 3.13, p  = 0.002). Cohen’s d-effect sizes for spiritual health and caregiving burden were 0.415 and 0.366, respectively. There was no statistically significant difference in mean changes in religious health ( p  = 0.067) or caregiving burden ( p  = 0.638) between the two intervention and control groups.

Given that the religious-spiritual intervention had a positive effect on existential health and no impact on religious health or care burden, it is recommended that comprehensive planning be undertaken to improve the spiritual health of family caregivers to enable them to better cope with critical situations such as a COVID-19 pandemic.

Trial registration

IRCT code number IRCT20150529022466N16 and trial ID number 48,021. (Registration Date2020/06/28)

Peer Review reports

With the global outbreak of COVID-19 on January 12, 2020, and the highly contagious nature of this virus, the World Health Organization issued protocols for limiting community interactions worldwide [ 1 ]. While individuals of all ages are susceptible to COVID-19, The high incidence of infection in older people, the greater severity of the disease, and the increased mortality are significant challenges in implementing appropriate preventive measures and future strategies to protect against this disease in the geriatric population [ 2 , 3 ]. According to the US Centers for Disease Control and Prevention, 31% of COVID-19 cases, 45% of hospitalizations, 53% of intensive care unit admissions, and 80% of COVID-19-related deaths in the United States occur in the elderly [ 4 ].

During the COVID-19 crisis, elderly people required various forms of assistance, including telephone and digital visits, with most of these services provided by family members [ 5 ], Park (2021) reported that long-term caregivers (> 1 year) had more negative somatic physical symptoms (headaches, body aches, and abdominal discomfort), worse mental health, and more significant fatigue than non-caregivers [ 6 ]. Family caregivers can only provide up to 80% of the required care to seniors with Multiple chronic conditions in the community, and they are also responsible for the majority of the costs and shoulder the related burden. This increased reliance on family caregivers has, in turn, heightened their care burden. The burden of care is a significant issue globally, with millions of individuals taking on caregiving responsibilities for their loved ones. The care burden encompasses various dimensions, including time-dependent, evolving, physical, social, and emotional aspects, making it a complex and highly individualized concept [ 7 ]. It often results from a negative imbalance between caregiving responsibilities and other obligations [ 8 ].

In Iran, like in many other countries, this burden can have profound implications on caregivers’ physical, emotional, and financial well-being. By introducing the concept of spiritual health into the discourse, we aim to shed light on a potentially overlooked aspect that could provide additional support and resilience to caregivers. Statistics indicate that caregivers who report a strong sense of spiritual well-being often exhibit lower levels of stress, anxiety, and depression, highlighting the importance of addressing this dimension in caregiving research. The existing literature on caregiver burden focuses mainly on caregiving’s physical and emotional aspects. While these studies provide valuable insights, there is a noticeable gap in understanding the role of spiritual health in mitigating the burden of care. Further exploration is needed to investigate how spiritual well-being can influence the overall caregiving experience and contribute to the well-being of the caregiver and the care recipient. In Iranians’ religious and national culture, the elderly hold a revered position and are highly respected. Reflecting on this cultural perspective, the Prophet of Islam stated, that respecting older people of my community is the same as respecting me [ 9 ]. This cultural context is evident in the fact that 86.4% of elderly individuals in Iran, according to statistics from the welfare organization, live with their children and spouses [ 10 ]. However, when caregiving responsibilities increase, they can overshadow the multiple health dimensions of the older people’s family members, including physical, psychological, social, and spiritual aspects. Coping strategies, such as spiritual-religious approaches, are often employed to manage the challenges [ 11 ].

There are two dimensions to spiritual health: religious and existential. Religious health refers to how a person understands his or her spiritual well-being when connected to a higher power. Conversely, existential health centers on an individual’s capacity for adaptation to their being, the societal landscape, and the broader environment [ 12 ]. In the past, the significance of spirituality in effectively managing stress was often underestimated; however, recent years have seen increased attention from researchers [ 11 , 13 , 14 ]. It is important to note that the understanding of spirituality is influenced by culture and religion, and its implications may vary for different individuals [ 15 ]. The current research gap lies in the lack of comprehensive studies that assess the intersection of spiritual health and care burden in the Iranian caregiving landscape. While some research exists on the broader topic of spirituality and health, there is a need for targeted investigations that consider the unique cultural and religious factors that shape the Iranian perspective on caregiving. Understanding these nuances can provide valuable insights into how spiritual care practices can be effectively integrated into support systems for caregivers in Iran. To the best of our knowledge, no previous study has investigated the impact of religious-spiritual care training on the spiritual health and care burden of family caregivers of older people during the COVID-19 pandemic. Given the critical role of nurses as caregivers for family and elderly health along with their supportive function [ 16 ], it is essential to identify caregivers at risk during critical situations and address their spiritual needs as part of community-oriented care. The study aimed to examine the impact of religious spiritual care training on the spiritual health and caregiving burden of older family carers during the COVID-19 pandemic. By thoroughly exploring the relationship between spiritual health and the caregiving burden of older family carers, we aim to identify potential strategies and interventions that can improve the well-being of caregivers and the overall quality of care provided to care recipients in Iran.

Study design

This study utilized a randomized controlled field trial design. The choice of a field randomized controlled trial for this study provides a rigorous and systematic approach to evaluating the effectiveness of a spiritual health intervention on care burden among Iranian caregivers. This design ensures internal validity, generalizability, and ethical soundness, thereby strengthening the overall quality of the research findings.

Participants and data collection

Participants were selected from the home care department of the comprehensive rehabilitation service center for the elderly in Mohammadieh, Bushehr City (affiliated with the welfare organization), and four comprehensive urban health centers in Bushehr Port, specifically Kheybar, Quds, Meraj, and Shohada centers. The inclusion criteria encompass caring for elderly individuals who showed a degree of dependence in at least one of their six daily activities, as defined by Katz’s criteria for activities of daily living (ADL). Additionally, caregivers had to possess literacy skills (reading and writing), with at least six months having elapsed since the commencement of their caregiving responsibilities. Furthermore, inclusion criteria require a family relationship between caregivers and elderly individuals in their care, cohabitation with older people, and delivering at least 40 h of care per week. Caregivers had to be at least 18-year-old Shia Muslims. The exclusion criteria dictated that the caregivers be excluded from the study under certain conditions, including the death of either the caregiver or the elderly individual during the study, refusal to continue participation in the study, the presence of neurological and psychiatric diseases, or the use of neuropsychiatric drugs, self-reported drug or alcohol addiction, or prior involvement in a spiritual-religious educational program related to elderly care.

Sample size

Based on the effect sizes observed in the studies by Hosseini et al. (2016) [ 17 ], Mahdavi et al. (2016) [ 18 ], and Moeini et al. (2012) [ 19 ], with a Type I error rate of 0.50 and a power of 80%, and using the G Power 3.1.9.2 software, the required sample size for the two-group test was approximately 80 individuals, with 40 participants in each group. Eligible elderly family caregivers were selected from available candidates and randomly assigned to either the test or control group (Fig.  1 ). Randomization was done using Random Allocation software and by a person who did not know the participants and did not know their characteristics.

Consort diagram

Instruments

The data collection instruments used in this study consisted of a demographic information form, along with the spiritual health questionnaire developed by Paloutzian and Ellison (1982) and the care burden questionnaire designed by Novak and Guest (1989).

Demographic information form

This form collected information about the caregiver, including age, number of children, family relationship to older people, level of education, occupation, income, and type of housing.

Spiritual health questionnaire (Paloutzian and Ellison, 1982)

The Spiritual Health Questionnaire, developed by Paloutzian and Ellison in 1982, is widely used to assess an individual’s spiritual well-being and beliefs. This questionnaire consists of 20 items that explore different aspects of spirituality, including beliefs, practices, values, and experiences. Participants are asked to respond to statements about spirituality on a six-point Likert scale, with responses to agree strongly or to disagree strongly. This questionnaire includes two subscales: (1) Religious well-being (10 items): This subscale assesses how an individual’s religious beliefs, values, and practices contribute to their overall well-being and sense of purpose. (2) Existential well-being (10 items): This subscale focuses on the individual’s sense of meaning, purpose, and connection to something greater than themselves, regardless of religious affiliation. Each subscale receives a score from 10 to 60. The spiritual health score is the sum of these two subscales and ranges from 20 to 120. In Iran, during the research conducted by Parvizi et al. (2000), the reliability of this questionnaire using Cronbach’s alpha coefficient was 0.82 [ 20 ]. In Hamdami et al.‘s research (2015), Cronbach’s alpha coefficient of the total spiritual health score was 0.79 [ 21 ].

Care burden questionnaire (Novak and Guest, 1989)

The Care Burden Questionnaire, developed by Novak and Guest in 1989, is a widely used instrument for assessing the burden experienced by caregivers who provide care to individuals with chronic illnesses or disabilities. Caregivers are asked to respond to a series of statements concerning caregiving burden on a Likert scale, with response options typically ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The maximum score that can be attained on this questionnaire is 96, while the minimum score is 0. The questionnaire includes five sub-scales designed to capture a specific aspect of the burden. These include time demands, emotional stress, social isolation, financial strain, and conflicts with other responsibilities. In Iran, in the study of Abbasi et al. (2013), the Cronbach’s alpha coefficient of this questionnaire was 0.90, and its subscales ranged from 0.72 to 0.82 [ 22 ].

Baseline test

Before the intervention, research sessions were initially scheduled to occur in person; however, the coronavirus pandemic rendered it impractical to conduct face-to-face training sessions. As a result, spiritual and religious training was carried out online without impacting b behavioral therapy (CBT) and stress management techniques. It was structured to address the emotional, social, and physical dimensions of caregiver burden while simultaneously fostering coping strategies and self-care practices. The intervention framework was informed by existing research on caregiver interventions, CBT, and stress management. Studies have shown the effectiveness of psychoeducational programs in reducing caregiver burden and enhancing well-being. The incorporation of CBT techniques aimed to help caregivers identify and reframe negative thought patterns, while stress management strategies were included to help caregivers better cope with stressors.

In the test group, the intervention took the form of spiritual care based on the model of Richards and Bergin, which was aligned with Islamic teachings. This model featured six key steps: First, caregivers were guided to pay attention to spiritual-cultural sensitivities. Second, they were trained to establish an open and secure spiritual relationship. Third, potential ethical challenges were addressed. Fourth, caregivers conducted a religious and spiritual assessment of clients. The fifth step involved defining suitable goals for spiritual therapy, and the final step focused on properly implementing spiritual interventions [ 23 ]. The educational sessions covered various dimensions of the care burden, including physical, mental, social, and financial elements, as well as facets of spiritual health, which included the religious dimension (about communication with a transcendent higher power) and the existential dimension (encompassing communication with oneself, creation, and all living beings). These educational sessions were delivered via pre-recorded video presentations developed by a specialist in geriatric nursing and religious education. Participants engaged in four virtual sessions offline, conducted through WhatsApp social messenger, with two sessions held per week. Each session involved the following activities: (1) Following up on the previous session’s topics; (2) providing feedback to participants; (3) summarizing and outlining previous topics to create a connection between the topics discussed; and (4) offering explanations and summaries related to the new session’s topic. One month after the end of the intervention, test and control group participants completed the Mental Health Questionnaire and the Carer’s Burden Questionnaire again. The control group continued with their daily lives as usual throughout the study. Upon its conclusion, the educational materials on spirituality and its various concepts, which had been shared via WhatsApp Messenger, were made available in alignment with the ethical principles that govern such research. The educational content for the sessions was developed by a multidisciplinary team consisting of a nurse psychiatrist, a gerontologist, and a Specialist in Quran and Hadith. The educational content was designed and compiled by the research team to improve practical skills, stress management, self-care, and communication, based on the model of Richards and Bergin and according to the teachings of Islam and the Shia religion. To ensure the accuracy and reliability of the content, the educational materials underwent a rigorous review process involving experts from diverse fields, including caregiving, psychology, and Quranic and Hadith sciences. Feedback from caregivers and pilot testing were also used to refine and validate the content before implementation. A pilot study was conducted to test the intervention’s feasibility, acceptability, and initial effectiveness. The pilot study involved a small group of caregivers who received the intervention, and their feedback was used to refine the program before full implementation. All contributors implementing the intervention received comprehensive training on the educational content and intervention protocols. These trainings were followed daily by viewing the participants’ WhatsApp to receive educational content and listening to audio files, making daily phone calls, and asking them questions over the phone to understand the content and express their questions. The intervention was implemented by a team of trained healthcare professionals, including a social gerontologist, a nursing gerontologist, and a medical-surgical nursing student with a master’s degree. They all had relevant qualifications and expertise in mental health and caregiving support. Potential challenges for implementers could include caregiver resistance, emotional distress, not receiving training materials on time, or difficulty engaging participants. The plan for dealing with such situations included regular monitoring of caregiver progress, open communication, and flexibility in the delivery of sessions. For participants who required more specialized training or support beyond the scope of the intervention, referrals were made through telephone communication with the training session facilitators. Response data from the instruments, such as the Care Burden Questionnaire and other assessment measures, were collected through self-report questionnaires and standardized rating scales administered by trained assessors. Caregivers were asked to respond based on their experiences before and after the intervention. To handle ambiguities in the response data, assessors were trained to clarify any uncertainties or ambiguities in the questions with caregivers. This involved providing clear explanations, and examples, and ensuring that caregivers understood the questions before responding. A specific post-intervention assessment time point was established to standardize the time after the intervention for all participants. This time point was determined based on the intervention duration and the optimal timeframe for assessing the intervention’s impact on caregiver burden based on past studies [ 24 , 25 ]. Caregivers were scheduled for the post-intervention assessment at this standardized time point to ensure consistency across all participants.

Ethical considerations

This study originated from a master’s thesis in internal surgical nursing at the Faculty of Midwifery Nursing, Bushehr University of Medical Sciences, with an ethics code number of IR.BPUMS.REC.1399.042. It is also registered with the Clinical Trial Centre of Iran under IRCT20150529022466N16. The caregivers were furnished with comprehensive information about the study, encompassing its objective, methodology, potential hazards and advantages, confidentiality protocols, and their entitlement to withdraw from the study at any point. Informed consent was obtained from all participants before they participated in the study. Measures were taken to ensure the confidentiality of participants’ personal information and data collected during the study. Participants were assured that their responses would be anonymized, stored securely, and only accessed by authorized research staff.

Data analysis

Due to the peak of the Corona pandemic and the closing of universities in person, the possibility of consulting statistics professors and performing data analysis was delayed for eight months. The data collected during the study were analyzed using SPSS version 19 software. The Shapiro-Wilk test was used to check the distribution of the data. An independent t-test, or Mann-Whitney test, was used to compare quantitative demographic variables between two groups. A Chi-squared or Fisher’s exact test was used to compare qualitative demographic variables between groups. To test the hypotheses above, a paired t-test was employed to ascertain the mean of the primary variables in question, before and after the intervention in each group. An independent t-test was utilized to determine the mean of the variables between groups, and Cohen’s d was calculated as the effect size. Independent t-tests were conducted to compare the mean scores of the changes. The significance level was assumed to be less than 0.05 in all cases.

No statistically significant differences were detected between the groups in terms of demographic variables, suggesting group homogeneity ( p  > 0.05) (Tables  1 and 2 ). Regarding spiritual health, within the intervention group, the post-test average score for total spiritual health was significantly higher than the pre-test score ( p  = 0.007), in contrast within the control group, the post-test average score was considerably lower than the pre-test score ( p  = 0.003). No statistically significant differences were observed between the two groups in terms of mean posttest spiritual health scores (Table  3 ) still, changes in overall spiritual health increased in the intervention group and decreased in the control group, with statistically significant differences between the two groups ( p  = 0.002) (Table  4 ). The Cohen’s d effect size for the difference in spiritual health between the intervention and control groups was 0.415, indicating a moderate effect of the intervention (Table  3 ). Within-group analysis showed no statistically significant differences between pre-and post-test scores for total care burden in either group. Furthermore, no statistically significant differences between the two groups were observed in terms of average care burden scores ( p  < 0.05) (Table  5 ). Likewise, the average changes in care burden scores between the intervention and control groups showed no statistically significant differences ( p  < 0.05) (Table  6 ). The Cohen’s d effect size for the difference in caregiving burden between the intervention and control groups was 0.366, indicating a moderate effect of the intervention on caregiving burden, although not statistically significant (Table  5 ).

This study aimed to evaluate the impact of religious spiritual care training on the spiritual health and care burden experienced by elderly family caregivers in Bushehr during the COVID-19 pandemic. The findings of this study suggest that a religious and spiritual intervention approach can effectively promote existential health and overall spiritual well-being. However, it was observed that this approach did not yield a notable impact on religious health or care burden. The Scores for existential health and overall spiritual health increased in the intervention group after the training, while they decreased in the control group. The mean change in religious health scores between the two groups did not reach statistical significance. These findings are consistent with the study conducted by Sayyadi et al. (2018) [ 26 ], who also observed an increase in spiritual health following a religious psychotherapy intervention. In this study, most family caregivers in the experimental and control groups initially demonstrated moderate to high levels of spiritual health. Similarly, Sayyadi et al. (2018) found higher spiritual health scores in medical and nursing students compared to other student populations. To explain and interpret the consistent findings regarding the positive effects of spiritual health on caregivers in the study by Sayyadi et al. and the current study on the impact of religious spiritual care training on elderly family caregivers, we can consider several factors that may contribute to these findings: (1) Spiritual health is often associated with providing a sense of support, purpose and coping mechanisms during challenging times. Caregivers facing the stress and demands of caregiving may benefit from a solid spiritual foundation to help them navigate their roles and find meaning in their experiences. Studies may have highlighted the role of spiritual health as a resource for caregivers to cope with the emotional and psychological challenges they face. (2) Spiritual health can help to build resilience and foster hope in individuals, including caregivers. By nurturing their spiritual well-being, caregivers may develop a sense of resilience that enables them to cope with adversity and maintain a positive outlook. Studies may have observed the positive impact of spiritual health on caregivers’ resilience and hope, leading to improved well-being and outcomes. (3) Spiritual health is often linked to personal growth and making sense of one’s experiences [ 27 ]. Caregivers possessing a robust spiritual foundation may engage in meaning-making processes, facilitating the discovery of purpose and significance within their caregiving journey. Studies may have underscored the role of spiritual health in promoting personal growth and facilitating meaning-making among caregivers. These factors, alongside the consistent focus on spiritual health across studies, provide a robust framework for understanding the positive impact of spiritual health on caregivers. Recognizing the importance of spiritual well-being within the broader context of caregiver health, and integrating interventions that specifically address spiritual needs, can contribute to improved outcomes and well-being for caregivers. This is supported by the findings of both Sayyadi et al. and the present study. It is important to note that the religious health scores did not increase after the intervention in the current study. The intervention, centered on religious and spiritual care training, had a significant impact on both existential well-being and overall spiritual health. The caregiver survey of palliative care patients will likely target different aspects of spiritual well-being, such as hope and general well-being. In interpreting these results, it is essential to consider the unique components of each intervention and how they may have influenced different aspects of spiritual health. On the other hand, Casalerio et al. (2024) in the study: Promoting Spiritual Coping of Family Caregivers of an Adult Relative with Severe Mental Illness: Development and Test of a Nursing Intervention, reported that the spiritual and religious intervention for caregivers increased their spiritual health dimension and their religious dimension [ 28 ]. These contrasting religious findings with the current study suggest that the effectiveness of religious and spiritual interventions may vary depending on the specific focus and approach of the intervention. Caregivers’ responses to such interventions may be influenced by factors such as the nature of the caring role, the context of the carer’s condition, and individual preferences regarding spirituality and religiosity. Further research and tailored interventions may be needed to address the diverse spiritual needs of caregivers in different care contexts.

Regarding the care burden, the results of the current study demonstrated no statistically significant differences in the average care burden scores within and between the groups. This result contrasts with previous studies by Polat et al. (2024), Xavier et al. (2023) [ 13 , 29 ], Partovirad et al. (2024) [ 11 ], Hekmatpour and colleagues (2018) [ 30 ], Shoghi et al. (2018) [ 31 ], which showed reductions in the care burden following intervention models and the current study care burden result align with previous studies by Khalili et al. (2024) [ 32 ], Salmoirago-Blotcher et al. (2016) [ 33 ], and Karadag Arli (2017) [ 34 ]. One of the reasons why the present study did not show the same effect of spiritual and religious interventions in reducing caregiver burden as similar studies have shown is probably due to the high caregiver burden in the relevant situation. In the present study, caregiver burden had increased due to the conditions of the Corona pandemic, and reducing caregiver burden may require more extended, and more social interventions. One potential explanation for the lack of reduction in care burden scores in the current study is the influence of social interaction theory and attachment theory. These theoretical frameworks emphasize the significance of the dynamic interplay between caregiver and care recipient, particularly highlighting the role of mutual appreciation and non-violent communication in mitigating caregiver burden [ 35 ]. The physical and mental conditions of care recipients, coupled with their inability to engage in appropriate interactions with caregivers during the COVID-19 crisis, may have intensified the care burden. Furthermore, a review of similar studies reveals that most interventions aimed at reducing care burden were conducted over longer periods than our study. These studies typically involved a higher number of sessions, ranging from 8 to 12 (e.g., Mohammadi and Babaei (2018) [ 36 ], Rahgooy et al. (2018) [ 37 ], Sotoudeh et al. (2018) [ 38 ] and Salehinejad et al. (2017) [ 39 ] Consequently, the shorter duration and fewer sessions in our study may have limited the effectiveness of the intervention in reducing the care burden. Additionally, the limitations imposed by social distancing measures may have exacerbated the needs of elderly individuals, leading to an increased caregiver burden. Furthermore, a review of similar studies reveals that most interventions aimed at reducing care burden were conducted over more extended periods than our study. These studies typically involved a higher number of sessions, ranging from 8 to 12 (e.g., Mohammadi and Babaei (2018) [ 30 ], Rahgooy et al. (2018) [ 32 ], Sotoudeh et al. (2018) [ 39 ] and Salehinejad et al. Consequently, the shorter duration and fewer sessions in our study may have limited the effectiveness of the intervention in reducing the care burden.

Limitations

This study had limitations. The limitations imposed by the pandemic, including the need for social distancing, made it impossible to conduct face-to-face training sessions and deprived participants and carers of the opportunity for close, face-to-face communication during the spiritual and religious intervention. This limitation may have affected the participants’ internal beliefs, emotions, and motivations. The restrictions imposed by the pandemic, through the utilization of routine telephone communications and collaboration with pertinent academic staff, exemplify adaptability and ingenuity in maintaining communication with participants. This multi-channel approach may have helped to ensure continued engagement and support for participants throughout the intervention. Despite the challenges posed by the lack of face-to-face communication, the study managed to keep participants engaged through alternative means. The regular phone calls and coordination with the professors may have fostered a sense of connection and support, potentially enhancing participants’ overall experience and engagement with the intervention. The lack of face-to-face interaction during the spiritual and religious intervention may have limited the depth of participants’ engagement and the impact on their internal beliefs and motivations. This limitation could affect the validity of the study findings, as face-to-face communication is often crucial for building trust and rapport in interventions of this nature. The short duration of the intervention and the constraints imposed by the pandemic may have limited the generalizability of the study results. Further research utilizing more extended intervention periods and more diverse participant groups may enhance the generalizability of the findings to a broader population. Utilizing virtual platforms for interactive sessions and group discussions could facilitate the replication of the advantages of face-to-face communication and cultivate a sense of community among participants. Conducting long-term follow-up studies to track the sustained effects of spiritual and religious interventions on caregiver burden could provide valuable insights into the lasting impact and effectiveness of the intervention over time.

Based on the study, the results were mixed. The religious and spiritual intervention was effective in improving existential health and overall spiritual health but did not have a significant impact on religious health and caregiving burden. The training in religious and spiritual care was determined to be effective in enhancing the existential well-being of elderly family caregivers, as evidenced by an increase in their sense of meaning, purpose, and fulfillment in the caregiving role. The intervention demonstrated effectiveness in improving caregivers’ overall spiritual health, suggesting positive outcomes in terms of emotional well-being, connectedness, and resilience. Notwithstanding the favorable outcomes in existential and general spiritual well-being, the intervention did not demonstrate a notable impact on religious well-being and caregiver burden, underscoring domains that may warrant further investigation and the development of alternative intervention strategies. It is crucial to recognize the intricate nature of caregiving dynamics and the various ways in which spirituality and religion can impact the well-being of caregivers. The result of the study indicates that integrating religious and spiritual care training could effectively enhance the existential and holistic spiritual well-being of elderly family caregivers. Practitioners and caregivers can utilize this intervention to foster a greater sense of meaning and spiritual well-being within the caregiving context. In addition, the study highlights the importance of personalized interventions that consider individual differences in spiritual beliefs and coping strategies. In conclusion, while the religious and spiritual intervention showed promising results in improving certain aspects of the spiritual health of elderly family caregivers in Bushehr, further research is needed to address the nuances of religious health and care burden. By carefully considering these key findings and implications, practitioners and researchers can tailor interventions to better support caregivers’ holistic well-being in the face of challenges such as the COVID-19 pandemic.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to express our gratitude to the Student Research Committee, the Persian Gulf Martyrs Hospital’s Clinical Research Development Center, and all the elderly caregivers who participated in this research, as their contributions were invaluable.

Research expenses by the vice president of research and the student research committee of Bushehr University of Medical Sciences, Iran, have been paid.

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Qorbani, A., Pouladi, S., Farhadi, A. et al. The impact of religious spiritual care training on the spiritual health and care burden of elderly family caregivers during the COVID-19 pandemic: a field trial study. BMC Nurs 23 , 584 (2024). https://doi.org/10.1186/s12912-024-02268-2

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• Published: 26 August 2024

Neural populations in the language network differ in the size of their temporal receptive windows

• Tamar I. Regev   ORCID: orcid.org/0000-0003-0639-0890 1 , 2   na1 ,
• Colton Casto   ORCID: orcid.org/0000-0001-6966-1470 1 , 2 , 3 , 4   na1 ,
• Eghbal A. Hosseini 1 , 2 ,
• Markus Adamek   ORCID: orcid.org/0000-0001-8519-9212 5 , 6 ,
• Anthony L. Ritaccio 7 ,
• Jon T. Willie   ORCID: orcid.org/0000-0001-9565-4338 5 , 6 ,
• Peter Brunner   ORCID: orcid.org/0000-0002-2588-2754 5 , 6 , 8 &
• Evelina Fedorenko   ORCID: orcid.org/0000-0003-3823-514X 1 , 2 , 3  

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Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Here we leveraged the high spatiotemporal resolution of human intracranial recordings ( n  = 22) to examine responses to sentences and linguistically degraded conditions. We discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows, with average windows of about 1, 4 and 6 words, respectively. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input—a property that may be critical for the efficiency and robustness of language processing.

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Code availability

Code used to conduct analyses and generate figures from the preprocessed data is available publicly on GitHub at https://github.com/coltoncasto/ecog_clustering_PUBLIC (ref. 93 ). The VERA software suite used to perform electrode localization can also be found on GitHub at https://github.com/neurotechcenter/VERA (ref. 82 ).

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Acknowledgements

We thank the participants for agreeing to take part in our study, as well as N. Kanwisher, former and current EvLab members, especially C. Shain and A. Ivanova, and the audience at the Neurobiology of Language conference (2022, Philadelphia) for helpful discussions and comments on the analyses and manuscript. T.I.R. was supported by the Zuckerman-CHE STEM Leadership Program and by the Poitras Center for Psychiatric Disorders Research. C.C. was supported by the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. A.L.R. was supported by NIH award U01-NS108916. J.T.W. was supported by NIH awards R01-MH120194 and P41-EB018783, and the American Epilepsy Society Research and Training Fellowship for clinicians. P.B. was supported by NIH awards R01-EB026439, U24-NS109103, U01-NS108916, U01-NS128612 and P41-EB018783, the McDonnell Center for Systems Neuroscience, and Fondazione Neurone. E.F. was supported by NIH awards R01-DC016607, R01-DC016950 and U01-NS121471, and research funds from the McGovern Institute for Brain Research, Brain and Cognitive Sciences Department, and the Simons Center for the Social Brain. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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These authors contributed equally: Tamar I. Regev, Colton Casto.

Authors and Affiliations

Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA

Tamar I. Regev, Colton Casto, Eghbal A. Hosseini & Evelina Fedorenko

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Program in Speech and Hearing Bioscience and Technology (SHBT), Harvard University, Boston, MA, USA

Colton Casto & Evelina Fedorenko

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA, USA

Colton Casto

National Center for Adaptive Neurotechnologies, Albany, NY, USA

Markus Adamek, Jon T. Willie & Peter Brunner

Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, USA

Department of Neurology, Mayo Clinic, Jacksonville, FL, USA

Anthony L. Ritaccio

Department of Neurology, Albany Medical College, Albany, NY, USA

Peter Brunner

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Contributions

T.I.R. and C.C. equally contributed to study conception and design, data analysis and interpretation of results, and manuscript writing. E.A.H. contributed to data analysis and manuscript editing; M.A. to data collection and analysis; A.L.R., J.T.W. and P.B. to data collection and manuscript editing. E.F. contributed to study conception and design, supervision, interpretation of results and manuscript writing.

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Correspondence to Tamar I. Regev , Colton Casto or Evelina Fedorenko .

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Extended data

Extended data fig. 1 dataset 1 k-medoids (k = 3) cluster assignments by participant..

Average cluster responses as in Fig. 2e grouped by participant. Shaded areas around the signal reflect a 99% confidence interval over electrodes. The number of electrodes constructing the average (n) is denoted above each signal in parenthesis. Prototypical responses for each of the three clusters were found in nearly all participants individually. However, for participants with only a few electrodes assigned to a given cluster (for example, P5 Cluster 3), the responses were more variable.

Extended Data Fig. 2 Dataset 1 k-medoids clustering with k = 10.

a) Clustering mean electrode responses (S + W + J + N) using k-medoids with k = 10 and a correlation-based distance. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). b) Electrode responses visualized on their first two principal components, colored by cluster. c) Timecourses of best representative electrodes (‘medoids’) selected by the algorithm from each of the ten clusters. d) Timecourses averaged across all electrodes in each cluster. Shaded areas around the signal reflect a 99% confidence interval over electrodes. Correlation with the k = 3 cluster averages are shown to the right of the timecourses. Many clusters exhibited high correlations with the k = 3 response profiles from Fig. 2 .

Extended Data Fig. 3 All Dataset 1 responses.

a-c) All Dataset 1 electrode responses. The timecourses (concatenated across the four conditions, ordered: sentences, word lists, Jabberwocky sentences, non-word lists) of all electrodes in Dataset 1 sorted by their correlation to the cluster medoid (medoid shown at the bottom of each cluster). Colors reflect the reliability of the measured neural signal, computed by correlating responses to odd and even trials (Fig. 1d ). The estimated temporal receptive window (TRW) using the toy model from Fig. 4 is displayed to the left, and the participant who contributed the electrode is displayed to the right. There was strong consistency in the responses from individual electrodes within a cluster (with more variability in the less reliable electrodes), and electrodes with responses that were more similar to the cluster medoid tended to be more reliable (more pink). Note that there were two reliable response profiles (relatively pink) that showed a pattern that was distinct from the three prototypical response profiles: One electrode in Cluster 2 (the 10th electrode from the top in panel B) responded only to the onset of the first word/nonword in each trial; and one electrode in Cluster 3 (the 4th electrode from the top in panel C) was highly locked to all onsets except the first word/nonword. These profiles indicate that although the prototypical clusters explain a substantial amount of the functional heterogeneity of responses in the language network, they were not the only observed responses.

Extended Data Fig. 4 Partial correlations of individual response profiles with each of the cluster medoids.

a) Pearson correlations of all response profiles with each of the cluster medoids, grouped by cluster assignment. b) Partial correlations ( Methods ) of all response profiles with each of the cluster medoids, controlling for the other two cluster medoids, grouped by cluster assignment. c) Response profiles from electrodes assigned to Cluster 1 that had a high partial correlation ( > 0.2, arbitrarily defined threshold) with the Cluster 2 medoid (and split-half reliability>0.3). Top: Average over all electrodes that met these criteria (n = 18, black). The Cluster 1 medoid is shown in red, and the Cluster 2 medoid is shown in green. Bottom: Four sample electrodes (black). d) Response profiles assigned to Cluster 2 that had a high partial correlation ( > 0.2, arbitrarily defined threshold) with the Cluster 1 medoid (and split-half reliability>0.3). Top: Average over all electrodes that meet these criteria (n = 12, black). The Cluster 1 medoid is shown in red, and the Cluster 2 medoid is shown in green. Bottom: Four sample electrodes (black; see osf.io/xfbr8/ for all mixed response profiles with split-half reliability>0.3). e) Anatomical distribution of electrodes in Dataset 1 colored by their partial correlation with a given cluster medoid (controlling for the other two medoids). Cluster-1- and Cluster-2-like responses were present throughout frontal and temporal areas (with Cluster 1 responses having a slightly higher concentration in the temporal pole and Cluster 2 responses having a slightly higher concentration in the superior temporal gyrus (STG)), whereas Cluster-3-like responses were localized to the posterior STG.

Extended Data Fig. 5 N-gram frequencies of sentences and word lists diverge with n-gram length.

N-gram frequencies were extracted from the Google n-gram online platform ( https://books.google.com/ngrams/ ), averaging across Google books corpora between the years 2010 and 2020. For each individual word, the n-gram frequency for n = 1 was the frequency of that word in the corpus; for n = 2 it was the frequency of the bigram (sequence of 2 words) ending in that word; for n = 3 it was the frequency of the trigram (sequence of 3 words) ending in that word; and so on. Sequences that were not found in the corpus were assigned a value of 0. Results are only presented until n = 4 because for n > 4 most of the string sequences, both from the Sentence and Word-list conditions, were not found in the corpora. The plot shows that the difference between the log n-gram values for the sentences and word lists in our stimulus set grows as a function of N. Error bars represent the standard error of the mean across all n-grams extracted from the stimuli used (640, 560, 480, 399 n-grams for n-gram length = 1, 2, 3, and 4, respectively).

Extended Data Fig. 6 Temporal receptive window (TRW) estimates with kernels of different shapes.

The toy TRW model from Fig. 4 was applied using five different kernel shapes: cosine ( a ), ‘wide’ Gaussian (Gaussian curves with a standard deviation of σ /2 that were truncated at +/− 1 standard deviation, as used in Fig. 4 ; b ), ‘narrow’ Gaussian (Gaussian curves with a standard deviation of σ /16 that were truncated at +/− 8 standard deviations; c ), a square (that is, boxcar) function (1 for the entire window; d ) and a linear asymmetric function (linear function with a value of 0 initially and a value of 1 at the end of the window; e ). For each kernel ( a-e ), the plots represent (left to right, all details are identical to Fig. 4 in the manuscript): 1) The kernel shapes for TRW = 1, 2, 3, 4, 6 and 8 words, superimposed on the simplified stimulus train; 2) The simulated neural signals for each of those TRWs; 3) violin plots of best fitted TRW values across electrodes (each dot represents an electrode, horizontal black lines are means across the electrodes, white dots are medians, vertical thin box represents lower and upper quartiles and ‘x’ marks indicate outliers; more than 1.5 interquartile ranges above the upper quartile or less than 1.5 interquartile ranges below the lower quartile) for all electrodes (black), or electrodes from only Clusters 1 (red) 2 (green) or 3 (blue); and 4) Estimated TRW as a function of goodness of fit. Each dot is an electrode, its size represents the reliability of its neural response, computed via correlation between the mean signals when using only odd vs. only even trials, x-axis is the electrode’s best fitted TRW, y-axis is the goodness of fit, computed via correlation between the neural signal and the closest simulated signal. For all kernels the TRWs showed a decreasing trend from Cluster 1 to 3.

Extended Data Fig. 7 Dataset 1 k-medoids clustering results with only S and N conditions.

a) Search for optimal k using the ‘elbow method’. Top: variance (sum of the distances of all electrodes to their assigned cluster centre) normalized by the variance when k = 1 as a function of k (normalized variance (NV)). Bottom: change in NV as a function of k (NV(k + 1) – NV(k)). After k = 3 the change in variance became more moderate, suggesting that 3 clusters appropriately described Dataset 1 when using only the responses to sentences and non-words (as was the case when all four conditions were used). b) Clustering mean electrode responses (only S and N, importantly) using k-medoids (k = 3) with a correlation-based distance. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). c) Average timecourse by cluster. Shaded areas around the signal reflect a 99% confidence interval over electrodes (n = 99, n = 61, and n = 17 electrodes for Cluster 1, 2, and 3, respectively). Clusters 1-3 showed a strong similarity to the clusters reported in Fig. 2 . d) Mean condition responses by cluster. Error bars reflect standard error of the mean over electrodes. e) Electrode responses visualized on their first two principal components, colored by cluster. f) Anatomical distribution of clusters across all participants (n = 6). g) Robustness of clusters to electrode omission (random subsets of electrodes were removed in increments of 5). Stars reflect significant similarity with the full dataset (with a p threshold of 0.05; evaluated with a one-sided permutation test, n = 1000 permutations; Methods ). Shaded regions reflect standard error of the mean over randomly sampled subsets of electrodes. Relative to when all conditions were used, Cluster 2 was less robust to electrode omission (although still more robust than Cluster 3), suggesting that responses to word lists and Jabberwocky sentences (both not present here) are particularly important for distinguishing Cluster 2 electrodes from Cluster 1 and 3 electrodes.

Extended Data Fig. 8 Dataset 2 electrode assignment to most correlated Dataset 1 cluster under ‘winner-take-all’ (WTA) approach.

a) Assigning electrodes from Dataset 2 to the most correlated cluster from Dataset 1. Assignment was performed using the correlation with the Dataset 1 cluster average, not the cluster medoid. Shading of the data matrix reflects normalized high-gamma power (70–150 Hz). b) Average timecourse by group. Shaded areas around the signal reflect a 99% confidence interval over electrodes (n = 142, n = 95, and n = 125 electrodes for groups 1, 2, and 3, respectively). c) Mean condition responses by group. Error bars reflect standard error of the mean over electrodes (n = 142, n = 95, and n = 125 electrodes for groups 1, 2, and 3, respectively, as in b ). d) Electrode responses visualized on their first two principal components, colored by group. e) Anatomical distribution of groups across all participants (n = 16). f-g) Comparison of cluster assignment of electrodes from Dataset 2 using clustering vs. winner-take-all (WTA) approach. f) The numbers in the matrix correspond to the number of electrodes assigned to cluster y during clustering (y-axis) versus the number electrodes assigned to group x during the WTA approach (x-axis). For instance, there were 44 electrodes that were assigned to Cluster 1 during clustering but were ‘pulled out’ to Group 2 (the analog of Cluster 2) during the WTA approach. The total number of electrodes assigned to each cluster during the clustering approach are shown to the right of each row. The total number of electrodes assigned to each group during the WTA approach are shown at the top of each column. N = 362 is the total number of electrodes in Dataset 2. g) Similar to F , but here the average timecourse across all electrodes assigned to the corresponding cluster/group during both procedures is presented. Shaded areas around the signals reflect a 99% confidence interval over electrodes.

Extended Data Fig. 9 Anatomical distribution of the clusters in Dataset 2.

a) Anatomical distribution of language-responsive electrodes in Dataset 2 across all subjects in MNI space, colored by cluster. Only Clusters 1 and 3 (those from Dataset 1 that replicate to Dataset 2) are shown. b) Anatomical distribution of language-responsive electrodes in subject-specific space for eight sample participants. c-h) Violin plots of MNI coordinate values for Clusters 1 and 3 in the left and right hemisphere ( c-e and f-h , respectively), where plotted points (n = 16 participants) represent the mean of all coordinate values for a given participant and cluster. The mean across participants is plotted with a black horizontal line, and the median is shown with a white circle. Vertical thin black boxes within violins plots represent the upper and lower quartiles. Significance is evaluated with a LME model ( Methods , Supplementary Tables 3 and 4 ). The Cluster 3 posterior bias from Dataset 1 was weakly present but not statistically reliable.

Extended Data Fig. 10 Estimation of temporal receptive window (TRW) sizes for electrodes in Dataset 2.

As in Fig. 4 but for electrodes in Dataset 2. a) Best TRW fit (using the toy model from Fig. 4 ) for all electrodes, colored by cluster (when k-medoids clustering with k = 3 was applied, Fig. 6 ) and sized by the reliability of the neural signal as estimated by correlating responses to odd and even trials (Fig. 6c ). The ‘goodness of fit’, or correlation between the simulated and observed neural signal (Sentence condition only), is shown on the y-axis. b) Estimated TRW sizes across all electrodes (grey) and per cluster (red, green, and blue). Black vertical lines correspond to the mean window size and the white dots correspond to the median. ‘x’ marks indicate outliers (more than 1.5 interquartile ranges above the upper quartile or less than 1.5 interquartile ranges below the lower quartile). Significance values were calculated using a linear mixed-effects model (comparing estimate values, two-sided ANOVA for LME, Methods , see Supplementary Table 8 for exact p-values). c-d) Same as A and B , respectively, except that clusters were assigned by highest correlation with Dataset 1 clusters (Extended Data Fig. 8 ). Under this procedure, Cluster 2 reliably separated from Cluster 3 in terms of its TRW (all ps<0.001, evaluated with a LME model, Methods , see Supplementary Table 9 for exact p-values).

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Regev, T.I., Casto, C., Hosseini, E.A. et al. Neural populations in the language network differ in the size of their temporal receptive windows. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01944-2

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DOI : https://doi.org/10.1038/s41562-024-01944-2

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