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Sampling Methods In Research: Types, Techniques, & Examples

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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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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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.

What is survey sampling: Understanding methodology and sampling techniques

The importance of survey sampling, cost-effectiveness, increased efficiency, generalizability, practicality, reducing bias, flexibility, ethical considerations, feasibility of analysis, population and sampling frame, sample size, simple random sampling, stratified sampling, systematic sampling, cluster sampling, convenience sampling, use surveyplanet to start survey sampling.

Surveys are crucial to the collection of valuable data and insights for research, market analysis, and decision-making. However, surveying an entire population is impractical and too costly.

This is why there is survey sampling. What is survey sampling? It is a statistical method that allows researchers to study a smaller population subset to draw meaningful conclusions about the entire group. Now we’ll cover its importance, fundamentals, and the various sampling techniques that researchers use in survey sampling.

Imagine a scenario where you want to study the preferences of coffee drinkers across an entire country. Interviewing every coffee lover would be impossible. Survey sampling allows researchers to select a representative group that can provide insights applicable to the entire population of coffee enthusiasts.

Properly conducted survey sampling yields reliable results while being cost and time-efficient.

The benefits of survey sampling

Survey sampling offers several benefits that make it a valuable research technique. Some of the key advantages include:

Surveying an entire population can be time-consuming, expensive, and sometimes impractical. Sampling helps researchers gather data from a population subset, significantly reducing costs, time, and effort—while still providing valuable insights.

By focusing on a smaller, representative sample, researchers can efficiently gather the necessary information without having to collect data from every individual in the population. This increased efficiency is particularly useful for large or geographically dispersed populations.

When done correctly, survey sampling provides results that are representative of the entire population. This means that the findings obtained from the sample can be generalized to the larger group, allowing researchers to draw conclusions and make inferences about the population as a whole.

Surveys are versatile research tools that can be applied to various research questions and objectives. Sampling makes it feasible to study complex or hard-to-reach populations for which conducting a census might not be feasible.

When a well-designed sampling method is used, it can help reduce potential bias in the data. Bias occurs when the sample needs to accurately represent the characteristics of the population, leading to accurate conclusions.

Proper sampling techniques, such as random sampling or stratified sampling, aim to minimize bias and improve the reliability of the findings. Learn how to avoid biased survey questions with our tips and tricks and successfully avoid gathering inaccurate data.

Surveys can be adapted to different data collection methods, such as face-to-face interviews, telephone surveys, online questionnaires, or mail surveys. This flexibility allows researchers to choose the most suitable method for their specific research context.

In some cases, conducting a census or surveying an entire population may raise ethical concerns, especially if the study involves sensitive or personal information. Sampling can protect the privacy and confidentiality of individuals since only a subset of the population is involved.

Read our article about the most common types of errors in surveying respondents and concise guidelines on how to avoid them.

Smaller sample sizes are more manageable for data analysis, especially when dealing with large datasets. Researchers can process the collected data more easily, leading to quicker results and insights.

Understanding survey sampling methodology

Survey sampling methods are essential techniques used in statistics and research to gather data from a subset of a larger population. The objective is to obtain accurate and reliable information about the entire population while minimizing costs and time.

We’ll now cover some common survey sampling methods. If you want to learn more about survey methodology, read our ultimate guide to survey data collection with methods, examples, and analysis .

The population is the entire group of individuals, items, or events that researchers aim to study. It represents the target of interest for the research.

Defining the population accurately is crucial because the conclusions drawn from the sample will be generalized back to this larger group. For example, if a company wants to understand the preferences of smartphone users in a country, the population would be all smartphone users within that country.

The sampling frame is a list or representation of all elements within the population. It acts as the basis for selecting the sample. It is essential to ensure that the sampling frame is comprehensive and up-to-date, as any omissions or inaccuracies may introduce bias into the sample.

For instance, in the smartphone user survey, the sampling frame ideally includes an updated list of all smartphone users or phone numbers within the country.

Determining a good survey sample size is one of the most important steps in survey sampling. The sample size directly impacts the precision of the estimates derived from the survey. A larger sample size generally results in more accurate findings, as it reduces the margin of error and increases the statistical power of the study. However, larger samples may also incur higher costs and require more time to collect and analyze the data.

The sample size calculation involves considering factors such as the desired confidence level, margin of error, population variability, and precision level required. Researchers often use statistical formulas to determine the optimal sample size that balances cost, time, and accuracy.

Explore our survey sample size calculator that can help you determine how many people you need to survey for data to be statistically significant.

Simple random sampling is one of the most straightforward and commonly used sampling techniques. It involves randomly selecting individuals from the sampling frame with each member of it having an equal probability of being chosen. This method ensures that the sample is representative of the population and reduces the risk of bias. Random number generators or drawing lots are often used to implement this technique.

Simple random sampling is particularly useful when the population is relatively homogeneous and there is no specific pattern or structure within the data. It is widely used in various research fields and surveys.

Stratified sampling is employed when the population can be divided into subgroups or strata based on specific characteristics or attributes. These characteristics can be demographic (age, gender), socioeconomic (income, education), geographic (region), or any other relevant factor.

Researchers then take random samples from each stratum. The sample size is proportional to the size of each stratum within the population. This method ensures that each subgroup is well represented in the final sample, making it ideal for studying the variations between different population segments.

For example, a study examining consumer preferences for a new product might use stratified sampling to ensure representation from different age groups and income levels.

Systematic sampling is a convenient and straightforward technique. It involves selecting every “k-th” element from the sampling frame after randomly choosing a starting point. The value of “k” is calculated by dividing the population size by the sample size. For instance, if the population size is 1000 and the desired sample size is 100, “k” would be 1000/100 = 10. Every 10th individual in the sampling frame would be selected for the sample.

Easy to implement and often more efficient than simple random sampling, systematic sampling is especially helpful when no specific order or pattern exists within the data. However, it may introduce bias if there is a periodicity or pattern in the sampling frame that aligns with the value of “k.”

Cluster sampling is employed when the population is geographically or organizationally clustered. Instead of sampling individuals directly from the sampling frame, researchers first divide the population into clusters or groups (e.g., neighborhoods, schools, companies).

Random samples of clusters are selected, with all individuals within the chosen clusters surveyed. Cluster sampling is useful when it is impractical to sample individuals directly. It also has the advantage of reducing costs and logistical challenges.

However, cluster sampling can introduce cluster-level biases and the sample may not be as diverse as other sampling techniques. Careful consideration of the clusters’ representativeness is essential to ensure valid conclusions.

Convenience sampling involves selecting individuals who are easily accessible or readily available to the researcher. This sampling method is often used when time and resources are limited, making it convenient to gather data quickly.

While convenience sampling is easy to implement, it can introduce significant bias into the results. The individuals selected may not be similar to the larger population, leading to potential inaccuracies in the findings. It is essential to interpret the results from convenience sampling with caution and recognize its limitations.

Survey sampling is a powerful tool that allows researchers to draw meaningful conclusions about a larger population by studying a carefully selected subset. Understanding the various sampling techniques and their appropriate use is crucial for obtaining reliable and valid results.

By applying survey sampling methodologies effectively, researchers can gain valuable insights and make better-informed decisions. The next time you come across survey data, consider the sampling technique used—it greatly impacts the credibility of the findings.

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling is a critical process in research, allowing researchers to draw conclusions about a larger population by examining a smaller, manageable subset. Sampling methods are essential for producing reliable, representative data without needing to survey an entire population. This guide covers various types of sampling methods, key techniques, and practical examples to help you select the most suitable method for your research.

Sampling is the process of selecting a subset of individuals or items from a larger population to make inferences about that population. Researchers use sampling to collect data more efficiently and to generalize findings to the entire group without surveying everyone.

Key Objectives of Sampling :

• Reduce Costs and Time : Sampling allows for efficient data collection by focusing on a representative subset.
• Improve Accuracy : Smaller, well-designed samples can lead to more accurate, focused data collection.
• Ensure Representativeness : By carefully selecting a sample, researchers can ensure that the findings are relevant to the larger population.

Types of Sampling Methods

Sampling methods can be broadly classified into two categories: probability sampling and non-probability sampling .

1. Probability Sampling

In probability sampling, every individual or item in the population has a known, non-zero chance of being selected. This type of sampling is often used when researchers aim for unbiased, generalizable results.

Examples of Probability Sampling :

• Simple random sampling
• Stratified sampling
• Systematic sampling
• Cluster sampling

2. Non-Probability Sampling

In non-probability sampling, individuals are selected based on specific characteristics or convenience rather than random selection. This method is suitable for exploratory research where generalizability is less critical.

Examples of Non-Probability Sampling :

• Convenience sampling
• Quota sampling
• Snowball sampling
• Purposive sampling

Techniques and Examples for Each Sampling Method

Probability sampling techniques.

• Technique : Each individual in the population has an equal chance of being selected. Researchers use random number generators or random selection tools to choose participants.
• Example : A school administrator randomly selects 50 students from a list of all students to survey about cafeteria satisfaction.
• Technique : The population is divided into subgroups (strata) based on a characteristic (e.g., age, gender), and random samples are taken from each subgroup.
• Example : In a study on employee satisfaction, researchers divide employees into departments (e.g., sales, HR, finance) and randomly select employees from each department.
• Technique : A starting point is randomly selected, and then every kth individual is chosen from a list. This method is often used when there’s a fixed pattern or order in the population list.
• Example : A researcher wants to survey a population of 1,000 people and decides to select every 10th person on a sorted list after a random start.
• Technique : The population is divided into clusters (groups) that are randomly selected. All individuals within selected clusters are then included in the sample.
• Example : In a national health study, a researcher randomly selects specific cities (clusters) and surveys all residents within those cities.

Non-Probability Sampling Techniques

• Technique : Participants are selected based on availability or ease of access, making it a fast and easy sampling method.
• Example : A psychology student surveys classmates because they are easily accessible and available for quick data collection.
• Technique : The population is divided into categories (e.g., age, gender), and a specified number of participants from each category is chosen non-randomly.
• Example : A researcher studying consumer preferences might set a quota to survey 50 men and 50 women in a shopping mall.
• Technique : Participants recruit other participants, making it useful for studying hard-to-reach populations.
• Example : In a study on experiences of ex-convicts, initial participants refer other ex-convicts they know, expanding the sample.
• Technique : Participants are selected based on specific criteria or characteristics relevant to the study’s purpose.
• Example : In a study on the effects of leadership training, a researcher selects participants who hold managerial positions to gain insights specific to leaders.

When to Use Each Sampling Method

• Simple Random Sampling : Use when you need a fully representative sample, especially if the population is homogeneous and a sampling frame is available.
• Stratified Sampling : Best when studying specific subgroups within a population, as it ensures representation across key characteristics.
• Systematic Sampling : Suitable when you have a large population list and need a simple yet systematic approach, especially if the list has no inherent order.
• Cluster Sampling : Useful for large, geographically dispersed populations; ideal when it’s impractical to survey individuals directly.
• Convenience Sampling : Ideal for exploratory studies, pilot tests, or when time and resources are limited.
• Quota Sampling : Use when studying demographic or categorical diversity, especially when you need specific representation within the sample.
• Snowball Sampling : Ideal for reaching hidden, hard-to-reach, or marginalized populations.
• Purposive Sampling : Best when studying a specific, well-defined population or a unique group that directly relates to the research question.

Examples of Sampling in Research Studies

• Objective : Investigate student study habits across grade levels.
• Sampling Method : Stratified sampling, where students are divided into grades (strata) and randomly sampled from each grade.
• Objective : Examine patient satisfaction in a hospital network.
• Sampling Method : Cluster sampling, where hospitals (clusters) are selected, and all patients within selected hospitals are surveyed.
• Objective : Understand shopping preferences among young adults.
• Sampling Method : Convenience sampling, where young adults at a popular mall are surveyed.
• Objective : Study the experiences of refugees in a new country.
• Sampling Method : Snowball sampling, where initial participants (refugees) refer others in their community.

Advantages and Disadvantages of Each Method

Tips for choosing the right sampling method.

• Define Your Research Goals : Clarify whether you need a representative sample or a specific target group to meet the objectives.
• Consider Resources : Time, budget, and accessibility influence the feasibility of sampling methods.
• Evaluate Population Characteristics : Large, diverse populations may require stratified or cluster sampling, while homogeneous populations might benefit from simple random sampling.
• Assess Generalizability : If generalizing results to a larger population is important, prioritize probability sampling methods.
• Address Ethical Concerns : Ensure ethical considerations for sensitive populations, especially when using snowball or purposive sampling.

Sampling is a cornerstone of research design, allowing researchers to make informed conclusions about populations through carefully selected samples. Whether using probability or non-probability sampling, understanding each method’s strengths and limitations can help researchers choose the best approach for their study. With well-chosen sampling methods, researchers can collect reliable data, make meaningful inferences, and contribute valuable insights to their fields.

• Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Sage Publications.
• Babbie, E. (2020). The Practice of Social Research . Cengage Learning.
• Fowler, F. J. (2014). Survey Research Methods . Sage Publications.
• Lohr, S. (2021). Sampling: Design and Analysis . Chapman and Hall/CRC.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods . Sage Publications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Sampling methods, types & techniques.

15 min read Your comprehensive guide to the different sampling methods available to researchers – and how to know which is right for your research.

Author: Will Webster

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.

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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

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 randomization 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 anonymizing 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 college 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 college), 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 specialized 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 specialized 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

How to determine sample size 12 min read, selection bias 11 min read, systematic random sampling 15 min read, convenience sampling 18 min read, probability sampling 8 min read, non-probability sampling 17 min read, stratified random sampling 12 min read, request demo.

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