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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply Psychology

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On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

• What is sampling in a research context?
• The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

• How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

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Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

• Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
• The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
• Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
• Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
• When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

Excellent and helpful for junior researcher!

Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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• Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Table of contents

Population vs sample, probability sampling methods, non-probability sampling methods, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

In a non-probability sample , individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalisable results.

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g., by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and many students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

Purposive sampling , also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.

You want to know more about the opinions and experiences of students with a disability at your university, so you purposely select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to ‘snowballs’ as you get in contact with more people.

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people she knows in the area.

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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

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 .

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 .

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

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What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages:  Generalisability may decrease if baseline characteristics repeat across every nth participant

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages:  Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

References (pdf)

Mohamed Khalifa

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No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

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Sampling Methods: Different Types in Research

By Jim Frost 3 Comments

What Are Sampling Methods?

Sampling methods are the processes by which you draw a sample from a population . When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences.

A population is the complete set of individuals that you’re studying. A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample.

Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs of your study—for example, adult Swedish women who are otherwise healthy but have osteoporosis. Then choose your sampling method.

Learn more about populations and samples , inferential vs. descriptive statistics and populations and parameters .

In research and inferential statistics , sampling methods are a vital issue. How you draw your sample affects how much you can trust the results! If your sample doesn’t reflect the population, your results might not be valid. It’s a crucial part of experimental design .

In this post, learn more about sampling methods, which ones produce representative samples, and the pros and cons of each procedure.

Probability vs Non-Probability Sampling Methods

Sampling methods have the following two broad categories:

• Probability sampling : Entails random selection and typically, but not always, requires a list of the entire population.
• Non-probability sampling : Does not use random selection but some other process, such as convenience. Usually does not sample from the whole population.

Probability sampling is typically more difficult and costly to implement, but, in exchange, these processes tend to increase validity by producing representative samples. In short, you can make valid conclusions about the population.  A statistical inference is when you use a sample to learn about a population. Learn more about Making Statistical Inferences .

On the other hand, non-probability sampling methods are often easier and less expensive, but the trade-off is that the validity of your conclusions is questionable. You might not be able to trust the results. Sampling bias is more likely to occur.

Learn more about Validity in Research and Psychology: Types & Examples and Internal and External Validity .

Probability Sampling Methods

Given the benefits of using representative samples, you’ll typically want to use a probability sampling method whenever possible. Let’s go over the standard methods. They each have pros and cons. Click the links to learn more about each sampling method and see examples. Learn more about representative samples .

To use a probability method, you’ll first need to develop a sampling frame, which lists all members of your target population. Then you can use one of the following methods.

Learn more about Sampling Frames: Definition, Examples & Uses .

Simple Random Sampling (SRS)

In simple random sampling (SRS), researchers take a complete list of the population and randomly select participants from it. All population members have an equal likelihood of being selected. Out of all sampling methods, statisticians consider this one to be the gold standard for producing representative samples. It’s entirely random, leaving little room for accidentally biasing the results.

However, this sampling method has some drawbacks.

First and foremost, this method can be pretty unwieldy and require abundant resources. For one thing, it requires a list of all population members, which can be a tremendous hurdle by itself. Attempting to perform SRS with an incomplete population list causes undercoverage bias and a nonrepresentative sample.

Furthermore, while random selection is beneficial, it also ensures that the subjects are maximally dispersed, making them harder to contact.

SRS can exclude smaller but crucial subpopulations purely by chance. Additionally, this approach produces less precise estimates for subgroups and the differences between subgroups than some other probability sampling methods.

Learn more about Simple Random Sampling  and Undercoverage Bias: Definition & Examples

Systematic Sampling

Systematic sampling is similar to SRS but attempts to ease some of the difficulties for researchers. There are several versions of this method.

One form uses a complete list of the population. The researchers randomly select the first subject and then move down the list choosing every X th subject rather than using a randomized technique.

The other form does not use a complete list of the population. This sampling method is suitable for populations that are tough to document, such as the homeless, because a comprehensive list won’t exist. The essential requirement for this sampling method is knowing how to locate them. While it’s not perfect, it’s a feasible option when you can’t obtain the full list.

Suppose you want to survey theater patrons but lack a complete list. Instead, you can use systematic sampling and recruit every 20th person who exits the theater. This approach works because they leave randomly.

This sampling method has some disadvantages. The form that uses a complete list of the population can closely mirror the results of simple random sampling. However, the non-randomness increases the potential for manipulation, even if accidentally. Additionally, patterns in the list can unintentionally create a non-representative sample.

The form that doesn’t use a list has more potential problems. Namely, it increases the potential for missing subgroups and acquiring a non-representative sample. This sampling method increases the knowledge you must have about the population and their habits. Without that knowledge, you won’t be able to find subjects that reflect the whole population.

Learn more about Systematic Sampling .

Stratified Sampling

In stratified sampling, researchers divide a population into similar subpopulations (strata). Then they randomly sample from the strata.

This sampling method can guarantee the presence of small but vital subpopulations in the sample. Relative to SRS, this method can increase the precision of subgroup estimates and the differences between subgroups. In short, it helps researchers gain a better understanding of the subgroups. Dividing the whole population into smaller, more similar subsets can also reduce costs and simplify data collection.

The drawbacks are that this sampling method requires additional upfront knowledge and planning. The researchers must know enough about the subgroups to devise an effective strata scheme. Then they must have sufficient information about all population members to assign them to the correct strata.

Learn more about Stratified Sampling .

Cluster Sampling

Like stratified sampling, the cluster sampling method divides the whole population into smaller groups. However, unlike strata, each cluster mirrors the full diversity present in the population. Then the researchers randomly sample from some of these clusters.

The primary benefit of this sampling method is that it reduces the costs of studying large, geographically dispersed populations. Using this method, researchers don’t need to sample the entire geographic region but only certain areas because they know individual clusters are similar to the population. Additionally, they don’t need to develop a list of potential subjects for clusters from which they’re not sampling. These considerations can significantly reduce planning, administrative, and travel costs.

When researchers can’t create a list of the entire population, cluster sampling can be an excellent choice.

On the downside, cluster sampling increases the design complexity. Researchers must understand how well each cluster approximates the whole population. If the clusters don’t fully represent the population, results can be biased. In real-world studies, clusters tend to be naturally occurring groups that don’t mirror the population, which reduces the ability to draw valid conclusions.

Learn more about Cluster Sampling .

Non-Probability Sampling Methods

Non-probability sampling methods don’t use random selection, and they typically don’t use a complete population list. While these methods are simpler and less expensive, your results are more likely to be biased, reducing your ability to make sound conclusions.

Researchers often use non-probability sampling methods for exploratory research, pilot studies, and qualitative research . These sampling methods provide quick and rough assessments, help work kinks out of measurement instruments and procedures, and help refine the design for a more rigorous study in the future.

Below are several standard non-probability sampling methods:

• Convenience sampling : The main criteria for recruiting subjects are those who are easy to contact and willing to participate. There are no inclusion requirements. Online polls are a type of convenience sampling. Learn more about Convenience Sampling .
• Quota Sampling : Non-random selection of subjects from population subgroups that the researchers define. Learn more about Quota Sampling .
• Purposive sampling : Investigators use subject-area knowledge to handpick a sample they think will help their study. Learn more about Purposive Sampling .
• Snowball sampling : Researchers use subjects to find and recruit other subjects. This method is helpful when a population is hard to contact. When recruits help you find more recruits, and those help find even more, and so on, the total number snowballs. Learn more about Snowball Sampling .

As you can see, there are many sampling methods. Each one has benefits and disadvantages. When designing a study, evaluate the nature of your target population, your research goals, and the available time and resources to choose your sampling method. After deciding between the sampling methods, calculate your sample size using a power analysis .

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)

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Reader Interactions

July 24, 2024 at 8:56 am

Hello Mr. Frost,

I would like to know whether people with mild Parkinson’s Disease symptoms are less likely to have kidney stones. Do PwP (People with Parkinson’s) have significantly less incidences of kidney stones than in the general population (~ 10%). So far, I have asked 12 people I know who has been diagnosed with Parkinson’s and 0% had kidney stones. I would like to increase my sampling size by randomly sampling members of a forum for PwP I belong to. Should I get a list of all forum subscribers and randomly select around 40 forum members to pose the question, “If you have been officially diagnosed with Parkinson’s, have also had a kidney stone?”. What would you suggest? I had posed the question in the forum before, but only PwP folks that had a Kidney stone responded.

Thanks, Mike

May 17, 2022 at 12:38 am

I think stratified sampling will work __ mke two groups as stratas _ then use SRS to obtain a complete sample .

May 15, 2022 at 7:37 pm

hi.what sampling technique will i use if my respondents are 1st yr college students awardees vs non awardees of different courses?

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An overview of sampling methods

Last updated

27 February 2023

Reviewed by

Cathy Heath

When researching perceptions or attributes of a product, service, or people, you have two options:

Survey every person in your chosen group (the target market, or population), collate your responses, and reach your conclusions.

Select a smaller group from within your target market and use their answers to represent everyone. This option is sampling .

Sampling saves you time and money. When you use the sampling method, the whole population being studied is called the sampling frame .

The sample you choose should represent your target market, or the sampling frame, well enough to do one of the following:

Generalize your findings across the sampling frame and use them as though you had surveyed everyone

Use the findings to decide on your next step, which might involve more in-depth sampling

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

How was sampling developed?

Valery Glivenko and Francesco Cantelli, two mathematicians studying probability theory in the early 1900s, devised the sampling method. Their research showed that a properly chosen sample of people would reflect the larger group’s status, opinions, decisions, and decision-making steps.

They proved you don't need to survey the entire target market, thereby saving the rest of us a lot of time and money.

• Why is sampling important?

We’ve already touched on the fact that sampling saves you time and money. When you get reliable results quickly, you can act on them sooner. And the money you save can pay for something else.

It’s often easier to survey a sample than a whole population. Sample inferences can be more reliable than those you get from a very large group because you can choose your samples carefully and scientifically.

Sampling is also useful because it is often impossible to survey the entire population. You probably have no choice but to collect only a sample in the first place.

Because you’re working with fewer people, you can collect richer data, which makes your research more accurate. You can:

Ask more questions

Go into more detail

Seek opinions instead of just collecting facts

Observe user behaviors

Double-check your findings if you need to

In short, sampling works! Let's take a look at the most common sampling methods.

• Types of sampling methods

There are two main sampling methods: probability sampling and non-probability sampling. These can be further refined, which we'll cover shortly. You can then decide which approach best suits your research project.

Probability sampling method

Probability sampling is used in quantitative research , so it provides data on the survey topic in terms of numbers. Probability relates to mathematics, hence the name ‘quantitative research’. Subjects are asked questions like:

How many boxes of candy do you buy at one time?

How often do you shop for candy?

How much would you pay for a box of candy?

This method is also called random sampling because everyone in the target market has an equal chance of being chosen for the survey. It is designed to reduce sampling error for the most important variables. You should, therefore, get results that fairly reflect the larger population.

Non-probability sampling method

In this method, not everyone has an equal chance of being part of the sample. It's usually easier (and cheaper) to select people for the sample group. You choose people who are more likely to be involved in or know more about the topic you’re researching.

Non-probability sampling is used for qualitative research. Qualitative data is generated by questions like:

Where do you usually shop for candy (supermarket, gas station, etc.?)

Which candy brand do you usually buy?

Why do you like that brand?

• Probability sampling methods

Here are five ways of doing probability sampling:

Simple random sampling (basic probability sampling)

Systematic sampling

Stratified sampling.

Cluster sampling

Multi-stage sampling

Simple random sampling.

There are three basic steps to simple random sampling:

Choose your sampling frame.

Decide on your sample size. Make sure it is large enough to give you reliable data.

Randomly choose your sample participants.

You could put all their names in a hat, shake the hat to mix the names, and pull out however many names you want in your sample (without looking!)

You could be more scientific by giving each participant a number and then using a random number generator program to choose the numbers.

Instead of choosing names or numbers, you decide beforehand on a selection method. For example, collect all the names in your sampling frame and start at, for example, the fifth person on the list, then choose every fourth name or every tenth name. Alternatively, you could choose everyone whose last name begins with randomly-selected initials, such as A, G, or W.

Choose your system of selecting names, and away you go.

This is a more sophisticated way to choose your sample. You break the sampling frame down into important subgroups or strata . Then, decide how many you want in your sample, and choose an equal number (or a proportionate number) from each subgroup.

For example, you want to survey how many people in a geographic area buy candy, so you compile a list of everyone in that area. You then break that list down into, for example, males and females, then into pre-teens, teenagers, young adults, senior citizens, etc. who are male or female.

So, if there are 1,000 young male adults and 2,000 young female adults in the whole sampling frame, you may want to choose 100 males and 200 females to keep the proportions balanced. You then choose the individual survey participants through the systematic sampling method.

Clustered sampling

This method is used when you want to subdivide a sample into smaller groups or clusters that are geographically or organizationally related.

Let’s say you’re doing quantitative research into candy sales. You could choose your sample participants from urban, suburban, or rural populations. This would give you three geographic clusters from which to select your participants.

This is a more refined way of doing cluster sampling. Let’s say you have your urban cluster, which is your primary sampling unit. You can subdivide this into a secondary sampling unit, say, participants who typically buy their candy in supermarkets. You could then further subdivide this group into your ultimate sampling unit. Finally, you select the actual survey participants from this unit.

• Uses of probability sampling

Probability sampling has three main advantages:

It helps minimizes the likelihood of sampling bias. How you choose your sample determines the quality of your results. Probability sampling gives you an unbiased, randomly selected sample of your target market.

It allows you to create representative samples and subgroups within a sample out of a large or diverse target market.

It lets you use sophisticated statistical methods to select as close to perfect samples as possible.

• Non-probability sampling methods

To recap, with non-probability sampling, you choose people for your sample in a non-random way, so not everyone in your sampling frame has an equal chance of being chosen. Your research findings, therefore, may not be as representative overall as probability sampling, but you may not want them to be.

Sampling bias is not a concern if all potential survey participants share similar traits. For example, you may want to specifically focus on young male adults who spend more than others on candy. In addition, it is usually a cheaper and quicker method because you don't have to work out a complex selection system that represents the entire population in that community.

Researchers do need to be mindful of carefully considering the strengths and limitations of each method before selecting a sampling technique.

Non-probability sampling is best for exploratory research , such as at the beginning of a research project.

There are five main types of non-probability sampling methods:

Convenience sampling

Purposive sampling, voluntary response sampling, snowball sampling, quota sampling.

The strategy of convenience sampling is to choose your sample quickly and efficiently, using the least effort, usually to save money.

Let's say you want to survey the opinions of 100 millennials about a particular topic. You could send out a questionnaire over the social media platforms millennials use. Ask respondents to confirm their birth year at the top of their response sheet and, when you have your 100 responses, begin your analysis. Or you could visit restaurants and bars where millennials spend their evenings and sign people up.

A drawback of convenience sampling is that it may not yield results that apply to a broader population.

This method relies on your judgment to choose the most likely sample to deliver the most useful results. You must know enough about the survey goals and the sampling frame to choose the most appropriate sample respondents.

Your knowledge and experience save you time because you know your ideal sample candidates, so you should get high-quality results.

This method is similar to convenience sampling, but it is based on potential sample members volunteering rather than you looking for people.

You make it known you want to do a survey on a particular topic for a particular reason and wait until enough people volunteer. Then you give them the questionnaire or arrange interviews to ask your questions directly.

Snowball sampling involves asking selected participants to refer others who may qualify for the survey. This method is best used when there is no sampling frame available. It is also useful when the researcher doesn’t know much about the target population.

Let's say you want to research a niche topic that involves people who may be difficult to locate. For our candy example, this could be young males who buy a lot of candy, go rock climbing during the day, and watch adventure movies at night. You ask each participant to name others they know who do the same things, so you can contact them. As you make contact with more people, your sample 'snowballs' until you have all the names you need.

This sampling method involves collecting the specific number of units (quotas) from your predetermined subpopulations. Quota sampling is a way of ensuring that your sample accurately represents the sampling frame.

• Uses of non-probability sampling

You can use non-probability sampling when you:

Want to do a quick test to see if a more detailed and sophisticated survey may be worthwhile

Want to explore an idea to see if it 'has legs'

Launch a pilot study

Do some initial qualitative research

Have little time or money available (half a loaf is better than no bread at all)

Want to see if the initial results will help you justify a longer, more detailed, and more expensive research project

• The main types of sampling bias, and how to avoid them

Sampling bias can fog or limit your research results. This will have an impact when you generalize your results across the whole target market. The two main causes of sampling bias are faulty research design and poor data collection or recording. They can affect probability and non-probability sampling.

Faulty research

If a surveyor chooses participants inappropriately, the results will not reflect the population as a whole.

A famous example is the 1948 presidential race. A telephone survey was conducted to see which candidate had more support. The problem with the research design was that, in 1948, most people with telephones were wealthy, and their opinions were very different from voters as a whole. The research implied Dewey would win, but it was Truman who became president.

Poor data collection or recording

This problem speaks for itself. The survey may be well structured, the sample groups appropriate, the questions clear and easy to understand, and the cluster sizes appropriate. But if surveyors check the wrong boxes when they get an answer or if the entire subgroup results are lost, the survey results will be biased.

How do you minimize bias in sampling?

 To get results you can rely on, you must:

Know enough about your target market

Choose one or more sample surveys to cover the whole target market properly

Choose enough people in each sample so your results mirror your target market

Have content validity . This means the content of your questions must be direct and efficiently worded. If it isn’t, the viability of your survey could be questioned. That would also be a waste of time and money, so make the wording of your questions your top focus.

If using probability sampling, make sure your sampling frame includes everyone it should and that your random sampling selection process includes the right proportion of the subgroups

If using non-probability sampling, focus on fairness, equality, and completeness in identifying your samples and subgroups. Then balance those criteria against simple convenience or other relevant factors.

What are the five types of sampling bias?

Self-selection bias. If you mass-mail questionnaires to everyone in the sample, you’re more likely to get results from people with extrovert or activist personalities and not from introverts or pragmatists. So if your convenience sampling focuses on getting your quota responses quickly, it may be skewed.

Non-response bias. Unhappy customers, stressed-out employees, or other sub-groups may not want to cooperate or they may pull out early.

Undercoverage bias. If your survey is done, say, via email or social media platforms, it will miss people without internet access, such as those living in rural areas, the elderly, or lower-income groups.

Survivorship bias. Unsuccessful people are less likely to take part. Another example may be a researcher excluding results that don’t support the overall goal. If the CEO wants to tell the shareholders about a successful product or project at the AGM, some less positive survey results may go “missing” (to take an extreme example.) The result is that your data will reflect an overly optimistic representation of the truth.

Pre-screening bias. If the researcher, whose experience and knowledge are being used to pre-select respondents in a judgmental sampling, focuses more on convenience than judgment, the results may be compromised.

How do you minimize sampling bias?

Focus on the bullet points in the next section and:

Make survey questionnaires as direct, easy, short, and available as possible, so participants are more likely to complete them accurately and send them back

Follow up with the people who have been selected but have not returned their responses

Ignore any pressure that may produce bias

• How do you decide on the type of sampling to use?

Use the ideas you've gleaned from this article to give yourself a platform, then choose the best method to meet your goals while staying within your time and cost limits.

If it isn't obvious which method you should choose, use this strategy:

Clarify your research goals

Clarify how accurate your research results must be to reach your goals

Evaluate your goals against time and budget

List the two or three most obvious sampling methods that will work for you

Confirm the availability of your resources (researchers, computer time, etc.)

Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints

Make your decision

• The takeaway

Effective market research is the basis of successful marketing, advertising, and future productivity. By selecting the most appropriate sampling methods, you will collect the most useful market data and make the most effective decisions.

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Types of Sampling Methods (With Examples)

Researchers are often interested in answering questions about populations like:

• What is the average height of a certain species of plant?
• What is the average weight of a certain species of bird?
• What percentage of citizens in a certain city support a certain law?

One way to answer these questions is to go around and collect data on every single individual in the population of interest.

However, this is typically too costly and time-consuming which is why researchers instead take a  sample  of the population and use the data from the sample to draw conclusions about the population as a whole.

There are many different methods researchers can potentially use to obtain individuals to be in a sample. These are known as  sampling methods .

In this post we share the most commonly used sampling methods in statistics, including the benefits and drawbacks of the various methods.

Probability Sampling Methods

The first class of sampling methods is known as  probability sampling methods  because every member in a population has an equal probability of being selected to be in the sample.

Simple random sample

Definition: Every member of a population has an equal chance of being selected to be in the sample. Randomly select members through the use of a random number generator or some means of random selection. 

Example: We put the names of every student in a class into a hat and randomly draw out names to get a sample of students.

Benefit:  Simple random samples are usually representative of the population we’re interested in since every member has an equal chance of being included in the sample.

Stratified random sample

Definition: Split a population into groups. Randomly select some members from each group to be in the sample.

Example: Split up all students in a school according to their grade – freshman, sophomores, juniors, and seniors. Ask 50 students from each grade to complete a survey about the school lunches.

Benefit:  Stratified random samples ensure that members from each group in the population are included in the survey.

Cluster random sample

Definition: Split a population into clusters. Randomly select some of the clusters and include all members from those clusters in the sample.

Example: A company that gives whale watching tours wants to survey its customers. Out of ten tours they give one day, they randomly select four tours and ask every customer about their experience.

Benefit: Cluster random samples get every member from some of the groups, which is useful when each group is reflective of the population as a whole.

Systematic random sample

Definition:  Put every member of a population into some order. Choosing a random starting point and select every n th member to be in the sample.

Example:  A teacher puts students in alphabetical order according to their last name, randomly chooses a starting point, and picks every 5th student to be in the sample.

Benefit: Systematic random samples are usually representative of the population we’re interested in since every member has an equal chance of being included in the sample.

Non-probability Sampling Methods

Another class of sampling methods is known as non-probability sampling methods  because not every member in a population has an equal probability of being selected to be in the sample.

This type of sampling method is sometimes used because it’s much cheaper and more convenient compared to probability sampling methods. It’s often used during exploratory analysis when researchers simply want to gain an initial understanding of a population.

However, the samples that result from these sampling methods cannot be used to draw inferences about the populations they came from because they typically aren’t representative samples.

Convenience sample

Definition:  Choose members of a population that are readily available to be included in the sample.

Example:  A researcher stands in front of a library during the day and polls people that happen to walk by.

Drawback: Location and time of day will affect the results. More than likely, the sample will suffer from undercoverage bias since certain people (e.g. those who work during the day) will not be represented as much in the sample.

Voluntary response sample

Definition:  A researcher puts out a request for volunteers to be included in a study and members of a population voluntarily decide to be included in the sample or not. 

Example:  A radio host asks listeners to go online and take a survey on his website.

Drawback:   People who voluntarily respond will likely have stronger opinions (positive or negative) than the rest of the population, which makes them an unrepresentative sample. Using this sampling method, the sample is likely to suffer from nonresponse bias – certain groups of people are simply less likely to provide responses.

Snowball sample

Definition:  Researchers recruit initial subjects to be in a study and then ask those initial subjects to recruit additional subjects to be in the study. Using this approach, the sample size “snowballs” bigger and bigger as each additional subject recruits more subjects.

Example: Researchers are conducting a study of individuals with rare diseases, but it’s difficult to find individuals who actually have the disease. However, if they can find just a few initial individuals to be in the study then they can ask them to recruit further individuals they may know through a private support group or through some other means.

Drawback:   Sampling bias is likely to occur. Because initial subjects recruit additional subjects, it’s likely that many of the subjects will share similar traits or characteristics that might be unrepresentative of the larger population under study. Thus, findings from the sample can’t be extrapolated to the population.

Read more about snowballing sampling here .

Purposive sample

Definition:  Researchers recruit individuals based on who they think will be most useful based on the purpose of their study.

Example:  Researchers want to know about the opinions that individuals in a city have about a potential new rock climbing gym being placed in the city square so they purposely seek out individuals that hang out at other rock climbing gyms around the city.

Drawback:   The individuals in the sample are unlikely to be representative of the overall population. Thus, findings from the sample can’t be extrapolated to the population.

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9 Replies to “Types of Sampling Methods (With Examples)”

Thank You for explaining the sampling types with simple easy to understand examples. According to me this feedback falls under voluntary type as you ask to fill forms online which not compulsory ……. hope I understand the concept.

Insightful. Thanks for sharing these notes.

Wow this is amazing it’s really understandable

I am so glad I found your site

Nice topic before reading this I have no idea about the topic but reading it makes me understand the whole concept of it thanks to you for making this topic simple. The words you use and the examples you gave were like very simple to understand for a person. The topic may be explained in another way in difficult manner but you choosed simple words to make understand the difficult topic. Thank you sir for this information.

I really enjoyed the whole course. A great course about sampling.

Simple rendom sampling Stratified random sample Cluster random sample Systematic random sample Non-probability Sampling Methods Convenience sample Voluntary response sample Snowball sample Purposive sample

All the topic explained in a very simple way really amazing Thanks 👍

Compelling statistical work. Good job and congratulations.

Thank you for your support and feedback Ruth! We greatly appreciate it!

irrevocably perfect !

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• Knowledge Base

Methodology

• What Is Probability Sampling? | Types & Examples

What Is Probability Sampling? | Types & Examples

Published on July 5, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling.

To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing.

Table of contents

Types of probability sampling, examples of probability sampling methods, probability vs. non-probability sampling, advantages and disadvantages of probability sampling, other interesting articles, frequently asked questions about probability sampling.

There are four commonly used types of probability sampling designs:

Simple random sampling

• Stratified sampling

Systematic sampling

• Cluster sampling

Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. This is the most common way to select a random sample.

To compile a list of the units in your research population, consider using a random number generator. There are several free ones available online, such as random.org , calculator.net , and randomnumbergenerator.org .

Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them would be impractical and time-consuming, as well as questionable for ethical reasons. Instead, you decide to use a random number generator to draw a simple random sample.

Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population.

For example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population.

To split your population into different subgroups, first choose which characteristic you would like to divide them by. Then you can select your sample from each subgroup. You can do this in one of two ways:

• By selecting an equal number of units from each subgroup
• By selecting units from each subgroup equal to their proportion in the total population

If you take a simple random sample, children from urban areas will have a far greater chance of being selected, so the best way of getting a representative sample is to take a stratified sample.

First, you divide the population into your strata: one for children from urban areas and one for children from rural areas. Then, you take a simple random sample from each subgroup. You can use one of two options:

• Select 100 urban and 100 rural, i.e., an equal number of units
• Select 80 urban and 20 rural, which gives you a representative sample of 100 people

Systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point. This method is useful in situations where records of your target population already exist, such as records of an agency’s clients, enrollment lists of university students, or a company’s employment records. Any of these can be used as a sampling frame.

To start your systematic sample, you first need to divide your sampling frame into a number of segments, called intervals. You calculate these by dividing your population size by the desired sample size.

Then, from the first interval, you select one unit using simple random sampling. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval.

Let’s refer back to our example about the political views of the municipality of 4,000 inhabitants. You can also draw a sample of 100 people using systematic sampling. To do so, follow these steps:

• Determine your interval: 4,000 / 100 = 40. This means that you must select 1 inhabitant from every 40 in the record.
• Using simple random sampling (e.g., a random number generator), you select 1 inhabitant.
• Let’s say you select the 11th person on the list. In every subsequent interval, you need to select the 11th person in that interval, until you have a sample of 100 people.

Cluster sampling is the process of dividing the target population into groups, called clusters. A randomly selected subsection of these groups then forms your sample. Cluster sampling is an efficient approach when you want to study large, geographically dispersed populations. It usually involves existing groups that are similar to each other in some way (e.g., classes in a school).

There are two types of cluster sampling:

• Single (or one-stage) cluster sampling, when you divide the entire population into clusters
• Multistage cluster sampling, when you divide the cluster further into more clusters, in order to narrow down the sample size

Clusters are pre-existing groups, so each high school is a cluster, and you assign a number to each one of them. Then, you use simple random sampling to further select clusters. How many clusters you select will depend on the sample size that you need.

Multi-stage sampling is a more complex form of cluster sampling, in which smaller groups are successively selected from larger populations to form the sample population used in your study.

First, you take a simple random sample of departments. Then, again using simple random sampling, you select a number of units. Based on the size of the population (i.e., how many employees work at the company) and your desired sample size, you establish that you need to include 3 units in your sample.

In stratified sampling , you divide your population in groups (strata) that share a common characteristic and then select some members from every group for your sample. In cluster sampling , you use pre-existing groups to divide your population into clusters and then include all members from randomly selected clusters for your sample.

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There are a few methods you can use to draw a random sample. Here are a few examples:

• The fishbowl draw
• A random number generator
• The random number function

Fishbowl draw

You are investigating the use of a popular portable e‐reader device among library and information science students and its effects on individual reading practices. You write the names of 25 students on pieces of paper, put them in a jar, and then, without looking, randomly select three students to be interviewed for your research.

All students have equal chances of being selected and no other consideration (such as personal preference) can influence this selection. This method is suitable when your total population is small, so writing the names or numbers of each unit on a piece of paper is feasible.

Random number generator

Suppose you are researching what people think about road safety in a specific residential area. You make a list of all the suburbs and assign a number to each one of them. Then, using an online random number generator, you select four numbers, corresponding to four suburbs, and focus on them.

This works best when you already have a list with the total population and you can easily assign every individual a number.

RAND function in Microsoft Excel

If your data are in a spreadsheet, you can also use the random number function (RAND) in Microsoft Excel to select a random sample.

Suppose you have a list of 4,000 people and you need a sample of 300. By typing in the formula =RAND() and then pressing enter, you can have Excel assign a random number to each name on the list. For this to work, make sure there are no blank rows.

This video explains how to use the RAND function.

Depending on the goals of your research study, there are two sampling methods you can use:

• Probability sampling : Sampling method that ensures that each unit in the study population has an equal chance of being selected
• Non-probability sampling : Sampling method that uses a non-random sample from the population you want to research, based on specific criteria, such as convenience

Probability sampling

In quantitative research , it is important that your sample is representative of your target population. This allows you to make strong statistical inferences based on the collected data. Having a sufficiently large random probability sample is the best guarantee that the sample will be representative and the results are generalizable and free from research biases like selection bias and sampling bias .

Non-probability sampling

Non-probability sampling designs are used in both quantitative and qualitative research when the number of units in the population is either unknown or impossible to individually identify. It is also used when you want to apply the results only to a certain subsection or organization rather than the general public. Non-probability sampling is at higher risk than probability sampling for research biases like sampling bias .

You are unlikely to be able to compile a list of every practicing organizational psychologist in the country, but you could compile a list with all the experts in your area and select a few to interview.

It’s important to be aware of the advantages and disadvantages of probability sampling, as it will help you decide if this is the right sampling method for your research design.

Advantages of probability sampling

There are two main advantages to probability sampling.

• Samples selected with this method are representative of the population at large. Due to this, inferences drawn from such samples can be generalized to the total population you are studying.
• As some statistical tests, such as multiple linear regression , t test , or ANOVA , can only be applied to a sample size large enough to approximate the true distribution of the population, using probability sampling allows you to establish correlation or cause-and-effect relationship between your variables.

Disadvantages of probability sampling

Choosing probability sampling as your sampling method comes with some challenges, too. These include the following:

• It may be difficult to access a list of the entire population, due to ethical or privacy concerns, or a full list may not exist. It can be expensive and time-consuming to compile this yourself.
• Although probability sampling reduces the risk of sampling bias , it can still occur. When your selected sample is not inclusive enough, representation of the full population is skewed .

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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

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

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 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.

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Sampling Methods: Guide To All Types with Examples

Sampling is an essential part of any research project. The right sampling method can make or break the validity of your research, and it’s essential to choose the right method for your specific question. In this article, we’ll take a closer look at some of the most popular sampling methods and provide real-world examples of how they can be used to gather accurate and reliable data.

LEARN ABOUT:   Research Process Steps

From simple random sampling to complex stratified sampling , we’ll explore each method’s pros, cons, and best practices. So, whether you’re a seasoned researcher or just starting your journey, this article is a must-read for anyone looking to master sampling methods. Let’s get started!

Content Index

What is sampling?

Types of sampling: sampling methods, types of probability sampling with examples:, uses of probability sampling, types of non-probability sampling with examples, uses of non-probability sampling, how do you decide on the type of sampling to use, difference between probability sampling and non-probability sampling methods.

Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights.

It is also a time-convenient and cost-effective method and hence forms the basis of any research design . Sampling techniques can be used in research survey software for optimum derivation.

For example, suppose a drug manufacturer would like to research the adverse side effects of a drug on the country’s population. In that case, it is almost impossible to conduct a research study that involves everyone. In this case, the researcher decides on a sample of people from each demographic and then researches them, giving him/her indicative feedback on the drug’s behavior.

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Sampling in market action research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling.

• Probability sampling: Probability sampling is a sampling technique where a researcher selects a few criteria and chooses members of a population randomly. All the members have an equal opportunity to participate in the sample with this selection parameter.
• Non-probability sampling: In non-probability sampling, the researcher randomly chooses members for research. This sampling method is not a fixed or predefined selection process. This makes it difficult for all population elements to have equal opportunities to be included in a sample.

This blog discusses the various probability and non-probability sampling methods you can implement in any market research study.

LEARN ABOUT: Survey Sampling

Probability sampling is a technique in which researchers choose samples from a larger population based on the theory of probability. This sampling method considers every member of the population and forms samples based on a fixed process.

For example, in a population of 1000 members, every member will have a 1/1000 chance of being selected to be a part of a sample. Probability sampling eliminates sampling bias in the population and allows all members to be included in the sample.

There are four types of probability sampling techniques:

• Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources is the Simple Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample. For example, in an organization of 500 employees, if the HR team decides on conducting team-building activities, they would likely prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected.
• Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters representing a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inferences from the feedback. For example, suppose the United States government wishes to evaluate the number of immigrants living in the Mainland US. In that case, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data.
• Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, this sampling technique is the least time-consuming. For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).
• Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.

LEARN ABOUT: Purposive Sampling

There are multiple uses of probability sampling:

• Reduce Sample Bias: Using the probability sampling method, the research bias in the sample derived from a population is negligible to non-existent. The sample selection mainly depicts the researcher’s understanding and inference. Probability sampling leads to higher-quality data collection as the sample appropriately represents the population.
• Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed toward one demographic . For example, suppose Square would like to understand the people that could make their point-of-sale devices. In that case, a survey conducted from a sample of people across the US from different industries and socio-economic backgrounds helps.
• Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data.

Learn More: Data Collection Methods: Types & Examples

The non-probability method is a sampling method that involves a collection of feedback based on a researcher or statistician’s sample selection capabilities and not on a fixed selection process. In most situations, the output of a survey conducted with a non-probable sample leads to skewed results, which may not represent the desired target population. But, there are situations, such as the preliminary stages of research or cost constraints for conducting research, where non-probability sampling will be much more useful than the other type.

Four types of non-probability sampling explain the purpose of this sampling method in a better manner:

• Convenience sampling: This method depends on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling  because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations with resource limitations, such as the initial stages of research, convenience sampling is used. For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly.
• Judgmental or purposive sampling: Judgmental or purposive samples are formed at the researcher’s discretion. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample.
• Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, surveying shelterless people or illegal immigrants will be extremely challenging. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method when the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.
• Quota sampling:   In Quota sampling , members in this sampling technique selection happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.

Non-probability sampling is used for the following:

• Create a hypothesis: Researchers use the non-probability sampling method to create an assumption when limited to no prior information is available. This method helps with the immediate return of data and builds a base for further research.
• Exploratory research: Researchers use this sampling technique widely when conducting qualitative research, pilot studies, or exploratory research .
• Budget and time constraints: The non-probability method when there are budget and time constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is easier to pick respondents randomly and have them take the survey or questionnaire .

For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method.

• Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy.
• Identify the effective sampling techniques that might potentially achieve the research goals.
• Test each of these methods and examine whether they help achieve your goal.
• Select the method that works best for the research.

Unlock the power of accurate sampling!

We have looked at the different types of sampling methods above and their subtypes. To encapsulate the whole discussion, though, the significant differences between probability sampling methods and non-probability sampling methods are as below:

Now that we have learned how different sampling methods work and are widely used by researchers in market research so that they don’t need to research the entire population to collect actionable insights, let’s go over a tool that can help you manage these insights.

LEARN ABOUT: 12 Best Tools for Researchers

QuestionPro understands the need for an accurate, timely, and cost-effective method to select the proper sample; that’s why we bring QuestionPro Software, a set of tools that allow you to efficiently select your target audience , manage your insights in an organized, customizable repository and community management for post-survey feedback.

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Sampling Methods: A guide for researchers

Affiliation.

• 1 Arizona School of Dentistry & Oral Health A.T. Still University, Mesa, AZ, USA [email protected].
• PMID: 37553279

Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research question. Characteristics of individuals included in the sample population should be clearly defined to determine eligibility for study participation and improve power. Sample selection methods differ based on study design. The purpose of this short report is to review common sampling considerations and related errors.

Keywords: research design; sample size; sampling.

Copyright © 2023 The American Dental Hygienists’ Association.

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Sampling Techniques: Definition, Types, and Examples

Sampling is an inherent human trait we follow whenever we want to study something, but the domain is huge enough to force us to base our study on a sub-sample only. This is true for most of the studies in the practical world. The whole population is never accessible; even if it is, it’s not worth going through all of it.

Why Do We Need Sampling Techniques?

Research benefits greatly from sampling. It is one of the most crucial elements that affect how accurate your study or survey results are. If your sample contains any errors, the outcome will be affected accordingly. Depending on the situation and necessity, numerous methodologies aid in sample collection. But before diving into the topic, let’s look at some essential statistical terms you might need to remember.

A population is a group of related objects or occurrences relevant to a particular topic or experiment.

It is the particular group from whom you will get data. The sample size is always smaller than the population as a whole.

It is the object or person being observed.

It is a list of everything in the population that can be observed, whether it be people or other objects.

What is a Sampling Technique?

It is seldom possible to gather data from every member of a group of individuals when conducting research on them. So, what do you do? Well, you pick a sample instead. The population that will actually take part in the study is the sample.

What are the different types of Sampling Techniques?

1.     probability sampling, 2.     non-probability sampling techniques.

Let’s go through both, along with their sub-types.

Probability Sampling Techniques

Using a set of predetermined criteria and a random selection of population members, a researcher uses the sampling technique known as probability sampling.  With this selection criteria, each member has an equal chance of being included in the sample. Our best shot at producing a sample that is accurately representative of the population and enables us to draw robust statistical conclusions about the entire group is through probability sampling. Random sampling is another name for it. It has four sub-divisions:

1.     Simple Random Sampling Technique:

The fact that this method is the most straightforward for probability sampling is a significant benefit. It does, however, come with a disclaimer: it might not choose enough people who fit our criteria. We use it when we don’t know anything about the target population beforehand.

A company has decided to give a bonus to 10 of its employees. These employees will be selected randomly through any method from the whole company.

2.     Systematic Sampling Technique:

In  systematic sampling , the first person is chosen randomly, and the others are selected according to a predetermined sampling interval.  Put each person, in the population, in some kind of order and select every nth member to be in the sample from a random starting point.

Suppose you need to choose a sample of 50 people from a population of 100. You will select every 2nd person on the list.

3.     Stratified Sampling Technique:

A researcher wants to know the number of people in a country who went to college. He\she would divide the country into cities and then further divide the cities into age groups. He\she would then randomly select a sample to get information about the topic.

4.     Cluster Sampling Technique:

The sample has a higher chance of mistakes because there may be significant differences between clusters, but it is pretty helpful for handling oversized and dispersed populations. It is challenging to ensure that the sampled clusters accurately reflect the entire population.

A mobile company is looking to survey people from a country about the usage of phones. It would divide the country into cities, known as clusters, and then further divide the cities into areas (clusters) that are more populated.

Non-probability Sampling Techniques

1.     convenience sampling technique:.

Although it is quick and affordable, this method cannot yield generalizable conclusions because it is impossible to determine whether the sample reflects the population. Considering how simple it was for the researcher to conduct the study and contact the subjects, it is frequently referred to as convenience sampling. Researchers with almost no authority choose the sample components, and they are selected entirely based on accessibility rather than representativeness.

When gathering feedback is time and money-constrained, this non-probability sampling technique is used.

2.     Purposive Sampling Technique:

In the  purposive sampling technique , the researcher uses their knowledge to choose a sample that will be most helpful to the research’s objectives.  This sort of sampling is also known as selective or judgment sampling. It is frequently employed when the researcher prefers to learn in-depth information on a particular occurrence versus drawing general conclusions from statistics or when the population is relatively tiny and focused. 

3.     Snowball Sampling Technique:

When subjects are challenging to trace, researchers use the  snowball sampling technique .  To discover people who are interested in participating in the study, the researcher contacts other people they know. Using the snowball theory, researchers can follow a few categories to interview and gather data in situations where it is challenging to survey people on a particular topic. This sampling strategy is also used by researchers when the  subject is highly delicate and taboo . The population expands like a snowball as a result of this referral strategy. This sampling technique works well when it’s challenging to pinpoint a sampling frame.

Snowball sampling carries a considerable risk of selection bias because the people who are referred will have characteristics in common with the person who refers them.

For example, if a researcher is conducting a study about the psychological effects of STDs, the snowball sampling technique would be useful as STDs are considered taboo in most areas.

4.     Quota Sampling Technique:

This approach divides the sample into groups based on traits and then interviews. The sample should reflect the population regarding the proportion of traits and attributes. The researcher stops collecting data once each group has adequate sample units. This sampling technique has numerous benefits, including its ability to compare groups within the population, quick and uncomplicated execution, and lack of need for a sample frame. The division of the groups may not be correct, and there is a possibility of some bias.

Sampling is a very extensive yet one of the most undermined areas in research and general statistical studies. People don’t realize how important it is to choose suitable sampling methods to achieve the correct results. Most use the same one or two generic approaches, regardless of their use case, resulting in improper results.

So, to achieve your research objectives properly, selecting a sampling technique   carefully while taking everything into consideration is crucial. In order to help you make your decision on a broader category, I’ve made up a table to help you choose the right approach for you, highlighting the major  differences between probability vs. non-probability sampling techniques .

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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Sampling Methods – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 25, 2023

If you are performing research on a large community, organisation, or country, then it may not be possible to collect data individually from each participant. To deal with this issue, you can use a group of a specific number of participants, and this group is referred to as a sample .

The method you apply for selecting your participants is known as the  sampling method . It helps in concluding the entire population based on the  outcomes of the research .

Example: If you want to research China’s entire population, it isn’t easy to gather information from 1.38 billion people. You can use a sampling method by conducting your research on a specific number of participants and drawing a conclusion about the entire population based on your study’s outcomes.

Uses of Sampling Method

The sampling method is used to:

• Gather data from a large group of population.
• Counter check on data collection.
• Speed up tabulation and publication of results.
• Increase the efficiency of the research.
• Conduct experimental research
• Obtain data for researches on population census.

What is the Difference between Population and Sample? 

Before starting with the sampling methods, it is important to understand the difference between sample and population.

It is a group selected from the target population when you aim to study a large population. This group is considered as the representative of the overall targeted population.

Example: Sample of 20 female cricketers.

If you add the set of individuals with specific characteristics according to the research requirements, the resulting group is called the population. 

Example: The income of government teachers in India

Sampling Frame Vs. Sampling Size

Sampling frame.

A list of all the elements from a population is known as the sampling frame.

For instance, you are selecting a telephone directory of students or a list of social media users.

This information can be gathered by contacting any commercial organisation. Sometimes some errors are also possible in the sampling frame due to its discrepancy in selecting samples.

Sampling Size

It is considered a subset of the population as it is selected to make the inference to the original population of a study. The chances of accuracy are depended on the size of the population. The larger the size, the more accurate the study is.

When it comes to census, the sample size is the same or parallel with the population size. But to maintain the budget and to consider the time frame, only a representative class is selected.

Methods of Sampling

There are usually two methods of sampling which are used widely. These are considered the best methods:

• Probability Method
• Non-Probability Method

Probability Method 

This method of sampling is conducted by using the method of randomisation. In this method, each individual has an equal and independent opportunity to be selected.  It has further sub-categories.

• Simple Random Methods
• Stratified Methods
• Systematic Method
• Cluster Method
• Multi-Stage

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Simple Random Method

The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling. 

Example: You want to identify how much time people spend on social media. You need to randomly select the participants and assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to select the participants randomly and assign a balanced diet.

Systematic Sampling Method

In this type of sampling, method participants are selected according to the fixed period interval and starting point. The fixed period interval can be calculated by dividing the sample size by the respective population size. 

Example: Framingham study , which includes selecting every second person from a list of two residents.

Stratified Method

Stratified sampling is a random selection of the participants by dividing them into strata and randomly selecting the participants from each level.

Example: You want to identify how much time people spend on social media. You need to divide the participants into groups based on their age and then assign a specific number of hours to spend on social media.

Example: You want to find out the benefits of a balanced diet. You need to divide participants into various groups based on their age, gender, and health conditions and assigned them to each group’s treatment group.

Matching Method

Even though if participants are selected randomly, they can be assigned to the various groups of comparison. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.

Cluster Sampling

It is a kind of sampling where the population is converted into sub-groups called clusters. These sub-groups or clusters are then selected randomly as a sample. The selected group should have all the characteristics of other groups. 

Example: You want to check high school students’ communication skills, and there are more than 50 schools in the city. You can’t visit each school to gather information. In such situations, you can select any five schools, and these schools will be your clusters.

Non-probability Sampling

Non-probability sampling techniques are often appropriate for exploratory and  qualitative research . This type of sample is not to test a  hypothesis  about a broad population but to develop an initial understanding of a small or under-researched population.

This type of sampling is different from probability, as its criteria are unique. The samples are not selected randomly; rather, these samples are selected according to the researcher’s ability. This might result in a biased result, and participants may find it difficult to be a part of the sample. Still, this is a prevalent method. It has the following types:

• Purposive type sampling
• Referral sampling

Convenience Sampling

Quota sampling.

Reading material: ResearchProspect has also published a very detailed guide about inductive and deductive reasoning for students.

Purposive Sampling

This type of sampling is based on the aims of the research. Therefore, only such elements of the population will be selected, which are according to the research’s purpose.

Example: You want to find out the opinion of people about jobs and businesses. You can select a few participants interested in doing 9-5 jobs and a few interested in doing business.

Referral Sampling

This type of sampling is used where the population is not defined or rare. In this technique, one participant is selected according to defined criteria. After that, the same selected participant is asked to refer to other samples fulfilling the study’s criteria. In this way, it goes enlarging its size with the help of the referral. 

Example: You can use it while conducting a study on the victims of physical harassment at workplaces. No matter how smoothly you approach them, not all women respond openly to your questions as they feel uncomfortable, or they get afraid of being humiliated. You can select the people from these victims’ circles (ex: their colleagues, friends, relatives) to get in touch with them and gather the required information for your research.

This type of sampling is applied according to availability. If the samples are not available easily, and the research is getting costly, this technique is applied to select the samples as per convenience. 

Example: You want to research the election campaigns. In this situation, you need to gather information from the available candidates (political leaders, media persons, voters) whenever and wherever you get any chance to meet them; otherwise, you will need to wait for the next election campaign.

This type of sampling is done when some standards are already adjusted. In this sampling, the representatives are selected from the population. This selected sample should resemble all the characteristics traits of the population. The size of the sample should reflect the. The participants are selected until sufficient information is gathered. 

Example: You want to identify and compare the high school’s academic performance, and you are allowed to select only 100 participants as per the standards of your study. You can select 25 students of the ninth standard, 25 students of the tenth standard, 25 students of the eleventh standard, and 25 students of the twelfth standard.

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Advantages of Sampling Method

Sampling has many advantages, such as:

• It saves a lot of time, as contacting the entire population would be difficult and time-consuming.
• It’s cost-effective.
• It has greater scope and adaptability.
• It provides accurate results.
• It can be managed easily.

Disadvantages of Sampling Method

• It may cause a feeling of discrimination among the participants who are not selected for the study.
• The researcher needs to be skilled, experienced, and qualified to ensure efficient sampling.
• It requires a lot of time, and results may not be reliable.

Frequently Asked Questions

What is sampling and its types.

Sampling is the process of selecting a subset of individuals or items from a larger population to gather data. Types include:

• Random Sampling: Each member has an equal chance.
• Stratified Sampling: Divides population into groups for proportional representation.
• Systematic Sampling: Every nth member is chosen.
• Cluster Sampling: Population is divided into clusters; random clusters are selected.
• Convenience Sampling: Convenient individuals are chosen.
• Snowball Sampling: Existing subjects refer new ones.

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Sampling methods, types & techniques.

15 min read Find out how sampling works, when to use it and how to select a representative sample for your research survey.

What is sampling?

In survey research, sampling is the process of using a subset of a population to represent the whole population. To help illustrate this further, let’s look at data sampling methods with examples below.

Let’s say you wanted to do some research on everyone in North America. To ask every person would be almost impossible. Even if everyone said “yes”, carrying out a  survey  across different states, in different languages and timezones, and then collecting and processing all the results, would take a long time and be very costly.

Sampling allows large-scale research to be carried out with a more realistic cost and time-frame because it uses a smaller number of individuals in the population with representative characteristics to stand in for the whole.

However, when you decide to sample, you take on a new task. You have to decide who is part of your sample list and how to choose the people who will best represent the whole population. How you go about that is what the practice of sampling is all about.

Sampling definitions

• Population:  The total number of people or things you are interested in
• Sample:  A smaller number within your population that will represent the whole
• Sampling:  The process and method of selecting your sample

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Why is sampling important?

Although the idea of sampling is easiest to understand when you think about a very large population, it makes sense to use sampling methods in research studies of all types and sizes. After all, if you can reduce the effort and cost of doing a study, why wouldn’t you? And because sampling allows you to research larger target populations using the same resources as you would smaller ones, it dramatically opens up the possibilities for research.

Sampling is a little like having gears on a car or bicycle. Instead of always turning a set of wheels of a specific size and being constrained by their physical properties, it allows you to translate your effort to the wheels via the different gears, so you’re effectively choosing bigger or smaller wheels depending on the terrain you’re on and how much work you’re able to do.

Sampling allows you to “gear” your research so you’re less limited by the constraints of cost, time, and complexity that come with different population sizes.

It allows us to do things like carrying out exit polls during elections, map the spread and effects rates of epidemics across geographical areas, and carry out nationwide census research that provides a snapshot of society and culture.

Types of sampling

Sampling strategies in research vary widely across different disciplines and research areas, and from study to study.

There are two major types of sampling methods: probability and non-probability sampling.

• Probability sampling , also known as  random sampling , is a kind of sample selection where randomisation is used instead of deliberate choice. Each member of the population has a known, non-zero chance of being selected.
• Non-probability sampling  techniques are where the researcher deliberately picks items or individuals for the sample based on non-random factors such as convenience, geographic availability, or costs.

As we delve into these categories, it’s essential to understand the nuances and applications of each method to ensure that the chosen sampling strategy aligns with the research goals.

Probability sampling methods

There’s a wide range of probability sampling methods to explore and consider. Here are some of the best-known options.

1. Simple random sampling

With  simple random sampling , every element in the population has an equal chance of being selected as part of the sample. It’s something like picking a name out of a hat. Simple random sampling can be done by anonymising the population – e.g. by assigning each item or person in the population a number and then picking numbers at random.

Pros:  Simple random sampling is easy to do and cheap. Designed to ensure that every member of the population has an equal chance of being selected, it reduces the risk of bias compared to non-random sampling.

Cons:  It offers no control for the researcher and may lead to unrepresentative groupings being picked by chance.

2. Systematic sampling

With  systematic sampling  the random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked.

Best practice is to sort your list in a random way to ensure that selections won’t be accidentally clustered together. This is commonly achieved using a random number generator. If that’s not available you might order your list alphabetically by first name and then pick every fifth name to eliminate bias, for example.

Next, you need to decide your sampling interval – for example, if your sample will be 10% of your full list, your sampling interval is one in 10 – and pick a random start between one and 10 – for example three. This means you would start with person number three on your list and pick every tenth person.

Pros:  Systematic sampling is efficient and straightforward, especially when dealing with populations that have a clear order. It ensures a uniform selection across the population.

Cons:  There’s a potential risk of introducing bias if there’s an unrecognized pattern in the population that aligns with the sampling interval.

3. Stratified sampling

Stratified sampling  involves random selection within predefined groups. It’s a useful method for researchers wanting to determine what aspects of a sample are highly correlated with what’s being measured. They can then decide how to subdivide (stratify) it in a way that makes sense for the research.

For example, you want to measure the height of students at a university where 80% of students are female and 20% are male. We know that gender is highly correlated with height, and if we took a simple random sample of 200 students (out of the 2,000 who attend the university), we could by chance get 200 females and not one male. This would bias our results and we would underestimate the height of students overall. Instead, we could stratify by gender and make sure that 20% of our sample (40 students) are male and 80% (160 students) are female.

Pros:  Stratified sampling enhances the representation of all identified subgroups within a population, leading to more accurate results in heterogeneous populations.

Cons:  This method requires accurate knowledge about the population’s stratification, and its design and execution can be more intricate than other methods.

4. Cluster sampling

With cluster sampling, groups rather than individual units of the target population are selected at random for the sample. These might be pre-existing groups, such as people in certain zip codes or students belonging to an academic year.

Cluster sampling can be done by selecting the entire cluster, or in the case of two-stage cluster sampling, by randomly selecting the cluster itself, then selecting at random again within the cluster.

Pros:  Cluster sampling is economically beneficial and logistically easier when dealing with vast and geographically dispersed populations.

Cons:  Due to potential similarities within clusters, this method can introduce a greater sampling error compared to other methods.

Non-probability sampling methods

The  non-probability sampling  methodology doesn’t offer the same bias-removal benefits as probability sampling, but there are times when these types of sampling are chosen for expediency or simplicity. Here are some forms of non-probability sampling and how they work.

1. Convenience sampling

People or elements in a sample are selected on the basis of their accessibility and availability. If you are doing a research survey and you work at a university, for example, a convenience sample might consist of students or co-workers who happen to be on campus with open schedules who are willing to take your  questionnaire .

This kind of sample can have value, especially if it’s done as an early or preliminary step, but significant bias will be introduced.

Pros:  Convenience sampling is the most straightforward method, requiring minimal planning, making it quick to implement.

Cons:  Due to its non-random nature, the method is highly susceptible to biases, and the results are often lacking in their application to the real world.

2. Quota sampling

Like the probability-based stratified sampling method, this approach aims to achieve a spread across the target population by specifying who should be recruited for a survey according to certain groups or criteria.

For example, your quota might include a certain number of males and a certain number of females. Alternatively, you might want your samples to be at a specific income level or in certain age brackets or ethnic groups.

Pros:  Quota sampling ensures certain subgroups are adequately represented, making it great for when random sampling isn’t feasible but representation is necessary.

Cons:  The selection within each quota is non-random and researchers’ discretion can influence the representation, which both strongly increase the risk of bias.

3. Purposive sampling

Participants for the sample are chosen consciously by researchers based on their knowledge and understanding of the research question at hand or their goals.

Also known as judgment sampling, this technique is unlikely to result in a  representative sample , but it is a quick and fairly easy way to get a range of results or responses.

Pros:  Purposive sampling targets specific criteria or characteristics, making it ideal for studies that require specialised participants or specific conditions.

Cons:  It’s highly subjective and based on researchers’ judgment, which can introduce biases and limit the study’s real-world application.

4. Snowball or referral sampling

With this approach, people recruited to be part of a sample are asked to invite those they know to take part, who are then asked to invite their friends and family and so on. The participation radiates through a community of connected individuals like a snowball rolling downhill.

Pros:  Especially useful for hard-to-reach or secretive populations, snowball sampling is effective for certain niche studies.

Cons:  The method can introduce bias due to the reliance on participant referrals, and the choice of initial seeds can significantly influence the final sample.

What type of sampling should I use?

Choosing the right sampling method is a pivotal aspect of any research process, but it can be a stumbling block for many.

Here’s a structured approach to guide your decision.

1) Define your research goals

If you aim to get a general sense of a larger group, simple random or stratified sampling could be your best bet. For focused insights or studying unique communities, snowball or purposive sampling might be more suitable.

2) Assess the nature of your population

The nature of the group you’re studying can guide your method. For a diverse group with different categories, stratified sampling can ensure all segments are covered. If they’re widely spread geographically, cluster sampling becomes useful. If they’re arranged in a certain sequence or order, systematic sampling might be effective.

3) Consider your constraints

Your available time, budget and ease of accessing participants matter. Convenience or quota sampling can be practical for quicker studies, but they come with some trade-offs. If reaching everyone in your desired group is challenging, snowball or purposive sampling can be more feasible.

4) Determine the reach of your findings

Decide if you want your findings to represent a much broader group. For a wider representation, methods that include everyone fairly (like  probability sampling ) are a good option. For specialised insights into specific groups, non-probability sampling methods can be more suitable.

5) Get feedback

Before fully committing, discuss your chosen method with others in your field and consider a test run.

Avoid or reduce sampling errors and bias

Using a sample is a kind of short-cut. If you could ask every single person in a population to take part in your study and have each of them reply, you’d have a highly accurate (and very labor-intensive) project on your hands.

But since that’s not realistic, sampling offers a “good-enough” solution that sacrifices some accuracy for the sake of practicality and ease. How much accuracy you lose out on depends on how well you control for sampling error , non-sampling error, and bias in your survey design. Our blog post helps you to steer clear of some of these issues.

How to choose the correct sample size

Finding the best sample size for your target population is something you’ll need to do again and again, as it’s different for every study.

To make life easier, we’ve provided a  sample size calculator . To use it, you need to know your:

• Population size
• Confidence level
• Margin of error  (confidence interval)

If any of those terms are unfamiliar, have a look at our blog post on  determining sample size  for details of what they mean and how to find them.

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Related resources

Sampling and non-sampling errors 10 min read, determining sample size 12 min read, selection bias: how to avoid errors in research 11 min read, systematic random sampling 12 min read, convenience sampling 18 min read, non-probability sampling 17 min read, simple random sampling 9 min read, request demo.

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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• What is the Importance of a Concept Paper and How to Write It 

APA format: Basic Guide for Researchers

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Experimental Research

Ai generator.

Humans are born curious. As babies, we quench our questions by navigating our surroundings with our available senses. Our fascination with the unknown lingers to adulthood that some of us build a career out of trying to discover the mysteries of the universe. To learn about a point of interest, one of the things we do is isolate and replicate the phenomenon in laboratories and controlled environment. Experimental research is a causal investigation into the cause-effect relationships by manipulating all other factors.  You may also see Student Research examples & samples

Experimental research is generally a quantitative research centered on validating or refuting certain claims on causative relationships of matter. We use this method in natural, applied, theoretical, and social sciences, to name a few. This research follows a scientific design that puts a weight on replicability. When the methodology and results are replicable, the study is verifiable by reviewers and critics.

Notable Experiments

We have been conducting experiments for the longest time. Experimental studies done some thousand of years ago prove that unrefined apparatus and limited knowledge, we were already trying to answer the questions of the universe. We had to start somewhere.

Anatomical Anomaly

Even before, societal beliefs have restricted scientific development. This is especially true for modern medicine. Back then, studying and opening cadavers is a punishable crime. Therefore, physicians based their knowledge on the human body on animal dissections. Because animals have a different body organization than humans, this limited what we knew about ourselves. It took actual studies and experiments on the human body to curtail the misinformation and improve medical knowledge.

Reviewing Resemblance

A garden of fuschia and peas helped change our understanding of heredity and inheritable traits. Mendel was curious about why the fuschia plants generate the colors of the flowers the way they do. He crossed varieties of the plant and obtained consistent results. He also tried to cross pea plants and came with repeatable results. The characteristics of the parent plants are passed down to their offsprings to a certain degree of similarity. He also figured out the predictability of certain traits to appear in the offspring. Mendelian genetics explain the laws of inheritance that are still relevant today.

Canine Conditioning 

In the history of psychology research, one experiment will always ring a bell. Pavlov conditioned a dog to expect food when a bell was rung. After repetitions of this approach, the dog started to salivate at the sound of the bell, even when Pavlov didn’t introduce the food. His work on training the reflexes and the mind is in line with the plasticity of the brain to learn and unlearn relationships based on stimuli.

Correlation Vs. Causation

We can opt for an experimental approach to research when we want to determine if the hypothesized cause follows the expected effect. We do this by following a scientific research method and design that emphasizes the replicability of results to limit and reduce biases. By isolating the variables and manipulating treatments, we can establish causation. This is important if we are to find out the relationship between A and B.

In our experiments, we will encounter two or more phenomena, and we might mislabel their connection. There are instances where that relationship is both correlative and causative. What we need to remember is that correlation is not causation. We can say that A causes B when event B is an explicit product of and entirely dependent on event A. Events A and B are correlated when they appear together, but after experimentation, A doesn’t necessarily result in B.

However, it is not enough to say A caused B. Our results are still subject to statistical treatment to determine the validity of the findings and the degree of causation. We still have to ask how much A influences B. Only then can we accept or reject our hypothesis .

Experimental Research Value

Experimental research is a trial-and-error with an educated basis. It lets us determine what works and what doesn’t, and the underlying relationship. In our daily life, we are engaging in pseudo experiments. While cooking, for instance, you taste the dish before you decide to pour additional seasoning. You test first if the food is fine without additives.

In some fields of science, the results of an experiment can be used to generalized a relationship as true for similar, if not all, cases. Experimental research papers make way for the formation of theories. When those theories become unrefuted for a long time, they can become laws that explain universal phenomena.

10+ Experimental Research Examples

Go over the following examples of experimental research papers . They may be able to help you gain a head start in your study or uproot you from where you’re stuck in your experiment.

1. Experimental Research Design Example

Size: 465 KB

2. Experimental Data Quality Research Example

Size: 318 KB

3. Experimental Research on Labor Market Discrimination

Size: 722 KB

4. Experimental Studies Research Example

Size: 230 KB

5. Short Description of Experimental Research Example

Size: 280 KB

6. Sample Experimental Design Research Example

Size: 109 KB

7. Experimental Research on Democracy Example

Size: 86 KB

8. Standards for Experimental Research Example

Size: 141 KB

9. Experimental Research for Evaluation Example

Size: 87 KB

10. Defense Experimental Research Example

Size: 315 KB

11. Formal Experoimental Research in DOC

How To Start Your Experiment

The best scientists and researchers started with the basics, too. Here are reminders on how you could improve your research writing skills. Who knows, one day, you will join the ranks of world changers with your experimental research report

1. Identify the Problem

To solve a problem, you need to define what it is first. You can begin with identifying the field of research you wish to investigate, then find gaps in knowledge from the related literature. An original work on a timely and relevant issue will help with the approval of your research proposal . After you have read scholarly articles about the topic, you can start narrowing the focus of your research into a specific topic.

2. Design the Experiment

Create a research plan for your intended research with the following notes. The experimental research design ideally employs a probabilistic sampling method to avoid biases from influencing the validity of your work. However, certain experiments call for non-probabilistic sampling techniques. Your experiment should have a control group with ambient conditions or blank treatments. This set up helps you objectively quantify the relationship between A and B.

3. Test the Hypothesis

In performing your experiment, you should have a variable that you would manipulate. The effect of the manipulation will be reflected in the dependent variable. By manipulating the factors that would cause event B, you can determine if A does, in fact, cause B. You can input the raw data into statistical analysis software and tools to see if you can derive a valid conclusion on the relationship between A and B. Correlation or causation and their degree can also be determined by different statistical tests.

4. Publish the Findings

After you have gone through all the efforts in conducting your research, the next step is communicating the findings to the academic community and the public, especially if public and government entities funded the study. You do this by submitting your paper to journals and academic conferences. For what use is the new knowledge you have worked for if you keep the results to yourself?

Experimental research separates science from fiction. Despite criticisms that this method exists in an ideal world, removed from reality, we cannot downsize its merits in the search for knowledge. Because the results are observable, replicable, and appreciable in a real-world sense, this research type will always have room in the development of scientific knowledge and the improvement of man. For as long as man is curious, science will keep growing.

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Sampling: how to select participants in my research study? *

Jeovany martínez-mesa.

1 Faculdade Meridional (IMED) - Passo Fundo (RS), Brazil.

David Alejandro González-Chica

2 University of Adelaide - Adelaide, Australia.

Rodrigo Pereira Duquia

3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

Renan Rangel Bonamigo

João luiz bastos.

4 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (RS), Brazil.

In this paper, the basic elements related to the selection of participants for a health research are discussed. Sample representativeness, sample frame, types of sampling, as well as the impact that non-respondents may have on results of a study are described. The whole discussion is supported by practical examples to facilitate the reader's understanding.

To introduce readers to issues related to sampling.

INTRODUCTION

The essential topics related to the selection of participants for a health research are: 1) whether to work with samples or include the whole reference population in the study (census); 2) the sample basis; 3) the sampling process and 4) the potential effects nonrespondents might have on study results. We will refer to each of these aspects with theoretical and practical examples for better understanding in the sections that follow.

TO SAMPLE OR NOT TO SAMPLE

In a previous paper, we discussed the necessary parameters on which to estimate the sample size. 1 We define sample as a finite part or subset of participants drawn from the target population. In turn, the target population corresponds to the entire set of subjects whose characteristics are of interest to the research team. Based on results obtained from a sample, researchers may draw their conclusions about the target population with a certain level of confidence, following a process called statistical inference. When the sample contains fewer individuals than the minimum necessary, but the representativeness is preserved, statistical inference may be compromised in terms of precision (prevalence studies) and/or statistical power to detect the associations of interest. 1 On the other hand, samples without representativeness may not be a reliable source to draw conclusions about the reference population (i.e., statistical inference is not deemed possible), even if the sample size reaches the required number of participants. Lack of representativeness can occur as a result of flawed selection procedures (sampling bias) or when the probability of refusal/non-participation in the study is related to the object of research (nonresponse bias). 1 , 2

Although most studies are performed using samples, whether or not they represent any target population, census-based estimates should be preferred whenever possible. 3 , 4 For instance, if all cases of melanoma are available on a national or regional database, and information on the potential risk factors are also available, it would be preferable to conduct a census instead of investigating a sample.

However, there are several theoretical and practical reasons that prevent us from carrying out census-based surveys, including:

• Ethical issues: it is unethical to include a greater number of individuals than that effectively required;
• Budgetary limitations: the high costs of a census survey often limits its use as a strategy to select participants for a study;
• Logistics: censuses often impose great challenges in terms of required staff, equipment, etc. to conduct the study;
• Time restrictions: the amount of time needed to plan and conduct a census-based survey may be excessive; and,
• Unknown target population size: if the study objective is to investigate the presence of premalignant skin lesions in illicit drugs users, lack of information on all existing users makes it impossible to conduct a census-based study.

All these reasons explain why samples are more frequently used. However, researchers must be aware that sample results can be affected by the random error (or sampling error). 3 To exemplify this concept, we will consider a research study aiming to estimate the prevalence of premalignant skin lesions (outcome) among individuals >18 years residing in a specific city (target population). The city has a total population of 4,000 adults, but the investigator decided to collect data on a representative sample of 400 participants, detecting an 8% prevalence of premalignant skin lesions. A week later, the researcher selects another sample of 400 participants from the same target population to confirm the results, but this time observes a 12% prevalence of premalignant skin lesions. Based on these findings, is it possible to assume that the prevalence of lesions increased from the first to the second week? The answer is probably not. Each time we select a new sample, it is very likely to obtain a different result. These fluctuations are attributed to the "random error." They occur because individuals composing different samples are not the same, even though they were selected from the same target population. Therefore, the parameters of interest may vary randomly from one sample to another. Despite this fluctuation, if it were possible to obtain 100 different samples of the same population, approximately 95 of them would provide prevalence estimates very close to the real estimate in the target population - the value that we would observe if we investigated all the 4,000 adults residing in the city. Thus, during the sample size estimation the investigator must specify in advance the highest or maximum acceptable random error value in the study. Most population-based studies use a random error ranging from 2 to 5 percentage points. Nevertheless, the researcher should be aware that the smaller the random error considered in the study, the larger the required sample size. 1

SAMPLE FRAME

The sample frame is the group of individuals that can be selected from the target population given the sampling process used in the study. For example, to identify cases of cutaneous melanoma the researcher may consider to utilize as sample frame the national cancer registry system or the anatomopathological records of skin biopsies. Given that the sample may represent only a portion of the target population, the researcher needs to examine carefully whether the selected sample frame fits the study objectives or hypotheses, and especially if there are strategies to overcome the sample frame limitations (see Chart 1 for examples and possible limitations).

Examples of sample frames and potential limitations as regards representativeness

Sampling can be defined as the process through which individuals or sampling units are selected from the sample frame. The sampling strategy needs to be specified in advance, given that the sampling method may affect the sample size estimation. 1 , 5 Without a rigorous sampling plan the estimates derived from the study may be biased (selection bias). 3

TYPES OF SAMPLING

In figure 1 , we depict a summary of the main sampling types. There are two major sampling types: probabilistic and nonprobabilistic.

Sampling types used in scientific studies

NONPROBABILISTIC SAMPLING

In the context of nonprobabilistic sampling, the likelihood of selecting some individuals from the target population is null. This type of sampling does not render a representative sample; therefore, the observed results are usually not generalizable to the target population. Still, unrepresentative samples may be useful for some specific research objectives, and may help answer particular research questions, as well as contribute to the generation of new hypotheses. 4 The different types of nonprobabilistic sampling are detailed below.

Convenience sampling : the participants are consecutively selected in order of apperance according to their convenient accessibility (also known as consecutive sampling). The sampling process comes to an end when the total amount of participants (sample saturation) and/or the time limit (time saturation) are reached. Randomized clinical trials are usually based on convenience sampling. After sampling, participants are usually randomly allocated to the intervention or control group (randomization). 3 Although randomization is a probabilistic process to obtain two comparable groups (treatment and control), the samples used in these studies are generally not representative of the target population.

Purposive sampling: this is used when a diverse sample is necessary or the opinion of experts in a particular field is the topic of interest. This technique was used in the study by Roubille et al, in which recommendations for the treatment of comorbidities in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis were made based on the opinion of a group of experts. 6

Quota sampling: according to this sampling technique, the population is first classified by characteristics such as gender, age, etc. Subsequently, sampling units are selected to complete each quota. For example, in the study by Larkin et al., the combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally-advanced melanoma, stage IIIC or IV, with BRAF mutation. 7 The study recruited 495 patients from 135 health centers located in several countries. In this type of study, each center has a "quota" of patients.

"Snowball" sampling : in this case, the researcher selects an initial group of individuals. Then, these participants indicate other potential members with similar characteristics to take part in the study. This is frequently used in studies investigating special populations, for example, those including illicit drugs users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana. 8

PROBABILISTIC SAMPLING

In the context of probabilistic sampling, all units of the target population have a nonzero probability to take part in the study. If all participants are equally likely to be selected in the study, equiprobabilistic sampling is being used, and the odds of being selected by the research team may be expressed by the formula: P=1/N, where P equals the probability of taking part in the study and N corresponds to the size of the target population. The main types of probabilistic sampling are described below.

Simple random sampling: in this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers. An example is the study by Pimenta et al, in which the authors obtained a listing from the Health Department of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants. 9

Systematic random sampling: in this case, participants are selected from fixed intervals previously defined from a ranked list of participants. For example, in the study of Kelbore et al, children who were assisted at the Pediatric Dermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order. 10

Stratified sampling: in this type of sampling, the target population is first divided into separate strata. Then, samples are selected within each stratum, either through simple or systematic sampling. The total number of individuals to be selected in each stratum can be fixed or proportional to the size of each stratum. Each individual may be equally likely to be selected to participate in the study. However, the fixed method usually involves the use of sampling weights in the statistical analysis (inverse of the probability of selection or 1/P). An example is the study conducted in South Australia to investigate factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated. 11

Cluster sampling: in this type of probabilistic sampling, groups such as health facilities, schools, etc., are sampled. In the above-mentioned study, the selection of households is an example of cluster sampling. 11

Complex or multi-stage sampling: This probabilistic sampling method combines different strategies in the selection of the sample units. An example is the study of Duquia et al. to assess the prevalence and factors associated with the use of sunscreen in adults. The sampling process included two stages. 12 Using the 2000 Brazilian demographic census as sampling frame, all 404 census tracts from Pelotas (Southern Brazil) were listed in ascending order of family income. A sample of 120 tracts were systematically selected (first sampling stage units). In the second stage, 12 households in each of these census tract (second sampling stage units) were systematically drawn. All adult residents in these households were included in the study (third sampling stage units). All these stages have to be considered in the statistical analysis to provide correct estimates.

NONRESPONDENTS

Frequently, sample sizes are increased by 10% to compensate for potential nonresponses (refusals/losses). 1 Let us imagine that in a study to assess the prevalence of premalignant skin lesions there is a higher percentage of nonrespondents among men (10%) than among women (1%). If the highest percentage of nonresponse occurs because these men are not at home during the scheduled visits, and these participants are more likely to be exposed to the sun, the number of skin lesions will be underestimated. For this reason, it is strongly recommended to collect and describe some basic characteristics of nonrespondents (sex, age, etc.) so they can be compared to the respondents to evaluate whether the results may have been affected by this systematic error.

Often, in study protocols, refusal to participate or sign the informed consent is considered an "exclusion criteria". However, this is not correct, as these individuals are eligible for the study and need to be reported as "nonrespondents".

SAMPLING METHOD ACCORDING TO THE TYPE OF STUDY

In general, clinical trials aim to obtain a homogeneous sample which is not necessarily representative of any target population. Clinical trials often recruit those participants who are most likely to benefit from the intervention. 3 Thus, the more strict criteria for inclusion and exclusion of subjects in clinical trials often make it difficult to locate participants: after verification of the eligibility criteria, just one out of ten possible candidates will enter the study. Therefore, clinical trials usually show limitations to generalize the results to the entire population of patients with the disease, but only to those with similar characteristics to the sample included in the study. These peculiarities in clinical trials justify the necessity of conducting a multicenter and/or global studiesto accelerate the recruitment rate and to reach, in a shorter time, the number of patients required for the study. 13

In turn, in observational studies to build a solid sampling plan is important because of the great heterogeneity usually observed in the target population. Therefore, this heterogeneity has to be also reflected in the sample. A cross-sectional population-based study aiming to assess disease estimates or identify risk factors often uses complex probabilistic sampling, because the sample representativeness is crucial. However, in a case-control study, we face the challenge of selecting two different samples for the same study. One sample is formed by the cases, which are identified based on the diagnosis of the disease of interest. The other consists of controls, which need to be representative of the population that originated the cases. Improper selection of control individuals may introduce selection bias in the results. Thus, the concern with representativeness in this type of study is established based on the relationship between cases and controls (comparability).

In cohort studies, individuals are recruited based on the exposure (exposed and unexposed subjects), and they are followed over time to evaluate the occurrence of the outcome of interest. At baseline, the sample can be selected from a representative sample (population-based cohort studies) or a non-representative sample. However, in the successive follow-ups of the cohort member, study participants must be a representative sample of those included in the baseline. 14 , 15 In this type of study, losses over time may cause follow-up bias.

Researchers need to decide during the planning stage of the study if they will work with the entire target population or a sample. Working with a sample involves different steps, including sample size estimation, identification of the sample frame, and selection of the sampling method to be adopted.

Financial Support: None.

* Study performed at Faculdade Meridional - Escola de Medicina (IMED) - Passo Fundo (RS), Brazil.

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Research: People Still Want to Work. They Just Want Control Over Their Time.

• Stephanie Tepper
• Neil Lewis, Jr.

It’s a strong predictor for how satisfied they are with their work — and their lives.

To better understand the role that control over one’s time plays in job and life satisfaction, the authors analyzed survey data from a nationally representative sample. They found: 1) People who had greater control over their time had the highest job satisfaction and overall satisfaction with their lives, 2) Those who felt a sense of time scarcity had less satisfaction with their jobs and were less satisfied with their lives, 3) The number of hours people worked was not related to how satisfied people were with their jobs, and 4) For those who had more control over their time, feeling time scarcity did not undermine their job satisfaction as much as it did for those who had less control over their time. Employers should therefore create and tailor flexible work policies to meet diverse employee needs, fostering satisfaction and retention.

Workers — particularly those considered “ knowledge workers ” who are able to do most if not all of their work with a laptop and an internet connection — have been fighting for the right to maintain control over their time for years. While working from home in 2020 and 2021, they demonstrated to their bosses that they are able to maintain, or in some cases even increase , their productivity while working flexibly. Their bosses, on the other hand, have been pulling them in the opposite direction; executives and managers have been fighting to get workers back into the offices that companies are paying a lot of money to lease. This struggle has affected workers and companies alike. Workers quit en masse during a period that became known as “ the Great Resignation ,” and employers who instituted return-to-office mandates have struggled to hire and retain top talent . Now, especially with Gen Z making up an increasing share of the working population and the conversations around hybrid work and returning to the office stagnating, demands for increased flexibility in work arrangements are still top of mind for many employees and job seekers.

• ST Stephanie Tepper is a behavioral scientist who studies behavioral and policy interventions to reduce economic inequality and promote economic opportunity. She is an Associate Fellow at the U.S. Office of Evaluation Sciences and a Postdoctoral Scholar at Jeb E. Brooks School of Public Policy at Cornell University.
• NL Dr. Neil Lewis Jr is a behavioral scientist who studies the motivational, behavioral, and equity implications of social interventions and policies. He is a Nancy and Peter Meinig Family Investigator in the Life Sciences at Cornell University and Weill Cornell Medicine, where he is also associate professor of communication, medicine, and public policy.

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• Published: 16 July 2024

Evidence of a hydrated mineral enriched in water and ammonium molecules in the Chang’e-5 lunar sample

• Shifeng Jin   ORCID: orcid.org/0000-0002-3522-1060 1 , 2   na1 ,
• Munan Hao 1 , 2   na1 ,
• Zhongnan Guo 3 ,
• Bohao Yin   ORCID: orcid.org/0009-0006-7753-6227 4 ,
• Yuxin Ma 1 ,
• Lijun Deng 5 ,
• Xu Chen   ORCID: orcid.org/0000-0003-3477-2204 1 ,
• Yanpeng Song 1 ,
• Cheng Cao 1 , 2 ,
• Congcong Chai 1 , 2 ,
• Yunqi Ma 6 ,
• Jiangang Guo   ORCID: orcid.org/0000-0003-3880-3012 1 , 7 &
• Xiaolong Chen   ORCID: orcid.org/0000-0002-3028-3772 1 , 2 , 7  

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The presence and distribution of water on the Moon are fundamental to our understanding of the Earth–Moon system. Despite extensive laboratory research and remote sensing explorations, the origin and chemical form of lunar water have remained elusive. In this study we present the discovery of a hydrated mineral, (NH 4 )MgCl 3 ·6H 2 O, in lunar soil samples returned by the Chang’e-5 mission that contains approximately 41 wt% H 2 O. The mineral’s structure and composition closely resemble novograblenovite, a terrestrial fumarole mineral formed through the reaction of hot basalt with water-rich volcanic gases, and carnallite, an Earth evaporite mineral. We rule out terrestrial contamination or rocket exhaust as the origin of this hydrate on the basis of its chemical and isotopic compositions and formation conditions. The presence of ammonium indicates a more complex lunar degassing history and highlights its potential as a resource for lunar habitation. Our findings also suggest that water molecules can persist in sunlit areas of the Moon as hydrated salts, providing crucial constraints on the fugacity of water and ammonia vapour in lunar volcanic gases.

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All data are available in the main text or the Supplementary Information . The X-ray crystallographic coordinates of the structures reported in this study have been deposited at the Cambridge Crystallographic Data Centre (CCDC) under deposition number 2166870. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/data_request/cif . Source data are provided with this paper.

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Acknowledgements

The CE5 lunar sample CE5C0400YJFM00507 (1.5 g) was provided by the China National Space Administration. We thank Y. Li, Q. Zhang, X. Wang, K. Ma, Q. Li, J. Zhou and T. Ying from the IOP, CAS, C. Sun from Tianjin University and C. Li and B. Liu from the National Astronomical Observatories, CAS, for their assistance in experiments and useful discussions. This work was supported by the Key Research Program of Chinese Academy of Sciences (grant number ZDBS-SSW-JSC007-2 to Xiaolong Chen), the Strategic Priority Research Program and Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (grant number XDB33010100 to Xiaolong Chen), the Informatization Plan of Chinese Academy of Sciences (grant number CAS-WX2021SF-0102 to S.J.), the National Natural Science Foundation of China (grant number 52272268 to S.J.) and the Youth Innovation Promotion Association of CAS (grant number 2019005 to S.J.). This work was also supported by the Synergetic Extreme Condition User Facility (SECUF).

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These authors contributed equally: Shifeng Jin, Munan Hao.

Authors and Affiliations

Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, China

Shifeng Jin, Munan Hao, Yuxin Ma, Xu Chen, Yanpeng Song, Cheng Cao, Congcong Chai, Jiangang Guo & Xiaolong Chen

School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China

Shifeng Jin, Munan Hao, Cheng Cao, Congcong Chai & Xiaolong Chen

School of Chemistry and Biological Engineering, University of Science & Technology Beijing, Beijing, China

Zhongnan Guo

School of Earth System Science, Tianjin University, Tianjin, China

Center of Advanced Analysis & Gene Sequencing, Zhengzhou University, Zhengzhou, China

Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining, China

Qi Wei & Yunqi Ma

Songshan Lake Materials Laboratory, Dongguan, China

Jiangang Guo & Xiaolong Chen

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S.J. conducted the sample selection, EDS, EPMA, IR, Raman and isotopic experiments, thermodynamic calculations and data analysis and wrote the manuscript. M.H. conducted the sample selection, singled out the ULM-1 crystal and determined the crystal structure. Z.G. performed thermodynamic calculations. B.Y. performed isotopic experiments. Yuxin Ma performed EDS experiments. L.D. performed EPMA measurements. Xu Chen captured the optical photographs. Y.S., C. Cao and C. Chai performed Raman experiments. Q.W. and Yunqi Ma performed isotopic experiments. J.G. performed Raman experiments. Xialong Chen supervised the project, analysed the data and wrote the manuscript.

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Correspondence to Xiaolong Chen .

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Unprocessed crystal structure data.

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Jin, S., Hao, M., Guo, Z. et al. Evidence of a hydrated mineral enriched in water and ammonium molecules in the Chang’e-5 lunar sample. Nat Astron (2024). https://doi.org/10.1038/s41550-024-02306-8

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NASA’s Perseverance Rover Scientists Find Intriguing Mars Rock

NASA’s Perseverance rover discovered “leopard spots” on a reddish rock nicknamed “Cheyava Falls” in Mars’ Jezero Crater in July 2024. Scientists think the spots may indicate that, billions of years ago, the chemical reactions in this rock could have supported microbial life; other explanations are being considered.

An annotated version of the image of “Cheyava Falls” indicates the markings akin to leopard spots, which have particularly captivated scientists, and the olivine in the rock. The image was captured by the WATSON instrument on NASA’s Perseverance Mars rover on July 18.

The six-wheeled geologist found a fascinating rock that has some indications it may have hosted microbial life billions of years ago, but further research is needed.

A vein-filled rock is catching the eye of the science team of NASA’s Perseverance rover. Nicknamed “Cheyava Falls” by the team, the arrowhead-shaped rock contains fascinating traits that may bear on the question of whether Mars was home to microscopic life in the distant past.

Analysis by instruments aboard the rover indicates the rock possesses qualities that fit the definition of a possible indicator of ancient life. The rock exhibits chemical signatures and structures that could possibly have been formed by life billions of years ago when the area being explored by the rover contained running water. Other explanations for the observed features are being considered by the science team, and future research steps will be required to determine whether ancient life is a valid explanation.

The rock — the rover’s 22nd rock core sample — was collected on July 21, as the rover explored the northern edge of Neretva Vallis, an ancient river valley measuring a quarter-mile (400 meters) wide that was carved by water rushing into Jezero Crater long ago.

NASA’s Perseverance Mars rover took this selfie, made up of 62 individual images, on July 23. A rock nicknamed “Cheyava Falls,” which has features that may bear on the question of whether the Red Planet was long ago home to microscopic life, is to the left of the rover near the center of the image. Credit: NASA/JPL-Caltech/MSSS Full Image Details

“Cheyava Falls” (left) shows the dark hole where NASA’s Perseverance took a core sample; the white patch is where the rover abraded the rock to investigate its composition. A rock nicknamed “Steamboat Mountain” (right) also shows an abrasion patch. This image was taken by Mastcam-Z on July 23.

NASA’s Perseverance used its Mastcam-Z instrument to view the “Cheyava Falls” rock sample within the rover’s drill bit. Scientists believe markings on the rock contain fascinating traits that may bear on the question of whether Mars was home to microscopic life in the distant past.

“We have designed the route for Perseverance to ensure that it goes to areas with the potential for interesting scientific samples,” said Nicola Fox, associate administrator, Science Mission Directorate at NASA Headquarters in Washington. “This trip through the Neretva Vallis riverbed paid off as we found something we’ve never seen before, which will give our scientists so much to study.”

Multiple scans of Cheyava Falls by the rover’s SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instrument indicate it contains organic compounds. While such carbon-based molecules are considered the building blocks of life, they also can be formed by non-biological processes.

NASA’s Perseverance rover used its Mastcam-Z instrument to capture this 360-degree panorama of a region on Mars called “Bright Angel,” where an ancient river flowed billions of years ago. “Cheyava Falls” was discovered in the area slightly right of center, about 361 feet (110 meters) from the rover.

“Cheyava Falls is the most puzzling, complex, and potentially important rock yet investigated by Perseverance,” said Ken Farley, Perseverance project scientist of Caltech in Pasadena. “On the one hand, we have our first compelling detection of organic material, distinctive colorful spots indicative of chemical reactions that microbial life could use as an energy source, and clear evidence that water — necessary for life — once passed through the rock. On the other hand, we have been unable to determine exactly how the rock formed and to what extent nearby rocks may have heated Cheyava Falls and contributed to these features.”

Other details about the rock, which measures 3.2 feet by 2 feet (1 meter by 0.6 meters) and was named after a Grand Canyon waterfall, have intrigued the team, as well.

As shown in this graphic, astrobiologists catalog a seven-step scale, called the CoLD (Confidence of Life Detection) scale, to research whether a sample could indicate life. This “Cheyava Falls” sample is an example of Step One: “Detect possible signal.” Much additional research must be conducted to learn more.

How Rocks Get Their Spots

In its search for signs of ancient microbial life, the Perseverance mission has focused on rocks that may have been created or modified long ago by the presence of water. That’s why the team homed in on Cheyava Falls.

“This is the kind of key observation that SHERLOC was built for — to seek organic matter as it is an essential component of a search for past life,” said SHERLOC’s principal investigator Kevin Hand of NASA’s Jet Propulsion Laboratory in Southern California, which manages the mission.

Running the length of the rock are large white calcium sulfate veins. Between those veins are bands of material whose reddish color suggests the presence of hematite, one of the minerals that gives Mars its distinctive rusty hue.

When Perseverance took a closer look at these red regions, it found dozens of irregularly shaped, millimeter-size off-white splotches, each ringed with black material, akin to leopard spots. Perseverance’s PIXL (Planetary Instrument for X-ray Lithochemistry) instrument has determined these black halos contain both iron and phosphate.

NASA’s Perseverance rover has made very compelling observations in a Martian rock that, with further study, could prove that life was present on Mars in the distant past — but how can we determine that from a rock, and what do we need to do to confirm it? Morgan Cable, a scientist on the Perseverance team, takes a closer look.

“These spots are a big surprise,” said David Flannery, an astrobiologist and member of the Perseverance science team from the Queensland University of Technology in Australia. “On Earth, these types of features in rocks are often associated with the fossilized record of microbes living in the subsurface.”

Spotting of this type on sedimentary terrestrial rocks can occur when chemical reactions involving hematite turn the rock from red to white. Those reactions can also release iron and phosphate, possibly causing the black halos to form. Reactions of this type can be an energy source for microbes, explaining the association between such features and microbes in a terrestrial setting.

We've Got Some Space for You

In one scenario the Perseverance science team is considering, Cheyava Falls was initially deposited as mud with organic compounds mixed in that eventually cemented into rock. Later, a second episode of fluid flow penetrated fissures in the rock, enabling mineral deposits that created the large white calcium sulfate veins seen today and resulting in the spots.

Another Puzzle Piece

While both the organic matter and the leopard spots are of great interest, they aren’t the only aspects of the Cheyava Falls rock confounding the science team. They were surprised to find that these veins are filled with millimeter-size crystals of olivine, a mineral that forms from magma. The olivine might be related to rocks that were formed farther up the rim of the river valley and that may have been produced by crystallization of magma.

If so, the team has another question to answer: Could the olivine and sulfate have been introduced to the rock at uninhabitably high temperatures, creating an abiotic chemical reaction that resulted in the leopard spots?

“We have zapped that rock with lasers and X-rays and imaged it literally day and night from just about every angle imaginable,” said Farley. “Scientifically, Perseverance has nothing more to give. To fully understand what really happened in that Martian river valley at Jezero Crater billions of years ago, we’d want to bring the Cheyava Falls sample back to Earth, so it can be studied with the powerful instruments available in laboratories.”

More Mission Information

A key objective of Perseverance’s mission on Mars is astrobiology , including caching samples that may contain signs of ancient microbial life. The rover will characterize the planet’s geology and past climate, to help pave the way for human exploration of the Red Planet and as the first mission to collect and cache Martian rock and regolith.

NASA's Mars Sample Return Program, in cooperation with ESA (European Space Agency), is designed to send spacecraft to Mars to collect these sealed samples from the surface and return them to Earth for in-depth analysis.

The Mars 2020 Perseverance mission is part of NASA’s Moon to Mars exploration approach, which includes Artemis missions to the Moon that will help prepare for human exploration of the Red Planet.

NASA’s Jet Propulsion Laboratory, which is managed for the agency by Caltech, built and manages operations of the Perseverance rover.

For more about Perseverance:

science.nasa.gov/mission/mars-2020-perseverance

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Jet Propulsion Laboratory, Pasadena, Calif.

818-393-9011

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