difference between random sample and random assignment

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AP®︎/College Statistics

Course: ap®︎/college statistics   >   unit 6.

• Statistical significance of experiment

Random sampling vs. random assignment (scope of inference)

• Conclusions in observational studies versus experiments
• Finding errors in study conclusions

• (Choice A)   Just the residents involved in Hilary's study. A Just the residents involved in Hilary's study.
• (Choice B)   All residents in Hilary's town. B All residents in Hilary's town.
• (Choice C)   All residents in Hilary's country. C All residents in Hilary's country.
• (Choice A)   Yes A Yes
• (Choice B)   No B No
• (Choice A)   Just the residents in Hilary's study. A Just the residents in Hilary's study.

	Random sampling	Not random sampling
	Can determine causal relationship in population.	Can determine causal relationship in that sample only.
	Can detect relationships in population, but cannot determine causality.	Can detect relationships in that sample only, but cannot determine causality.

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Random Sampling vs Random Assignment

Random sampling and Random assignment are two important distinctions, and understanding the difference between the two is important to get accurate and dependable results.

Random sampling is a proper procedure for selecting a subset of bodies from a larger set of bodies, each of which has the same likelihood of being selected. In contrast, Random allocation of participants involves assigning participants to different groups or conditions of the experiment, and this minimizes pre-existing confounding factors.

Table of Content

What is Random Sampling?

What is random assignment, differences between random sampling and random assignment, examples of random sampling and random assignment, applications of random sampling and random assignment, advantages of random sampling and random assignment, disadvantages of random sampling and random assignment, importance of random sampling and random assignment.

Random sampling is a technique in which a smaller number of individuals are picked up from a large number of people within the population in an impartial manner so that no one person within the population has a greater possibility of being selected than any other person.

This technique makes it possible not to have a selection bias, and, therefore, the sample is so constituted that the results can be generalized to the entire population.

Different techniques of random sampling include – Simple random sampling, stratified sampling, and systematic sampling, all of which have different approaches towards achieving the principle of sampling referred to as representativeness.

Random assignment is the process of distributing participants in experimental research in different groups or under different conditions.

This process also guarantees that no participant tends to be placed in a particular group, thus reducing the possibility of selection bias within a given study. In doing so, random assignment enhances the chances of the two groups’ equality at the different stages of an experiment, so the researcher can effectively link results to the treatment or intervention under consideration without worrying about other factors.

This increases the internal reliability of the study and assists in establishing a cause-and-effect relationship.

Differences between Random Sampling and Random Assignment can be learnt using the table added below:

Aspect	Random Sampling	Random Assignment
Purpose	To obtain a representative sample of a larger population.	To evenly distribute participants across different experimental conditions.
Application	Used in surveys and observational studies to ensure sample representativeness.	Used in experiments to control for variables and ensure groups are comparable.
Process	Randomly selects individuals from the population.	Randomly assigns individuals to different groups or conditions.
Outcome	Provides a sample that mirrors the population’s characteristics.	Ensures that differences observed between groups are due to the treatment or intervention.
Focus	Accuracy of the sample in reflecting the population.	Validity of the experiment by controlling for confounding variables.

Various examples of Random Sampling and Random Assignment

Random Sampling	Random Assignment
Surveying 1,000 randomly selected voters to gauge public opinion.	Randomly assigning participants to a treatment or control group in a clinical trial.
Selecting a random sample of students from a school to study academic performance.	Randomly assigning students to either a new teaching method or traditional method group.
Using random sampling to choose households for a national health survey.	Randomly assigning patients to different drug dosage levels in a medical study.
Sampling customers from different regions to assess brand satisfaction.	Randomly assigning participants to different marketing strategies in an advertising experiment.
Drawing a random sample of participants from a population for a psychological study.	Randomly assigning individuals to different therapy types in a behavioral study.

Some applications of Random Sampling and Random Assignment are added in the table below:

Application	Random Sampling	Random Assignment
Public Opinion Polls	Selecting a representative sample of voters to gauge public opinion.	Not applicable; polls use sampling, not assignment.
Clinical Trials	Sampling patients from a larger population for study inclusion.	Randomly assigning participants to treatment or control groups.
Educational Research	Sampling students from different schools to study educational outcomes.	Randomly assigning students to different teaching methods.
Marketing Research	Sampling customers to gather feedback on a product or service.	Randomly assigning customers to different marketing strategies.
Behavioral Studies	Sampling participants from a population to study behavior patterns.	Randomly assigning participants to various experimental conditions.

Some advantages of Random Sampling and Random Assignment are added in the table below:

Advantages	Random Sampling	Random Assignment
Reduces Bias	Minimizes selection bias, ensuring a representative sample.	Balances pre-existing differences between groups, reducing bias.
Generalizability	Ensures findings can be generalized to the larger population.	Enhances internal validity by controlling for confounding variables.
Reliability	Provides a basis for statistical analysis and valid conclusions.	Allows for clear attribution of effects to the treatment or intervention.
Equal Chance	Each member of the population has an equal chance of being selected.	Each participant has an equal chance of being assigned to any group.
Reduces Sampling Error	Helps reduce sampling error by accurately representing the population.	Ensures that any differences observed are due to the experimental conditions.

Some disadvantages of Random Sampling and Random Assignment are added in the table below:

Disadvantages	Random Sampling	Random Assignment
Cost and Time	Can be costly and time-consuming to implement, especially with large populations.	May be logistically challenging and resource-intensive.
Practical Challenges	May face difficulties in achieving a truly random sample due to accessibility issues.	May not always be feasible or ethical, especially in certain contexts.
Representativeness	Small sample sizes may not fully represent the population, affecting accuracy.	Random assignment may not eliminate all sources of bias or variability.
Implementation Issues	Practical difficulties in ensuring true randomness.	Potential for unequal distribution of key variables if sample sizes are small.
Ethical Concerns	May face ethical issues if certain groups are underrepresented.	Ethical dilemmas may arise if one group receives less beneficial treatment.

Importance of Random Sampling and Random Assignment are added in the table below:

Importance	Random Sampling	Random Assignment
Purpose	Ensures the sample represents the population	Ensures participants are evenly distributed across experimental groups.
Bias Reduction	Reduces selection bias in sample selection.	Minimizes pre-existing differences between groups.
Generalizability	Allows findings to be generalized to the population.	Improves the validity of conclusions about the treatment effect.
Validity	Ensures that sample findings reflect the broader population.	Ensures observed effects are due to the intervention, not confounding variables.
Statistical Analysis	Provides a basis for accurate statistical inferences.	Facilitates robust comparison between experimental conditions.

Random sampling and random assignment are two significant techniques in research that act differently yet are equally important in study procedures.

• Random sampling makes sure that a sample is selected from the population in a way that will reflect on the whole population, and this helps in reducing bias.
• Random assignment , on the other hand, is useful in experimental investigations and aims at assigning the participants to the groups equally since it helps in preventing the influence of external variables and keeps only the treatment or intervention factor active.

Combined, these methods increase the credibility of results, allowing the development of more accurate conclusions based on research. By comprehending each class’s roles, research workers keep their studies and conclusions a lot more precise.

Random SamplingMethod Simple Random Sampling Systematic Sampling vs Random Sampling

FAQs on Random Sampling and Random Assignment

What is the difference between random sampling and random assignment.

Random sampling is the one in which subjects are chosen haphazardly from a population so that every member of that population has the same likelihood of being selected. Random assignment is the process of assigning the participants of an experiment to various groups or conditions in a random manner so that any background difference is not a factor.

What is random sampling, and why is it significant to research?

On the other hand, random sampling helps in achieving a representative sample, which helps in making generalizations and cuts down on selection bias.

Why does random assignment help increase the validity of an experiment?

Random assignment equalizes the variability between groups. This way, any variations that are noticed in the study are attributed to the treatment or the intervention.

What are the types of random sampling that are widely used in research studies?

Some of them are simple random sampling, stratified sampling, and systematic sampling, all of which have different ways of obtaining a representative sample.

Can random assignment be used in all types of research?

Although random assignment is optimum for making experiments with the view of finding cause-and-effect relationships, it may not be possible or even immoral in some cases, like in observational research or some healthcare conditions.

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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.

Longitudinal study	Cross-sectional study
observations	Observations at a in time
Observes the multiple times	Observes (a “cross-section”) in the population
Follows in participants over time	Provides of society at a given point

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

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

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

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

• 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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Random Assignment in Psychology: Definition & Examples

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

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

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

In psychology, random assignment refers to the practice of allocating participants to different experimental groups in a study in a completely unbiased way, ensuring each participant has an equal chance of being assigned to any group.

In experimental research, random assignment, or random placement, organizes participants from your sample into different groups using randomization. 

Random assignment uses chance procedures to ensure that each participant has an equal opportunity of being assigned to either a control or experimental group.

The control group does not receive the treatment in question, whereas the experimental group does receive the treatment.

When using random assignment, neither the researcher nor the participant can choose the group to which the participant is assigned. This ensures that any differences between and within the groups are not systematic at the onset of the study. 

In a study to test the success of a weight-loss program, investigators randomly assigned a pool of participants to one of two groups.

Group A participants participated in the weight-loss program for 10 weeks and took a class where they learned about the benefits of healthy eating and exercise.

Group B participants read a 200-page book that explains the benefits of weight loss. The investigator randomly assigned participants to one of the two groups.

The researchers found that those who participated in the program and took the class were more likely to lose weight than those in the other group that received only the book.

Importance 

Random assignment ensures that each group in the experiment is identical before applying the independent variable.

In experiments , researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. Random assignment increases the likelihood that the treatment groups are the same at the onset of a study.

Thus, any changes that result from the independent variable can be assumed to be a result of the treatment of interest. This is particularly important for eliminating sources of bias and strengthening the internal validity of an experiment.

Random assignment is the best method for inferring a causal relationship between a treatment and an outcome.

Random Selection vs. Random Assignment 

Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study.

On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. 

Random selection ensures that everyone in the population has an equal chance of being selected for the study. Once the pool of participants has been chosen, experimenters use random assignment to assign participants into groups. 

Random assignment is only used in between-subjects experimental designs, while random selection can be used in a variety of study designs.

Random Assignment vs Random Sampling

Random sampling refers to selecting participants from a population so that each individual has an equal chance of being chosen. This method enhances the representativeness of the sample.

Random assignment, on the other hand, is used in experimental designs once participants are selected. It involves allocating these participants to different experimental groups or conditions randomly.

This helps ensure that any differences in results across groups are due to manipulating the independent variable, not preexisting differences among participants.

When to Use Random Assignment

Random assignment is used in experiments with a between-groups or independent measures design.

In these research designs, researchers will manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables.

There is usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable at the onset of the study.

How to Use Random Assignment

There are a variety of ways to assign participants into study groups randomly. Here are a handful of popular methods: 

• Random Number Generator : Give each member of the sample a unique number; use a computer program to randomly generate a number from the list for each group.
• Lottery : Give each member of the sample a unique number. Place all numbers in a hat or bucket and draw numbers at random for each group.
• Flipping a Coin : Flip a coin for each participant to decide if they will be in the control group or experimental group (this method can only be used when you have just two groups) 
• Roll a Die : For each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1, 2, or 3 places them in a control group and rolling 3, 4, 5 lands them in an experimental group.

When is Random Assignment not used?

• When it is not ethically permissible: Randomization is only ethical if the researcher has no evidence that one treatment is superior to the other or that one treatment might have harmful side effects. 
• When answering non-causal questions : If the researcher is just interested in predicting the probability of an event, the causal relationship between the variables is not important and observational designs would be more suitable than random assignment. 
• When studying the effect of variables that cannot be manipulated: Some risk factors cannot be manipulated and so it would not make any sense to study them in a randomized trial. For example, we cannot randomly assign participants into categories based on age, gender, or genetic factors.

Drawbacks of Random Assignment

While randomization assures an unbiased assignment of participants to groups, it does not guarantee the equality of these groups. There could still be extraneous variables that differ between groups or group differences that arise from chance. Additionally, there is still an element of luck with random assignments.

Thus, researchers can not produce perfectly equal groups for each specific study. Differences between the treatment group and control group might still exist, and the results of a randomized trial may sometimes be wrong, but this is absolutely okay.

Scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when data is aggregated in a meta-analysis.

Additionally, external validity (i.e., the extent to which the researcher can use the results of the study to generalize to the larger population) is compromised with random assignment.

Random assignment is challenging to implement outside of controlled laboratory conditions and might not represent what would happen in the real world at the population level. 

Random assignment can also be more costly than simple observational studies, where an investigator is just observing events without intervening with the population.

Randomization also can be time-consuming and challenging, especially when participants refuse to receive the assigned treatment or do not adhere to recommendations. 

What is the difference between random sampling and random assignment?

Random sampling refers to randomly selecting a sample of participants from a population. Random assignment refers to randomly assigning participants to treatment groups from the selected sample.

Does random assignment increase internal validity?

Yes, random assignment ensures that there are no systematic differences between the participants in each group, enhancing the study’s internal validity .

Does random assignment reduce sampling error?

Yes, with random assignment, participants have an equal chance of being assigned to either a control group or an experimental group, resulting in a sample that is, in theory, representative of the population.

Random assignment does not completely eliminate sampling error because a sample only approximates the population from which it is drawn. However, random sampling is a way to minimize sampling errors. 

When is random assignment not possible?

Random assignment is not possible when the experimenters cannot control the treatment or independent variable.

For example, if you want to compare how men and women perform on a test, you cannot randomly assign subjects to these groups.

Participants are not randomly assigned to different groups in this study, but instead assigned based on their characteristics.

Does random assignment eliminate confounding variables?

Yes, random assignment eliminates the influence of any confounding variables on the treatment because it distributes them at random among the study groups. Randomization invalidates any relationship between a confounding variable and the treatment.

Why is random assignment of participants to treatment conditions in an experiment used?

Random assignment is used to ensure that all groups are comparable at the start of a study. This allows researchers to conclude that the outcomes of the study can be attributed to the intervention at hand and to rule out alternative explanations for study results.

Further Reading

• Bogomolnaia, A., & Moulin, H. (2001). A new solution to the random assignment problem .  Journal of Economic theory ,  100 (2), 295-328.
• Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do .  Journal of Clinical Psychology ,  59 (7), 751-766.

Random Selection vs. Random Assignment

Random selection and random assignment  are two techniques in statistics that are commonly used, but are commonly confused.

Random selection  refers to the process of randomly selecting individuals from a population to be involved in a study.

Random assignment  refers to the process of randomly  assigning  the individuals in a study to either a treatment group or a control group.

You can think of random selection as the process you use to “get” the individuals in a study and you can think of random assignment as what you “do” with those individuals once they’re selected to be part of the study.

The Importance of Random Selection and Random Assignment

When a study uses  random selection , it selects individuals from a population using some random process. For example, if some population has 1,000 individuals then we might use a computer to randomly select 100 of those individuals from a database. This means that each individual is equally likely to be selected to be part of the study, which increases the chances that we will obtain a representative sample – a sample that has similar characteristics to the overall population.

By using a representative sample in our study, we’re able to generalize the findings of our study to the population. In statistical terms, this is referred to as having  external validity – it’s valid to externalize our findings to the overall population.

When a study uses  random assignment , it randomly assigns individuals to either a treatment group or a control group. For example, if we have 100 individuals in a study then we might use a random number generator to randomly assign 50 individuals to a control group and 50 individuals to a treatment group.

By using random assignment, we increase the chances that the two groups will have roughly similar characteristics, which means that any difference we observe between the two groups can be attributed to the treatment. This means the study has  internal validity  – it’s valid to attribute any differences between the groups to the treatment itself as opposed to differences between the individuals in the groups.

Examples of Random Selection and Random Assignment

It’s possible for a study to use both random selection and random assignment, or just one of these techniques, or neither technique. A strong study is one that uses both techniques.

The following examples show how a study could use both, one, or neither of these techniques, along with the effects of doing so.

Example 1: Using both Random Selection and Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. Once they have the 100 individuals, they once again use a computer to randomly assign 50 of the individuals to a control group (e.g. stick with their standard diet) and 50 individuals to a treatment group (e.g. follow the new diet). They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample and random assignment when putting individuals in either a treatment or control group. By doing so, they’re able to generalize the findings from the study to the overall population  and  they’re able to attribute any differences in average weight loss between the two groups to the new diet.

Example 2: Using only Random Selection

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 individuals to be in the study by using a computer to randomly select 100 names from a database. However, they decide to assign individuals to groups based solely on gender. Females are assigned to the control group and males are assigned to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers used random selection to obtain their sample, but they did not use random assignment when putting individuals in either a treatment or control group. Instead, they used a specific factor – gender – to decide which group to assign individuals to. By doing this, they’re able to generalize the findings from the study to the overall population but they are  not  able to attribute any differences in average weight loss between the two groups to the new diet. The internal validity of the study has been compromised because the difference in weight loss could actually just be due to gender, rather than the new diet.

Example 3: Using only Random Assignment

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 100 males athletes to be in the study. Then, they use a computer program to randomly assign 50 of the male athletes to a control group and 50 to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 male athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. However, they did use random assignment, which means they can attribute any difference in weight loss to the new diet.

Example 4: Using Neither Technique

Study:  Researchers want to know whether a new diet leads to more weight loss than a standard diet in a certain community of 10,000 people. They recruit 50 males athletes and 50 female athletes to be in the study. Then, they assign all of the female athletes to the control group and all of the male athletes to the treatment group. They record the total weight loss of each individual after one month.

Results:  The researchers did not use random selection to obtain their sample since they specifically chose 100 athletes. Because of this, their sample is not representative of the overall population so their external validity is compromised – they will not be able to generalize the findings from the study to the overall population. Also, they split individuals into groups based on gender rather than using random assignment, which means their internal validity is also compromised – differences in weight loss might be due to gender rather than the diet.

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Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study.

It is possible to have both random selection and assignment in a study. Let’s say you drew a random sample of 100 clients from a population list of 1000 current clients of your organization. That is random sampling. Now, let’s say you randomly assign 50 of these clients to get some new additional treatment and the other 50 to be controls. That’s random assignment.

It is also possible to have only one of these (random selection or random assignment) but not the other in a study. For instance, if you do not randomly draw the 100 cases from your list of 1000 but instead just take the first 100 on the list, you do not have random selection. But you could still randomly assign this nonrandom sample to treatment versus control. Or, you could randomly select 100 from your list of 1000 and then nonrandomly (haphazardly) assign them to treatment or control.

And, it’s possible to have neither random selection nor random assignment. In a typical nonequivalent groups design in education you might nonrandomly choose two 5th grade classes to be in your study. This is nonrandom selection. Then, you could arbitrarily assign one to get the new educational program and the other to be the control. This is nonrandom (or nonequivalent) assignment.

Random selection is related to sampling . Therefore it is most related to the external validity (or generalizability) of your results. After all, we would randomly sample so that our research participants better represent the larger group from which they’re drawn. Random assignment is most related to design . In fact, when we randomly assign participants to treatments we have, by definition, an experimental design . Therefore, random assignment is most related to internal validity . After all, we randomly assign in order to help assure that our treatment groups are similar to each other (i.e. equivalent) prior to the treatment.

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Parametric and Resampling Statistics (cont):

Random sampling and random assignment.

The major assumption behind traditional parametric procedures--more fundamental than normality and homogeneity of variance--is the assumption that we have randomly sampled from some population (usually a normal one). Of course virtually no study you are likely to run will employ true random sampling, but leave that aside for the moment. To see why this assumption is so critical, consider an example in which we draw two samples, calculate the sample means and variances, and use those as estimates of the corresponding population parameters. For example, we might draw a random sample of anorexic girls (potentially) given one treatment, and a random sample of anorexic girls given another treatment, and use our statistical test to draw inferences about the parameters of the corresponding populations from which the girls were randomly sampled. We would probably like to show that our favorite treatment leads to greater weight gain than the competing treatment, and thus the mean of the population of all girls given our favorite treatment is greater than the mean of the other population. But statistically, it makes no sense to say that the sample means are estimates of the corresponding population parameters unless the samples are drawn randomly from that (those) populations(s). (Using the 12 middle school girls in your third period living-arts class is not going to give you a believable estimate of U. S. (let alone world) weights of pre-adolescent girls.) That is why the assumption of random sampling is so critical. In the extreme, if we don't sample randomly, we can't say anything meaningful about the parameters, so why bother? That is part of the argument put forth by the resampling camp.

Of course, those of us who have been involved in statistics for any length of time recognize this assumption, but we rarely give it much thought. We assume that our sample, though not really random, is a pretty good example of what we would have if we had the resources to draw truly random samples, and we go merrily on our way, confident in the belief that the samples we actually have are "good enough" for the purpose. That is where the parametric folks and the resampling folks have a parting of the ways.

The parametric people are not necessarily wrong in thinking that on occasion nonrandom sampling is good enough. If we are measuring something that would not be expected to vary systematically among participants, such as the effect of specific stimulus variations on visual illusions, then a convenience sample may give acceptable results. But keep in mind that any inferences we draw are not statistical inferences, but logical inferences. Without random sampling we cannot make a statistical inference about the mean of a larger population. But on nonstatistical grounds it may make good sense to assume that we have learned something about how people in general process visual information. But using that kind of argument to brush aside some of the criticisms of parametric tests doesn't diminish the fact that the resampling approach legitimately differs in its underlying philosophy.

The resampling approach, and for now I mean the randomization test approach, and not bootstrapping, really looks at the problem differently. In the first place, people in that area don't give a "population" the centrality that we are used to assigning to it in parametric statistics. They don't speak as if they sit around fondly imagining those lovely bell-shaped distributions with numbers streaming out of them, that we often see in introductory textbooks. In fact, they hardly appear to think about populations at all. And they certainly don't think about drawing random samples from those imaginary populations. Those people are as qualified as you could wish as statisticians, but they don't worry too much about estimating parameters, for which you really do need random samples. They just want to know the likelihood of the sample data falling as they did if treatments were equally effective. And for that, they don't absolutely need to think of populations.

In the history of statistics, the procedures with which we are most familiar were developed on the assumption of random sampling. And they were developed with the expectation that we are trying to estimate the corresponding population mean, variance, or whatever. This idea of "estimation" is central to the whole history of traditional statistics--we estimate population means so that we can (hopefully) conclude that they are different and that the treatments have different effects.

But that is not what the randomization test folks are trying to do. They start with the assumption that samples are probably not drawn randomly, and assume that we have no valid basis (or need) for estimating population parameters. This, I think, is the best reason to think of these procedures as nonparametric procedures, though there are other reasons to call them that. But if we can't estimate population parameters, and thus have no legitimate basis for retaining or rejecting a null hypothesis about those parameters, what basis do we have for constructing any statistical test. It turns out that we have legitimate alternative ways for testing our hypothesis, though I'm not sure that we should even be calling it a null hypothesis.

This difference over the role of random sampling is a critical difference between the two approaches. But that is not all. The resampling people, in particular, care greatly about random assignment . The whole approach is based on the idea of random assignment of cases to conditions. That will appear to create problems later on, but take it as part of the underlying rationale. Both groups certainly think that random assignment to conditions is important, primarily because it rules out alternative explanations for any differences that are found. But the resampling camp goes further, and makes it the center point of their analysis. To put it very succinctly, a randomization test works on the logical principle that if cases were randomly assigned to treatments, and if treatments have absolutely no effect on scores, then a particular score is just as likely to have appeared under one condition than under any other. Notice that the principle of random assignment tells us that if the null hypothesis is true, we could validly shuffle the data and expect to get essentially the same results. This is why random assignment is fundamental to the statistical procedure employed.

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A good way to understand random sampling, random assignment, and the difference between the two is to draw a random sample of your own and carry out an example of random assignment. To complete this assignment, begin by opening a second web browser window (or printing this page), and then finish each part in the order below. Psychology Headlines From around the world. People's Moral Values Change with the Seasons, Study Finds U.K. Reports of Antisemitic Incidents Reach Record High U.S. Secretaries of State Urge Musk to Block Election Misinformation Sierra Leone Used to Chain Mental Health Patients; Times Are Changing Report Details Systemic Racism Within London Police Service U.S. Schools Taking Meditation Breaks to Help Students Manage Stress B'tselem's report contains testimony from 55 recently released Palestinian detainees, whose graphic accounts suggest a dramatic worsening of prison conditions since the start of the Gaza war 10 months ago. A U.N. report last week also contained shocking allegations of abuse directed against Palestinian…">Israeli Human Rights Group Alleges Abuse of Palestinian Detainees Worldwide, Scientists Very Concerned About Climate Change, Survey Finds Source: Psychology News Center 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. You Might Also Like Difference Between Sympathy and Empathy Explained America vs. Russia: The Key Differences to Know Difference Between Lo Mein and Chow Mein Explained Privacy overview. Cookie Duration Description cookielawinfo-checkbox-analytics 11 months This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". cookielawinfo-checkbox-functional 11 months The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". cookielawinfo-checkbox-necessary 11 months This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". cookielawinfo-checkbox-others 11 months This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. cookielawinfo-checkbox-performance 11 months This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". viewed_cookie_policy 11 months The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. Terms and Conditions - Privacy Policy Search Search Please fill out this field. 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 .” Terms of Service Editorial Policy Privacy Policy Differences in isotopic compositions of individual grains and aggregated seed samples affect interpretation of ancient plant cultivation practices Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jade D'Alpoim Guedes ORCID record for Cheryl A. Makarewicz For correspondence: [email protected] Info/History Preview PDF The stable carbon (lower case Greek delta 13 C) and nitrogen (lower case Greek delta 15 N) isotope analysis of charred archaeological grains provides a remarkably precise scale of information: the growing conditions under which a plant was cultivated in a single field and season. Here we investigate how the measurement of single individual grains or aggregate bulk samples for carbon and nitrogen isotopes impacts how we characterize variation and, consequently, our interpretations of ancient cultivation practices. Using experimentally grown barley (Hordeum vulgare var. nudum), this work investigates lower case Greek delta 13 C and lower case Greek delta 15 N intra-panicle variation between both uncharred and charred individual grains from four plants. We found limited intra- and inter-panicle isotopic variation in single grain isotope values, ca. 0.5per thousand in lower case Greek delta 13 C and ca. 1per thousand in lower case Greek delta 15 N, reemphasizing the degree to which grains are representative of their local growing conditions. To explore the interpretive impact of aggregate versus single-grain isotopic sampling, we measured charred barley recovered from a single storage context excavated from Trench 42 (ca. 1900 BCE) at Harappa. Aggregate samples of a random selection of Trench 42 barley demonstrated remarkable inter-sample homogeneity, with a less than 0.5per thousand difference in lower case Greek delta 13 C and lower case Greek delta 15 N values, reinforcing the ability of aggregate samples to capture a representative isotopic average of a single depositional context. However, the measurement of single-grains revealed moderate 2 to 3per thousand variation in lower case Greek delta 13 C, and an outstandingly wide isotopic variation of ca. 8per thousand in lower case Greek delta 15 N values, indicating the degree to which cultivation practices varied beyond what the bulk samples indicated. These results highlight how decisions in the selection and measurement of archaeological grains for isotopic analysis impact data resolution, with profound consequences for understanding past agricultural diversity. Competing Interest Statement The authors have declared no competing interest. View the discussion thread. 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However, many students struggle to differentiate between these two concepts, and very often use these terms interchangeably. Here we will explain the distinction between random sampling and random assignment. Random sampling vs. random assignment (scope of inference) Random sampling vs. random assignment (scope of inference) Google Classroom. Microsoft Teams. Hilary wants to determine if any relationship exists between Vitamin D and blood pressure. She is considering using one of a few different designs for her study. Determine what type of conclusions can be drawn from each study design. Random Selection vs. Random Assignment A simple explanation of the difference between random selection and random assignment along with several examples. Random Assignment in Experiments Random sampling and random assignment are both important concepts in research, but it's important to understand the difference between them. Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. PDF Random sampling vs. assignment Random sampling allows us to obtain a sample representative of the population. Therefore, results of the study can be generalized to the population. Random assignment allows us to make sure that the only difference between the various treatment groups is what we are studying. For example, in the serif/sans serif example, random assignment helps ... Random Sampling vs Random Assignment Random sampling and Random assignment are two important distinctions, and understanding the difference between the two is important to get accurate and dependable results. Random sampling is a proper procedure for selecting a subset of bodies from a larger set of bodies, each of which has the same likelihood of being selected. What's the difference between random assignment and random ... 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 vs. random assignment (scope of inference) The table below summarizes what type of conclusions we can make based on the study design. Random sampling. Not random sampling. Random assignment. Can determine causal relationship in population. This design is relatively rare in the real world. Can determine causal relationship in that sample only. Random Sampling vs. Random Assignment Lecture Lecturer: Cody ConnerIn this video I discuss the differences between random sampling and random assignment.Learn more and find our documents on our OSF page:... What's the difference between random selection and random ... Random selection, or random sampling, is a way of selecting members of a population for your study's sample. In contrast, random assignment is a way of sorting the sample into control and experimental groups. Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal ... Difference between Random Selection and Random Assignment Difference between Random Selection and Random Assignment Random selection and random assignment are commonly confused or used interchangeably, though the terms refer to entirely different processes. Random selection refers to how sample members (study participants) are selected from the population for inclusion in the study. Random assignment is an aspect of experimental design in which study ... Random Assignment in Psychology: Definition & Examples Random Selection vs. Random Assignment Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study. On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. Random sampling vs. random assignment This video discusses random sampling and random assignment, and concepts of generalizability and causality. PDF Random-is-Random-delMas-Fry Random is Random, but not always for the same purpose - easy to conflate the purposes of randomization in study design. Idea of "random" central to both sampling and assignment to groups, but role of randomness is different. "Bias" can refer to bias in sampling, or researcher bias in assigning groups. Random Selection vs. Random Assignment A simple explanation of the difference between random selection and random assignment along with several examples. Random Selection & Assignment Random selection is how you draw the sample of people for your study from a population. Random assignment is how you assign the sample that you draw to different groups or treatments in your study. It is possible to have both random selection and assignment in a study. Let's say you drew a random sample of 100 clients from a population list ... Random Sampling and Random Assignment Parametric and Resampling Statistics (cont): Random Sampling and Random Assignment. random sample random sample. systematically. nonparametric. Random Assignment Assignment A good way to understand random sampling, random assignment, and the difference between the two is to draw a random sample of your own and carry out an example of random assignment. To complete this assignment, begin by opening a second web browser window (or printing this page), and then finish each part in the order below. Random assignment Mathematically, there are distinctions between randomization, pseudorandomization, and quasirandomization, as well as between random number generators and pseudorandom number generators. How much these differences matter in experiments (such as clinical trials) is a matter of trial design and statistical rigor, which affect evidence grading. Studies done with pseudo- or quasirandomization are ... Representative Sample vs. Random Sample: What's the Difference? What is the difference between representative samples and random samples, and how are they are used to reduce sampling bias? PDF Difference between Random Selection and Random Assignment Random selection and random assignment are commonly confused or used interchangeably, though the terms refer to entirely different processes. Random selection refers to how sample members (study participants) are selected from the population for inclusion in the study. Random assignment is an aspect of experimental design in which study ... Difference Between Random Sampling and Random Assignment? Random sampling is a method of selecting people for a study. Random assignment splits the sample participants into two groups: control and experimental. Simple Random Sample vs. Stratified Random Sample: What's the Difference? A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups based on shared characteristics. Differences in isotopic compositions of individual grains and Aggregate samples of a random selection of Trench 42 barley demonstrated remarkable inter-sample homogeneity, with a less than 0.5per thousand difference in lower case Greek delta13C and lower case Greek delta15N values, reinforcing the ability of aggregate samples to capture a representative isotopic average of a single depositional context. assignment essay homework paper phd report resume review thesis