how to write sampling techniques in research

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.

Need a helping hand?

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.

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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 ...

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

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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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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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Sampling Methods In Reseach: 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.

Learn about our Editorial Process

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.

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.

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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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• How it works

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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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.

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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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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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3.4 Sampling Techniques in Quantitative Research

Target population.

The target population includes the people the researcher is interested in conducting the research and generalizing the findings on. 40 For example, if certain researchers are interested in vaccine-preventable diseases in children five years and younger in Australia. The target population will be all children aged 0–5 years residing in Australia. The actual population is a subset of the target population from which the sample is drawn, e.g. children aged 0–5 years living in the capital cities in Australia. The sample is the people chosen for the study from the actual population (Figure 3.9). The sampling process involves choosing people, and it is distinct from the sample. 40 In quantitative research, the sample must accurately reflect the target population, be free from bias in terms of selection, and be large enough to validate or reject the study hypothesis with statistical confidence and minimise random error. 2

Sampling techniques

Sampling in quantitative research is a critical component that involves selecting a representative subset of individuals or cases from a larger population and often employs sampling techniques based on probability theory. 41 The goal of sampling is to obtain a sample that is large enough and representative of the target population. Examples of probability sampling techniques include simple random sampling, stratified random sampling, systematic random sampling and cluster sampling ( shown below ). 2 The key feature of probability techniques is that they involve randomization. There are two main characteristics of probability sampling. All individuals of a population are accessible to the researcher (theoretically), and there is an equal chance that each person in the population will be chosen to be part of the study sample. 41 While quantitative research often uses sampling techniques based on probability theory, some non-probability techniques may occasionally be utilised in healthcare research. 42 Non-probability sampling methods are commonly used in qualitative research. These include purposive, convenience, theoretical and snowballing and have been discussed in detail in chapter 4.

Sample size calculation

In order to enable comparisons with some level of established statistical confidence, quantitative research needs an acceptable sample size. 2 The sample size is the most crucial factor for reliability (reproducibility) in quantitative research. It is important for a study to be powered – the likelihood of identifying a difference if it exists in reality. 2 Small sample-sized studies are more likely to be underpowered, and results from small samples are more likely to be prone to random error. 2 The formula for sample size calculation varies with the study design and the research hypothesis. 2 There are numerous formulae for sample size calculations, but such details are beyond the scope of this book. For further readings, please consult the biostatistics textbook by Hirsch RP, 2021. 43 However, we will introduce a simple formula for calculating sample size for cross-sectional studies with prevalence as the outcome. 2

z   is the statistical confidence; therefore,  z = 1.96 translates to 95% confidence; z = 1.68 translates to 90% confidence

p = Expected prevalence (of health condition of interest)

d = Describes intended precision; d = 0.1 means that the estimate falls +/-10 percentage points of true prevalence with the considered confidence. (e.g. for a prevalence of 40% (0.4), if d=.1, then the estimate will fall between 30% and 50% (0.3 to 0.5).

Example: A district medical officer seeks to estimate the proportion of children in the district receiving appropriate childhood vaccinations. Assuming a simple random sample of a community is to be selected, how many children must be studied if the resulting estimate is to fall within 10% of the true proportion with 95% confidence? It is expected that approximately 50% of the children receive vaccinations

z = 1.96 (95% confidence)

d = 10% = 10/ 100 = 0.1 (estimate to fall within 10%)

p = 50% = 50/ 100 = 0.5

Now we can enter the values into the formula

Given that people cannot be reported in decimal points, it is important to round up to the nearest whole number.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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 .

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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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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 structure the Sampling Strategy section of your dissertation

The Sampling Strategy section of your Research Strategy chapter (usually Chapter Three: Research Strategy ) needs to be well structured. A good structure involves four steps : describing , explaining , stating and justifying . You need to: (1) describe what you are studying, including the units involved in your sample and the target population ; (2) explain the types of sampling technique available to you; (3) state and describe the sampling strategy you used; and (4) justify your choice of sampling strategy. In this article, we explain each of these four steps:

• STEP ONE: Describe what you are studying
• STEP TWO: Explain the types of sampling technique available to you
• STEP THREE: State and describe the sampling strategy you used
• STEP FOUR: Justify your choice of sampling strategy

STEP ONE Describe what you are studying

First, the reader needs to know what you studied. This should include details about the following:

The units you measured (or examined).

Your target population .

If you used a probability sampling technique to select your sample , you will also need to describe:

Your sampling frame .

If you are unsure what of any of these terms mean (i.e., unit , sampling frame , population ), you might want to read the article, Sampling: The basics , before reading on. If you feel comfortable with these terms, let's imagine we completed a dissertation on the career choices of students at the University of Oxford, England. Below we describe our units , target population and sampling frame (imagining that we used a probability sampling technique ).

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011.

From this description , the reader learns the following:

Units: students Population: all undergraduate and postgraduate students, full-time and part-time, at the University of Oxford, England Sampling frame: all students enrolled at the University of Oxford, as of 05 January 2011 (i.e., according to Student Records, assuming this is the department that maintains a list of all students studying at the university)

Note the difference between the target population and the sampling frame, from which we select our sample (when using a probability sampling technique). They are the same in all respects apart from the fact that the sampling frame tells the reader that only those students enrolled in the university according to Student Records on a particular date (i.e., 05 January 2011) are being studied. If the list of students kept by Student Records is very different from the population of all students studying at the university, this should be made clear [see the article, Sampling: The basics, to understand more about sampling frames and potential sampling bias].

By the time you come to write up the Sampling Strategy section of your Research Strategy chapter, you should know whether the sampling frame is the same as the population. If it is not, you should highlight the difference between the two. This completes the first part of the Sampling Strategy section of your Research Strategy chapter.

STEP TWO Explain the types of sampling technique available to you

Once you know what units you are studying, as well as your population and sampling frame , the reader will often want to know what types of sampling technique you could use . We say could use rather than should use because whilst there are certain ideal choices of sampling technique, there is seldom a right or wrong answer. Instead, researchers choose sampling techniques that they feel are most appropriate to their study, based on theoretical and practical reasons.

Broadly speaking, you could choose to select your sample from (a) your sampling frame using either a probability sampling technique (e.g., simple random sampling, systematic random sampling, stratified random sampling) or (b) from your population using a non-probability sampling technique (e.g., quota sampling, purposive sampling, convenience sampling, snowball sampling). To understand the differences between these techniques, as well as their advantages and disadvantages, you may want to start by reading the articles: Probability sampling and Non-probability sampling .

When explaining the types of sampling technique that were available to you in this part of your Sampling Strategy section, you should take into account: (a) the research strategy guiding your dissertation; and (b) theoretical and practical sampling issues.

The research strategy guiding your dissertation

Theoretically , the ideal sampling technique for a piece of research (i.e., probability or non-probability sampling) differs depending on whether you are using a quantitative , qualitative or mixed methods research design .

Theoretical and practical sampling issues

Whilst there are theoretical ideals when it comes to choosing a sampling technique to use for your dissertation (i.e., probability or non-probability sampling), it is often practical issues that determine not only whether you choose one type of sampling technique over another (e.g., non-probability sampling over probability sampling ), but also the specific technique that you use (e.g., purposive sampling over quota sampling ; i.e., both are non-probability sampling techniques). Such practical issues range from whether your target population is known (i.e., whether you can get access to a list of the population) to whether you have the time and money to get access to such a list [click on the relevant article to understand the advantages and disadvantages (i.e., theoretical and practical considerations ) of the different probability sampling (e.g., simple random sampling , systematic random sampling , stratified random sampling ) and non-probability sampling techniques (e.g., quota sampling , purposive sampling , self-selection sampling , convenience sampling , snowball sampling )].

Assuming that you understand the differences between these sampling techniques, and their relative merits, let's consider what sampling choices are open to us using our example of career choices of students at the University of Oxford, England . The green text illustrates what we have already written above.

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences (i.e., generalisations ) from our sample of students to all students at the university . Such a probability sampling technique would provide greater external validity for our findings. Since we wanted to compare the career choices of different strata (i.e., groups of students); more specifically, males and females , the appropriate choice of probability sampling technique would have been a stratified random sample . However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique . Since we wanted to compare different strata (i.e., groups of students) and achieve a sample that is as representative as possible of our population , we could have used a quota sample .

From this explanation , the reader learns the following:

Types of sampling strategy available: probability and non-probability sampling Ideal choice: probability sampling Preferred choice of probability sampling technique: stratified random sample Preferred choice of non-probability sampling technique: quota sample

When you are writing up this part of the Sampling Strategy section of your Research Strategy chapter, you may be expected to include a much more comprehensive list of reasons why you prefer one type of sampling strategy (i.e., probability or non-probability) and more specifically, a particular sampling technique (e.g., stratified random sampling over quota sampling). We provide information about the advantages and disadvantages of these different sampling strategies and sampling techniques in the following articles: for probability sampling , see simple random sampling , systematic random sampling , stratified random sampling ; for non-probability sampling techniques, see quota sampling , purposive sampling , self-selection sampling , convenience sampling , snowball sampling .

STEP THREE State and describe the sampling strategy you used

Third, you need to state what sampling strategy and sampling technique you used, describing what you did.

Again, let's consider this for our example of career choices of students at the University of Oxford, England . The green text illustrates what we have already written above.

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences (i.e., generalisations ) from our sample of students to all students at the university. Such a probability sampling technique would provide greater external validity for our findings. Since we wanted to compare the career choices of different strata (i.e., groups of students), including males and females , the appropriate choice of probability sampling technique would have been a stratified random sample . However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique . Since we wanted to compare different strata (i.e., groups of students) and achieve a sample that is as representative as possible of our population , we could have used a quota sample . In the event, we used quota sampling to select the sample of students that would be invited to take part in our dissertation research. Student Records provided us with the appropriate quotas for male and female students, which showed a 53:47 male-female ration [ NOTE: this is a fictitious figure]. We selected a sample size of 200 students, which was based on subjective judgement and practicalities of cost and time. Therefore, we sampled 106 male students (i.e., 53% of our sample size of 200 students) and 94 female students (i.e., 47% of our sample size of 200 students). For convenience, we stood outside the main library where we felt the thoroughfare (i.e., number of students passing by) would be highest.

From this statement and description , the reader learns the following:

Sampling strategy chosen: non-probability sampling Specific sampling technique used: quota sampling

Details of quota sampling: strata (i.e., groups of students) of interest are males and females ratio of males-females at the university was 53:47 sample size selected was 200 students quota sample filled based on ease of access to students at the main university library.

Again, when you are writing up this part of the Sampling Strategy section of your Research Strategy chapter, it may be appropriate to include greater description of the sampling technique you used.

STEP FOUR Justify your choice of sampling strategy

Finally, you need to justify your choice of sampling strategy. When writing up the Sampling Strategy section of your Research Strategy chapter, you may find it easier to combine the third and fourth steps (i.e., stating and describing the sampling strategy you used, as well as justifying that choice). Taking our example of the career choices of students at the University of Oxford, England , we illustrate how the two steps can be integrated. As before, the green text illustrates what we have already written above.

Career choices of students at the University of Oxford, England We examined the career choices of all students at the University of Oxford, England. By all students we mean all undergraduate and postgraduate students, full-time and part-time, studying at the University of Oxford, England, enrolled as of 05 January 2011. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences (i.e., generalisations ) from our sample of students to all students at the university. Such a probability sampling technique would provide greater external validity for our findings. Since we wanted to compare the career choices of different strata (i.e., groups of students), including males and females , the appropriate choice of probability sampling technique would have been a stratified random sample . However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique . Since we wanted to compare different strata (i.e., groups of students) and achieve a sample that is as representative as possible of our population , we could have used a quota sample . In the event, we used quota sampling to select the sample of students that would be invited to take part in our dissertation research. We were unable to use a stratified random sampling , our preferred choice, because we could not obtain permission from Student Records to access a complete list of all students at the university. Without any other way of attaining a list of all students, we had to use quota sampling . However, Student Records did provide us with the appropriate quotas for male and female students, which showed a 53:47 male-female ration [note: this is a fictitious figure]. We selected a sample size of 200 students, which was based on subjective judgement and practicalities of cost and time. Therefore, we sampled 106 male students (i.e., 53% of our sample size of 200 students) and 94 female students (i.e., 47% of our sample size of 200 students). For convenience, we stood outside the main library where we felt the thoroughfare (i.e., number of students passing by) would be highest.

From this justification , the reader learns the following:

Main reason for rejecting the ideal sampling strategy:

Access to a list of all students (i.e., the sampling frame needed for probability sampling ) was not granted by Student Records.

No other way of attaining a list of all students was available.

When you think about justifying your choice of sampling technique when writing up the Sampling Strategy section of your Research Strategy chapter, you should consider both practical reasons (e.g., what time you have available, what access you have, etc.) and theoretical reasons (i.e., those relating to the specific sampling technique , but also your choice of research paradigm , research design and research methods ).

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• An Bras Dermatol
• v.91(3); May-Jun 2016

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.

• Data Science

What are Sampling Techniques? Different Types and Methods

Home Blog Data Science What are Sampling Techniques? Different Types and Methods

Data is the backbone of the majority of what researchers and data scientists do, and they require data to undertake experiments, analyze scenarios, and test ideas. The data samples come from the study population, and samples are the selected portion of the data that represent the whole population. However, dealing with enormous amounts of data is one of the main challenges in data analytics . It is unnecessary and even impractical to investigate the entire population when researching a specific group. Data sampling is the process of analyzing data from a small group of individuals in a larger group. Data sampling allows you to research using various sampling techniques in data analytics without looking at the complete dataset. But what is the sampling technique? Essentially, it's the methods used to obtain a subset of data from a larger set for analysis. However, It is crucial for a person with a career to make sense of data, navigate it, and use it to impact a world filled with data. KnowledgeHut is an online platform focused on providing outcome-based immersive learning experiences to learners.

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What is Sampling?

Statistics defines sampling as the process of gathering information about a population from a subset, like a selected individual or a small group and analyzing that information to study the whole population. The sample space constitutes the foundation of data which in turn is responsible for determining the accuracy of the study or research. Sampling, however, is not as simple as it seems. To land an accurate result, the sample size needs to be accurate, followed by implementing the right sampling methods based on the sample size. based on sample size.

Sampling Steps

An analyst needs to follow certain steps in order to reach conclusions from a broader perspective. The Sampling steps include the following -

• Step 1: Identity and clearly define the target group/population. 
• Step 2: Create a specific sampling frame. 
• Step 3: Select the right sampling methods to be used. 
• Step 4: Specify the sample size. 
• Step 5: Collect the required sampled data.

Major Types of Sampling Methods

There are two types of sampling methods used in market action research - 

1. Probability Sampling

In the probability sampling approach, a researcher selects a few criteria and randomly selects individuals from a population. Using this selection parameter, each member has an equal chance of participating in the sample. 

2. Non-Probability Sampling

In this type of sampling, randomly chosen participants are used by researchers. This type of sampling is not a set or predetermined selection procedure. As a result, it is difficult for all parts of a population to have equal chances of being included in a sample.  

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Different Types of Sampling Techniques

To choose and reach every unit in the population, probability sampling is typically favored when conducting large-scale investigations, particularly when a sample frame is available. We can measure the standard deviation of estimations, create confidence intervals, and formally test hypotheses using probability sampling.

The different sampling methods in probability sampling include: 

A. Simple random sampling  

Simple random sampling gives each member of the population an equal chance of being chosen for the sample. It's similar to drawing a name out of a bowl. Simple random sampling can be performed by anonymizing the population, for example, assigning a number to each object or person in the population and selecting numbers randomly.

Simple random sampling eliminates any bias from the sampling process and is inexpensive, simple, and quick to use. It also provides the researcher with no means of control, increasing the likelihood that unrepresentative groupings will be chosen randomly.

Applications 

• Lottery techniques,  
• Split and Train in machine learning. 

Advantages 

• Little bias due to the random nature of the sample collection 
• Given the usage of random generators, sample selection is straightforward. 
• Due to representativeness, the findings can be broadly interpreted. 

Disadvantage 

• All responders' potential availability might be expensive and time-consuming. 
• Huge sample size 

B. Cluster sampling  

Cluster sampling involves selecting portions of the target population randomly from groupings rather than from single units. These might be already established groupings like residents of particular postal codes or students who attend a particular academic year.

However, in the case of a two-stage cluster sampling, the cluster can be randomly chosen in the first stage, and then the cluster can be randomly chosen again in the second stage. 

Applications  

• One Stage Cluster 
• Two Stage Cluster 
• Reduces time and money. 
• Practical and easy to use. 
• Larger sample sizes can be used. 
• Increased errors in sampling 
• The sample frame's variety might not be well reflected. 

C. Systematic sampling 

In systematic sampling, sometimes called systematic clustering, only the first item is subject to random selection. Afterward, every nth thing or person is chosen according to a rule. Although there is some element of randomness, the researcher may control the frequency at which things are chosen, ensuring that the picks won't unintentionally group.

• Quality Control:  To statistically check the quality of their goods, industrial companies frequently utilize systematic sampling. Here, a sample is gathered by periodically grabbing something from the present production stream. 
• Auditing: The most obvious method to sample an account list for an audit of savings accounts is to look for conformity with accounting processes.
• Cost- and time-effective 
• increases the sample's distribution across the population.

Disadvantage  

• It is important to know the full population. 
• Probable sample bias in case the dataset contains periodic patterns. 

D. Stratified random sampling.  

Stratified sampling uses random selection within established groupings. Knowing information about the target population helps researchers stratify it for research purposes. Although stratified sampling offers advantages, it also raises the issue of subdividing a population, increasing the chance of bias. 

The three types of stratified random sampling are: 

• Proportionate: When compared to the overall population, the sample size for each stratum in this method is proportionate to the number of the stratum's population. 
• Disproportionate: A proportionate stratified random sampling differs from a disproportionate stratified random sampling only by its sampling fraction. Disproportionate sampling results in different sampling fractions for different strata. 
• Optimal: Variable standard deviation determines the size of these strata in optimal stratified random sampling. 

Advantages  

• A greater percentage of all groups represented. 
• The estimations can be as precise if there is uniformity within strata and variation across strata. 
• Complex methodology 
• Possibly more costly and time-consuming 
• Requires understanding of strata membership. 

Non-probability sampling techniques are selected when the precision of the results is not crucial. Non-probability sampling doesn't need a frame, is affordable, and is simple. The bias in the results can be lessened if a non-probability sample is appropriately implemented. Making assumptions about the entire population is hazardous to make, according to the fundamental drawback of non-probability sampling.

The different types of sampling techniques in non-probability sampling include: 

A. Convenience sampling  

The simplest sampling technique is convenience sampling, where participants are picked up based on their availability and desire to participate in the survey. The sample could not be representative of the population as a whole. Hence the results are subject to severe bias. 

This type of sampling technique is usually conducted in offices and social networking sites. Example of sampling techniques includes online surveys, product surveys etc. 

• Obtaining a sample is comparatively easy. 
• Cost-effective 
• Participants are easily accessible. 
• Results cannot be generalized. 
• Possibility of an imbalance in the population's representation 
• Rise of substantial prejudice or biases in the sample frame 

B. Judgmental or purposive sampling

In judgment (or purposeful) sampling, a researcher uses judgment to select individuals from the population to take part in the study. Researchers frequently think they can use good judgment to gather a representative sample while saving time and money.

There is a likelihood that the results will be extremely accurate with a small margin of error because the researcher's expertise is essential for establishing a group in this sampling approach.

This sampling method is used for a small group of chosen groups. 

• Relatively inexpensive and less time consuming 
• Enables researchers to directly contact their target market. 
• Near-real-time outcomes 
• Risk of the researcher making mistakes in judgment 
• Bias levels are high, and dependability is low. 
• Difficulty in generalizing study results 

3. Snowball sampling  

This sampling technique entails primary data sources proposing other prospective primary data sources that may be employed in the study. To create more subjects, the snowball sampling approach relies on referrals from the original participants. As a result, using this sampling technique, sample group members are chosen by chain referral. 

When examining difficult-to-reach groups, the social sciences frequently adopt this sampling methodology. As more subjects who are known to the existing subjects are nominated, the sample grows in size like a snowball. For instance, participants can be asked to suggest more users for interviews while researching risk behaviors among intravenous drug users. 

Three sub-parts of snowball sampling include - 

• Linear snowball sampling - Only one subject is recruited, and the subject only makes one referral. 
• Non-discriminatory exponential snowball sampling - One subject is recruited, and that one subject offers several references. 
• Exponentially discriminative snowball sampling - One subject is recruited, who generates several references. However, only one topic is chosen from the recommendations. 
• Researchers can access uncommon subjects in a certain community. 
• Inexpensive and simple to execute. 
• The additional subjects can be recruited without the assistance of recruiting staff. 
• It's possible that the sample isn't representative. 
• Bias in sampling might exist. 
• It might be challenging to infer conclusions about the wider population with certainty since the sample is liable to biases. 

D. Quota sampling  

Quota sampling is the most used sampling technique used by most market researchers. The survey population is split up into subgroups that are mutually exclusive by the researchers. These categories are chosen based on well-known characteristics, qualities, or interests. The researcher chooses representative samples from each class. 

Quota sampling is carried out in the following steps - 

• Separate the population into distinct subgroups. 
• Determine the percentage of auxiliary groupings in the population. 
• Choose unique subjects for every group of subgroups. 
• Make sure the sample represents the population. 
• Controlled quota sampling - This limit the samples that researchers can choose by introducing certain restrictions. 
• Uncontrolled quota sampling - In uncontrolled quota sampling, a researcher is allowed to select the individuals of the sample group. 
• Independent of sample frames 
• Provides researchers with the opportunity to study a particular subgroup.
• Possibility of an oversized sample 
• Impossible to calculate the sampling error. 
• Researchers' incompetence and/or lack of experience may lead to biases and substandard work.

Factors While Choosing Probability and Non-Probability Samples

To achieve the objectives of the study accurately, it is critical to pick a sampling technique carefully for every research project. However, it is important to note that different sampling methods require different elements to form the sample frame. The efficiency of the sample depends on several variables, which include types of sampling methods require different elements to form the sample frame. The efficiency of the sample depends on a number of variables, which include: 

• To answer a research question, the sample size should be large enough but not so large that it becomes inefficient to sample. 
• The margin of error. 
• Depending on the study or use case, determine the best sampling method. 
• Deviate from any pre-established sample guidelines to rule out biases. 
• Omit hard-to-reach target groups. 
• Low response rates. 

Difference Between Probability Sampling and Non-probability Sampling Methods

The various sampling techniques in research and their subtypes have already been considered. To summarise the entire subject, however, the key distinctions between probability sampling techniques and non-probability sampling techniques are as follows:

Importance of Sampling

In data analytics , sampling is the process of selecting a representative subset of a larger population. Sampling is a relatively easier technique to study a population closely from drawn samples. "What is sampling techniques in research?" is a common question among budding researchers. Sampling helps an analyst determine a given population's characteristics more cost-effectively and practically.

Sampling aims to collect data that can be used to draw conclusions about the wider population. There are many reasons why sampling is important in data analytics - 

• Sampling allows the data analysts to work with a smaller dataset, which can be more manageable and easier to work with.   
• Sampling can help to ensure that the data is representative of the population as a whole, which is important because it means that the conclusions drawn from the data are more likely to be accurate.  
• Sampling can help to reduce bias in data analysis. This is because Sampling allows analysts to select a representative subset of a population, which can help to reduce the skew that may be present in the data.  
• Sampling is an important tool that helps analysts to collect accurate and reliable data.  

Important Terminologies Need to Know

1. population.

The population in statistics is the entire group of items from which a sample can be drawn. The population can be defined in terms of geographical location, age, gender, occupation, etc.  A population can be very large, making it impractical or impossible to study all group members.

A sample is a set of data collected and used to determine the population parameters. Once the sample has been selected, data can be collected and analyzed in order to make inferences or predictions about the population as a whole.

3. Sampling

Sampling is the process of selecting a representative group from a larger population. The main purpose of sampling is to provide information about a population that can be used to make inferences or predictions about that population.

Research and surveys require a sample from a large population. Sampling is a great way to achieve that. As many might wonder, "What is sampling techniques?", it's essential to know that sampling techniques vary depending on the type of results that one wants. It is important to remember that different sampling techniques should be applied according to the case taken; keeping this in mind, we must choose the appropriate sampling techniques. You would have a highly accurate (and time-consuming) undertaking on your hands if you could ask everyone in a population to participate in your study and have everyone respond. You would have a similarly accurate effort if you could ask every single person in a population to participate and have each one respond.

But since that isn't practicable, sampling provides a "good enough" option that trades some accuracy for convenience and usability. How well you account for bias, non-sampling error, and sampling error in your survey design will determine how much precision you lose. KnowledgeHut is a trusted online platform dedicated to skill-based learning modules.   

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Frequently Asked Questions (FAQs)

In statistical analysis, sampling techniques meaning is the process of selecting a specified number of observations from a larger population. Depending on the sort of study being done, a variety of methods, including systematic sampling and simple random sampling, may be employed to draw samples from a broader population. 

Certain limitations of sampling techniques include the following - 

• Probability of biased results 
• Selecting a good sample frame can be difficult at times 
• Different sampling techniques demand different time frames 

Data sampling in data science is a statistical analysis approach used to pick, move, and analyze a representative selection of data points in order to spot trends and patterns in the larger data set being looked at. 

The salary range of a data scientist can be anywhere between ₹4 Lakhs to ₹24 Lakhs annually. The average salary of a data scientist in India is ₹11 Lakhs per annum.

With the rapid growth of automation and technology in business modules, data scientists are very much in demand, which is projected to grow even more in the coming years. So it can be said that data science is a very promising career with tremendous growth opportunities shortly.

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Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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• What Is Purposive Sampling? | Definition & Examples

What Is Purposive Sampling? | Definition & Examples

Published on August 11, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Purposive sampling refers to a group of non-probability sampling techniques in which units are selected because they have characteristics that you need in your sample. In other words, units are selected “on purpose” in purposive sampling.

Also called judgmental sampling, this sampling method relies on the researcher’s judgment when identifying and selecting the individuals, cases, or events that can provide the best information to achieve the study’s objectives.

Purposive sampling is common in qualitative research and mixed methods research . It is particularly useful if you need to find information-rich cases or make the most out of limited resources, but is at high risk for research biases like observer bias .

Table of contents

When to use purposive sampling, purposive sampling methods and examples, maximum variation sampling, homogeneous sampling, typical case sampling, extreme (or deviant) case sampling, critical case sampling, expert sampling, example: step-by-step purposive sampling, advantages and disadvantages of purposive sampling, other interesting articles, frequently asked questions about purposive sampling.

Purposive sampling is best used when you want to focus in depth on relatively small samples . Perhaps you would like to access a particular subset of the population that shares certain characteristics, or you are researching issues likely to have unique cases.

The main goal of purposive sampling is to identify the cases, individuals, or communities best suited to helping you answer your research question . For this reason, purposive sampling works best when you have a lot of background information about your research topic. The more information you have, the higher the quality of your sample.

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Depending on your research objectives, there are several purposive sampling methods you can use:

• Maximum variation (or heterogeneous) sampling

Maximum variation sampling , also known as heterogeneous sampling, is used to capture the widest range of perspectives possible.

To ensure maximum variation, researchers include both cases, organizations, or events that are considered typical or average and those that are more extreme in nature. This helps researchers to examine a subject from different angles, identifying important common patterns that are true across variations.

Homogeneous sampling, unlike maximum variation sampling, aims to reduce variation, simplifying the analysis and describing a particular subgroup in depth.

Units in a homogeneous sample share similar traits or specific characteristics—e.g., life experiences, jobs, or cultures. The idea is to focus on this precise similarity, analyzing how it relates to your research topic. Homogeneous sampling is often used for selecting focus group participants.

Typical case sampling is used when you want to highlight what is considered a normal or average instance of a phenomenon to those who are unfamiliar with it. Participants are generally chosen based on their likelihood of behaving like everyone else sharing the same characteristics or experiences.

Keep in mind that the goal of typical case sampling is to illustrate a phenomenon, not to make generalized statements about the experiences of all participants. For this reason, typical case sampling allows you to compare samples, not generalize samples to populations.

The idea behind extreme case sampling is to illuminate unusual cases or outliers. This can involve notable successes or failures, “top of the class vs. bottom of the class” scenarios, or any unusual manifestation of a phenomenon of interest.

This form of sampling, also called deviant case sampling, is often used when researchers are developing best practice guidelines or are looking into “what not to do.”

Critical case sampling is used when a single or very small number of cases can be used to explain other similar cases.  Researchers determine whether a case is critical by using this maxim: “if it happens here, it will happen anywhere.” In other words, a case is critical if what is true for one case is likely to be true for all other cases.

Although you cannot make statistical inferences with critical case sampling, you can apply your findings to similar cases. Researchers use critical case sampling in the initial phases of their research, in order to establish whether a more in-depth study is needed.

If you first ask local government officials and they do not understand them, then probably no one will. Alternatively, if you ask random passersby, and they do understand them, then it’s safe to assume most people will.

Expert sampling is used when your research requires individuals with a high level of knowledge about a particular subject. Your experts are thus selected based on a demonstrable skill set, or level of experience possessed.

This type of sampling is useful when there is a lack of observational evidence, when you are investigating new areas of research, or when you are conducting exploratory research .

Purposive sampling is widely used in qualitative research , when you want to focus in depth on a certain phenomenon. There are five key steps involved in drawing a purposive sample.

Step 1: Define your research problem

Start by deciding your research problem : a specific issue, challenge, or gap in knowledge you aim to address in your research. The way you formulate your problem determines your next steps in your  research design , as well as the sampling method and the type of analysis you undertake.

Step 2: Determine your population

You should begin by clearly defining the population from which your sample will be taken, since this is where you will draw your conclusions from.

Step 3: Define the characteristics of your sample

In purposive sampling, you set out to identify members of the population who are likely to possess certain characteristics or experiences (and to be willing to share them with you). In this way, you can select the individuals or cases that fit your study, focusing on a relatively small sample.

Alternatively, you may be interested in identifying common patterns, despite the variations in how the youth responded to the intervention. You can draw a maximum variation sample by including a range of outcomes:

• Youth who reported no effects after the intervention
• Youth who had an average response to the intervention
• Youth who reported significantly better outcomes than the average after the intervention

Step 4: Collect your data using an appropriate method

Depending on your research question and the type of data you want to collect, you can now decide which data collection method is best for you.

Step 5: Analyze and interpret your results

Purposive sampling is an effective method when dealing with small samples, but it is also an inherently biased method. For this reason, you need to document the research bias in the methodology section of your paper and avoid applying any interpretations beyond the sampled population.

Knowing the advantages and disadvantages of purposive sampling can help you decide if this approach fits your research design.

Advantages of purposive sampling

There are several advantages to using purposive sampling in your research.

• Although it is not possible to make statistical inferences from the sample to the population, purposive sampling techniques can provide researchers with the data to make other types of generalizations from the sample being studied. Remember that these generalizations must be logical, analytical, or theoretical in nature to be valid.
• Purposive sampling techniques work well in qualitative research designs that involve multiple phases, where each phase builds on the previous one. Purposive sampling provides a wide range of techniques for the researcher to draw on and can be used to investigate whether a phenomenon is worth investigating further.

Disadvantages of purposive sampling

However, purposive sampling can have a number of drawbacks, too.

• As with other non-probability sampling techniques, purposive sampling is prone to research bias . Because the selection of the sample units depends on the researcher’s subjective judgment, results have a high risk of bias, particularly observer bias .
• If you are not aware of the variations in attitudes, opinions, or manifestations of the phenomenon of interest in your target population, identifying and selecting the units that can give you the best information is extremely difficult.

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
• Cluster sampling
• Stratified sampling
• 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

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

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

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

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 .

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 .

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Pricing Strategies and Analysis

Introduction to pricing strategies and analysis.

Pricing is a critical aspect of market research and a key determinant of a product's success. Effective pricing strategies consider factors such as production costs, market demand, competition, and perceived value. This study guide will explore various pricing strategies and analytical techniques to help you make informed decisions and optimize your pricing for market success.

Common Terms and Definitions

Price Elasticity of Demand : A measure of how responsive the quantity demanded of a product is to changes in its price.

Cost-Plus Pricing : A pricing strategy that involves adding a fixed markup to the cost of producing a product.

Value-Based Pricing : A pricing strategy that sets prices based on the perceived value of the product to the customer.

Penetration Pricing : A pricing strategy that involves setting a low initial price to attract customers and gain market share.

Skimming Pricing : A pricing strategy that involves setting a high initial price to maximize profits from early adopters before gradually lowering the price.

Competitive Pricing : A pricing strategy that involves setting prices based on the prices of competing products in the market.

Price Discrimination : A pricing strategy that involves charging different prices to different customers based on their willingness to pay.

Factors Influencing Pricing Decisions

• Production costs
• Market demand
• Competition
• Perceived value
• Target audience
• Product life cycle stage
• Company objectives

Pricing Models and Strategies

Cost-Based Pricing : Setting prices based on the cost of producing the product, with a fixed markup added to ensure profitability.

Value-Based Pricing : Setting prices based on the perceived value of the product to the customer, considering factors such as benefits, quality, and brand reputation.

Competitive Pricing : Setting prices based on the prices of competing products in the market, either matching or slightly undercutting them to attract customers.

Dynamic Pricing : Adjusting prices in real-time based on factors such as demand, supply, and competitor pricing, often using algorithms and data analytics.

Freemium Pricing : Offering a basic version of the product for free while charging for premium features or services, often used in software and digital products.

Pricing Analysis Techniques

Break-Even Analysis : Determining the point at which total revenue equals total costs, helping to set prices that ensure profitability.

Price Sensitivity Analysis : Assessing how changes in price affect customer demand, often using surveys or market experiments.

Conjoint Analysis : A statistical technique that helps determine the relative importance of different product attributes, including price, in customers' purchasing decisions.

Price Elasticity Analysis : Measuring how responsive the quantity demanded of a product is to changes in its price, helping to predict the impact of price changes on revenue.

Common Questions and Answers

How do I choose the right pricing strategy for my product?

Consider factors such as your target audience, production costs, competition, and company objectives. Analyze market data and customer preferences to determine which pricing strategy aligns best with your goals and market position.

What is the difference between cost-based and value-based pricing?

Cost-based pricing focuses on setting prices based on the cost of producing the product, with a fixed markup added for profitability. Value-based pricing, on the other hand, sets prices based on the perceived value of the product to the customer, considering factors such as benefits, quality, and brand reputation.

How can I use pricing analysis to optimize my pricing strategy?

Techniques such as break-even analysis, price sensitivity analysis, conjoint analysis, and price elasticity analysis can help you understand the relationship between price and demand, as well as the relative importance of price in customers' purchasing decisions. Use these insights to adjust your pricing strategy for optimal market performance.

Effective pricing strategies and analysis are essential for market success. By understanding the key concepts, pricing models, and analytical techniques outlined in this study guide, you will be well-equipped to make informed pricing decisions and optimize your pricing strategy for maximum profitability and market share.

Marketing Mix and Promotional Strategies

Product development and innovation, consumer behavior and insights, competitive analysis and industry trends, data analysis and interpretation, data collection and sampling techniques.

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