research proposal sampling technique

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.

Psst... there’s more!

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

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

Excellent and helpful for junior researcher!

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

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Instant insights, infinite possibilities

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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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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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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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 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.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

The Purpose of Sampling

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

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

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

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

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

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

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

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

Random Sampling

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

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

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

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

Stratified Sampling

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

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

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

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

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

Opportunity Sampling

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

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

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

Systematic Sampling

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

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

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

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

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

Sample size

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

Reliability and Validity

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

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

Practical Considerations

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

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

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• Getting started
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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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Sampling Methods

Affiliation.

• 1 14742 School of Nursing, University of Texas Health Science Center, San Antonio, TX, USA.
• PMID: 32155099
• DOI: 10.1177/0890334420906850

Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration. In addition, issues related to sampling methods are described to highlight potential problems.

Keywords: breastfeeding; non-probability sampling; probability sampling; sampling; sampling methods.

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Sampling Techniques for Qualitative Research

• First Online: 27 October 2022

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• Heather Douglas 4  

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This chapter explains how to design suitable sampling strategies for qualitative research. The focus of this chapter is purposive (or theoretical) sampling to produce credible and trustworthy explanations of a phenomenon (a specific aspect of society). A specific research question (RQ) guides the methodology (the study design or approach ). It defines the participants, location, and actions to be used to answer the question. Qualitative studies use specific tools and techniques ( methods ) to sample people, organizations, or whatever is to be examined. The methodology guides the selection of tools and techniques for sampling, data analysis, quality assurance, etc. These all vary according to the purpose and design of the study and the RQ. In this chapter, a fake example is used to demonstrate how to apply your sampling strategy in a developing country.

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Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research, the role of sampling in mixed methods-research.

Preparation of Qualitative Research

Douglas, H. (2010). Divergent orientations in social entrepreneurship organisations. In K. Hockerts, J. Robinson, & J. Mair (Eds.), Values and opportunities in social entrepreneurship (pp. 71–95). Palgrave Macmillan.

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Douglas, H., Eti-Tofinga, B., & Singh, G. (2018a). Contextualising social enterprise in Fiji. Social Enterprise Journal, 14 (2), 208–224. https://doi.org/10.1108/SEJ-05-2017-0032

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Douglas, H., Eti-Tofinga, B., & Singh, G. (2018b). Hybrid organisations contributing to wellbeing in small Pacific island countries. Sustainability Accounting, Management and Policy Journal, 9 (4), 490–514. https://doi.org/10.1108/SAMPJ-08-2017-0081

Douglas, H., & Borbasi, S. (2009). Parental perspectives on disability: The story of Sam, Anna, and Marcus. Disabilities: Insights from across fields and around the world, 2 , 201–217.

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Douglas, H. (1999). Community transport in rural Queensland: Using community resources effectively in small communities. Paper presented at the 5th National Rural Health Conference, Adelaide, South Australia, pp. 14–17th March.

Douglas, H. (2006). Action, blastoff, chaos: ABC of successful youth participation. Child, Youth and Environments, 16 (1). Retrieved from http://www.colorado.edu/journals/cye

Douglas, H. (2007). Methodological sampling issues for researching new nonprofit organisations. Paper presented at the 52nd International Council for Small Business (ICSB) 13–15 June, Turku, Finland.

Draper, H., Wilson, S., Flanagan, S., & Ives, J. (2009). Offering payments, reimbursement and incentives to patients and family doctors to encourage participation in research. Family Practice, 26 (3), 231–238. https://doi.org/10.1093/fampra/cmp011

Puamua, P. Q. (1999). Understanding Fijian under-achievement: An integrated perspective. Directions, 21 (2), 100–112.

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Heather Douglas

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Rajendra Baikady

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Douglas, H. (2022). Sampling Techniques for Qualitative Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_29

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

• Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
• Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
• It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling techniques are a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

In this Article:

• Purposive Sampling
• Convenience Sampling
• Snowball Sampling
• Theoretical Sampling

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique in qualitative research that’s commonly used. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling  

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique in qualitative research is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique in qualitative research, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on September 5, 2024.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

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As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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Sampling in Qualitative Research

In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research. But sampling has long been a central concern in the social and humanistic inquiry, albeit in a different guise suited to the different goals. There is a need for more explicit discussion of qualitative sampling issues. This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It then describes common questions about sampling in qualitative research. In conclusion it proposes the concept of qualitative clarity as a set of principles (analogous to statistical power) to guide assessments of qualitative sampling in a particular study or proposal.

Questions of what is an appropriate research sample are common across the many disciplines of gerontology, albeit in different guises. The basic questions concern what to observe and how many observations or cases are needed to assure that the findings will contribute useful information. Throughout the history of gerontology, the most recognized and elaborate discourse about sampling has been associated with quantitative research, including survey and medical research. But concerns about sampling have long been central to social and humanistic inquiry (e.g., Mead 1953 ). The authors argue such concerns remained less recognized by quantitative researchers because of differing focus, concepts, and language. Recently, an explicit discussion about concepts and procedures for qualitative sampling issues has emerged. Despite the growing numbers of textbooks on qualitative research, most offer only a brief discussion of sampling issues, and far less is presented in a critical fashion ( Gubrium and Sankar 1994 ; Werner and Schoepfle 1987 ; Spradley 1979 , 1980 ; Strauss and Corbin 1990 ; Trotter 1991 ; but cf. Denzin and Lincoln 1994 ; DePoy and Gitlin 1993 ; Miles and Huberman 1994 ; Pelto and Pelto 1978 ).

The goal of this article is to extend and further refine the explicit discussion of sampling issues and techniques for qualitative research in gerontology. Throughout the article, the discussion draws on a variety of examples in aging, disability, ethnicity as well as more general anthropology.

The significance of the need to understand qualitative sampling and its uses is increasing for several reasons. First, emerging from the normal march of scientific developments that builds on prior research, there is a growing consensus about the necessity of complementing standardized data with insights about the contexts and insiders' perspectives on aging and the elderly. These data are best provided by qualitative approaches. In gerontology, the historical focus on aging pathology obscured our view of the role of culture and personal meanings in shaping how individuals at every level of cognitive and physical functioning personally experience and shape their lives. The individual embodying a “case” or “symptoms” continues to make sense of, manage, and represent experiences to him- or herself and to others. A second significance to enhancing our appreciation of qualitative approaches to sampling is related to the societal contexts of the scientific enterprise. Shifts in public culture now endorse the inclusion of the experiences and beliefs of diverse and minority segments of the population. A reflection of these societal changes is the new institutional climate for federally funded research, which mandates the inclusion and analysis of data on minorities. Qualitative approaches are valuable because they are suited to assessing the validity of standardized measures and analytic techniques for use with racial and ethnic subpopulations. They also permit us to explore diversities in cultural and personal beliefs, values, ideals, and experiences.

This article will outline the guiding principles and rationales, features, and practices of sampling in qualitative research. It describes the scientific implications of the cultural embeddedness of sampling issues as a pervasive feature in wider society. It then describes common questions about sampling in qualitative research. It concludes by proposing an analog to statistical power, qualitative clarity , as a set of principles to guide assessments of the sampling techniques in a study report or research proposal. The term clarity was chosen to express the goal of making explicit the details of how the sample was assembled, the theoretical assumptions, and the practical constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. The concept focuses on evaluating the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation. These can be evaluated even if the factors to be measured cannot be specified.

A wide range of opinions about sampling exists in the qualitative research community. The authors take issue with qualitative researchers who dismiss these as irrelevant or even as heretical concerns. The authors also disagree with those quantitative practitioners who dismiss concerns about qualitative sampling as irrelevant in general on the grounds that qualitative research provides no useful knowledge. It is suggested that such a position is untenable and uninformed.

This article focuses only on qualitative research; issues related to combined qualitative and quantitative methods are not discussed. The focus is on criteria for designing samples; qualitative issues related to suitability of any given person for research are not addressed. The criteria for designing samples constitute what Johnson (1990) labels as “Criteria One issues,” the construction and evaluation of theory and data-driven research designs. Criteria Two issues relate to the individual subjects in terms of cooperativeness, rapport, and suitability for qualitative study methods.

Although this article may appear to overly dichotomize qualitative and quantitative approaches, this was done strictly for the purposes of highlighting key issues in a brief space. The authors write here from the perspective of researchers who work extensively with both orientations, singly and in combination, in the conduct of major in-depth and longitudinal research grants that employ both methods. It is the authors' firm belief that good research requires an openness to multiple approaches to conceptualizing and measurement phenomena.

Contributions, Logic and Issues in Qualitative Sampling

Major contributions.

Attention to sampling issues has usually been at the heart of anthropology and of qualitative research since their inception. Much work was devoted to evaluating the appropriateness of theory, design strategies, and procedures for sampling. Important contributions have been made by research devoted to identifying and describing the nature of sample universes and the relevant analytic units for sampling. For example, the “universe of kinship” ( Goodenough 1956 ) has been a mainstay of cross-cultural anthropological study. Kinship studies aim to determine the fundamental culturally defined building blocks of social relationships of affiliation and descent (e.g., Bott 1971 ; Fortes 1969 ). Ethnographic investigations document the diversity of kinship structures, categories of kith and kin, and terminologies that give each culture across the globe its distinctive worldview, social structure, family organization, and patterns to individual experiences of the world.

Concerns with sampling in qualitative research focus on discovering the scope and the nature of the universe to be sampled. Qualitative researchers ask, “What are the components of the system or universe that must be included to provide a valid representation of it?” In contrast, quantitative designs focus on determining how many of what types of cases or observations are needed to reliably represent the whole system and to minimize both falsely identifying or missing existing relationships between factors. Thus the important contributions of qualitative work derived from concerns with validity and process may be seen as addressing core concerns of sampling, albeit in terms of issues less typically discussed by quantitative studies. Two examples may clarify this; one concerns time allocation studies of Peruvian farmers and the other addresses a census on Truk Island in the South Pacific.

The Andes mountains of Peru are home to communities of peasants who farm and tend small herds to garner a subsistence living. To help guide socioeconomic modernization and to improve living conditions, refined time allocation studies (see Gross 1984 ) were conducted in the 1970s to assess the rational efficiency of traditional patterns of labor, production, and reproduction. Seemingly irrational results were obtained. A systematic survey of how villagers allocated their time to various activities identified a few healthy adults who sat in the fields much of the day. Given the marginal food supplies, such “inactivity” seemed irrational and suggested a possible avenue for the desired interventions to improve village economic production. Only after interviewing the farmers to learn why the men sat in the fields and then calculating the kilocalories of foods gained by putting these men to productive work elsewhere was an explanation uncovered. It was discovered that crop yields and available calories would decline , not increase, due to foraging birds and animals. Because the farmers sat there, the events of animal foraging never occurred in the data universe. Here, judgments about the rationality of behaviors were guided by too narrow a definition of the behavioral universe, shaped by reliance on analytic factors external to the system (e.g., biases in industrial economies that equate “busyness” with production). An important message here is that discovery and definition of the sample universe and of relevant units of activity must precede sampling and analyses.

On Truk Island in the South Pacific, two anthropologists each conducted an independent census using the same methods. They surveyed every person in the community. Statistical analyses of these total universe samples were conducted to determine the incidence of types of residence arrangements for newlywed couples. The researchers reached opposite conclusions. Goodenough (1956) argued that his colleague's conclusion that there are no norms for where new couples locate their residence clearly erred by classifying households as patrilocal (near the father), matrilocal, or neolocal (not near either parent) at one time as if isolated from other social factors. Goodenough used the same residence typology as did his colleague in his analysis, but identified a strong matralineal pattern (wife's extended family). Evidence for this pattern becomes clear when the behaviors are viewed in relation to the extended family and over time. The newlyweds settle on whatever space is available but plan to move later to the more socially preferred (e.g., matralineal) sites. This later aspect was determined by combining survey-based observations of behavior with interviews to learn “what the devil they think they are doing” ( Geertz 1973 ). Thus different analytic definitions of domestic units led to opposite conclusions, despite the use of a sample of the total universe of people! Social constructions of the lived universe, subjectively important temporal factors have to be understood to identify valid units for analyses and interpretation of the data.

The Peruvian and the Truk Island examples illustrate some of the focal contributions of qualitative approaches to sampling. Altering the quantitatively oriented sampling interval, frequency, or duration would not have produced the necessary insights. The examples also suggest some of the dilemmas challenging sampling in qualitative research. These will be addressed in a later section. Both cases reveal the influence of deeply ingrained implicit cultural biases in the scientific construction of the sampling universe and the units for sampling.

The Cultural Embeddedness of the Concept of Sampling

Sampling issues are not exclusive to science. Widespread familiarity with sampling and related issues is indicated by the pervasive popular appetite for opinion and election polls, surveys of consumer product prices and quality, and brief reports of newsworthy scientific research in the mass media. Sampling issues are at the heart of jury selection, which aims to represent a cross section of the community; frequent debates erupt over how to define the universe of larger American society (e.g., by race and gender) to use for juror selection in a specific community. We can shop for sampler boxes of chocolates to get a tasty representation of the universe of all the candies from a company. Debates about the representativeness, size, and biases in survey results because of the people selected for study or the small size of samples are a part of everyday conversation. Newspapers frequently report on medical or social science research, with accounts of experts' challenging the composition or size of the sample or the wording of the survey questions. Critical skills in sampling are instilled during schooling and on-the-job training.

Such widespread familiarity with basic sampling issues suggests a deep cultural basis for the fascination and thus the need for a more critical understanding. The concept and practices of sampling resonate with fundamental cultural ideals and taboos. It is perhaps the case that sampling is linked, in American culture, to democratic ideals and notions of inclusion and representation.

What does that mean for qualitative researchers designing sampling strategies? We need to be aware that the language of science is ladened with cultural and moral categories. Thus gerontological research may potentially be shaped by both cultural themes masked as scientific principles. Basic terms for research standards can simultaneously apply to ideals for social life ( Luborsky 1994 ). We construct and are admonished by peers to carefully protect independent and dependent variables; we design studies to provide the greatest statistical power and speak of controlling variables. At the same time, psychosocial interventions are designed to enhance these same factors of individual independence and senses of power and control. We examine constructs and data to see if they are valid or invalid; the latter word also is defined in dictionaries as referring to someone who is not upright but physically deformed or sickly. Qualitative research, likewise, needs to recognize that we share with informants in the search for themes and coherence in life, and normatively judge the performance of others in these terms ( Luborsky 1994 , 1993b ).

The ideals of representativeness and proportionality are not, in practice, unambiguous or simple to achieve as is evidenced in the complex jury selection process. Indeed, there is often more than one way to achieve representativeness. Implicit cultural values may direct scientists to define some techniques as more desirable than others. Two current examples illustrate how sampling issues are the source of vitriolic debate outside the scientific community: voting procedures, and the construction or apportionment of voting districts to represent minority, ethnic, or racial groups. Representing “the voice of the people” in government is a core tenet of American democracy, embodied in the slogan “one person one vote.” Before women's suffrage, the universe was defined as “one man one vote.” A presidential nomination for U.S. Attorney General Dr. Lani Guinier, was withdrawn, in part, because she suggested the possibility of an alternative voting system (giving citizens more than one vote to cast) to achieve proportional representation for minorities. We see in these examples that to implement generalized democratic ideals of equal rights and representation can be problematic in the context of the democratic ideal of majority rule. Another example is the continuing debate in the U.S. Supreme Court over how to reapportion voting districts so as to include sufficient numbers of minority persons to give them a voice in local elections. These examples indicate the popular knowledge of sampling issues, the intensity of feelings about representativeness, and the deep dilemmas about proportional representation and biases arising within a democratic society. The democratic ideals produce multiple conflicts at the ideological level.

It is speculated that the association of sampling issues with such core American cultural dilemmas exacerbates the rancor between qualitative and quantitative gerontology; whereas in disciplines that do not deal with social systems, there is a tradition of interdependence instead of rancor. For example, the field of chemistry includes both qualitative and quantitative methods but is not beset by the tension found in gerontology. Qualitative chemistry is the set of methods specialized in identifying the types and entire range of elements and compounds present in materials or chemical reactions. A variety of discovery-oriented methods are used, including learning which elements are reacting with one another. Quantities of elements present may be described in general ranges as being from a trace to a substantial amount. Quantitative chemistry includes measurement-oriented methods attuned to determining the exact quantity of each constituent element present. Chemists use both methods as necessary to answer research problems. The differences in social contextual factors may contribute to the lower level of tension between quantitative and qualitative traditions within the European social sciences situated as they are within alternative systems for achieving democratic representation in government (e.g., direct plebiscites or multiparty governments rather than the American electoral college approach to a two-party system).

Ideals and Techniques of Qualitative Sampling

The preceding discussion highlighted the need to first identify the ideal or goal for sampling and second to examine the techniques and dilemmas for achieving the ideal. The following section describes several ideals, sampling techniques, and inherent dilemmas. Core ideals include the determination of the scope of the universe for study and the identification of appropriate analytic units when sampling for meaning

Defining the universe

This is simultaneously one of qualitative research's greatest contributions and greatest stumbling blocks to wider acceptance in the scientific community. As the examples of the Peruvian peasants and Trukese postmarital residence norms illustrated, qualitative approaches that can identify relevant units (e.g., of farming activity or cultural ideals for matralineal residence) are needed to complement behavioral or quantitative methods if we are to provide an internally valid definition of the scope of the universe to be sampled. Probability-based approaches do not capture these dimensions adequately.

The problem is that the very nature of such discovery-oriented techniques runs counter to customary quantitative design procedures. This needs to be clearly recognized. Because the nature of the units and their character cannot be specified ahead of time, but are to be discovered, the exact number and appropriate techniques for sampling cannot be stated at the design stage but must emerge during the process of conducting the research. One consequence is that research proposals and reports may appear incomplete or inadequate when in fact they are appropriately defined for qualitative purposes. One technique in writing research proposals has been to specify the likely or probable number of subjects to be interviewed.

Evidence that a researcher devoted sufficient attention to these issues can be observed in at least two dimensions. First, one finds a wealth of theoretical development of the concepts and topics. In qualitative research, these serve as the analytic tools for discovery and aid in anticipating new issues that emerge during the analyses of the materials. Second, because standardized measurement or diagnostic tests have not yet been developed for qualitative materials, a strong emphasis is placed on analytic or interpretive perspectives to the data collection and data analyses.

Expository styles, traditional in qualitative studies, present another dilemma for qualitative discussions of sampling. An impediment to wider recognition of what constitutes an adequate design is customary, implicit notions about the “proper” or traditional formats for writing research proposals and journal articles. The traditional format for grant applications places discussions of theory in the section devoted to the general significance of the research application separate from the methods and measures. However, theoretical issues and conceptual distinctions are the research tools and methods for qualitative researchers, equivalent to the quantitative researchers' standardized scales and measures. As the authors have observed it written reviews of grant applications over many years, reviewers want such “clutter” in qualitative documents placed where it belongs elsewhere in the proposal, not in the design section ( Rubinstein 1994 ). Qualitative researchers look for the analytic refinement, rigor, and breadth in conceptualization linked to the research procedures section as signs of a strong proposal or publication. Thus basic differences in scientific emphases, complicated by expectations for standardized scientific discourse, need to be more fully acknowledged.

Appropriate analytic units: Sampling for meaning

The logic or premises for qualitative sampling for meaning is incompletely understood in gerontology. Although it appears that, in the last decade, there has been an improved interdisciplinary acceptance and communication within gerontology, gerontology is largely driven by a sense of medicalization of social aging and a bias toward survey sampling and quantitative analysis based on “adequate numbers” for model testing and other procedures. At the same time, and partly in reaction to the dominance of the quantitative ethos, qualitative researchers have demurred from legitimating or addressing these issues in their own work.

Understanding the logic behind sampling for meaning in gerontological research requires an appreciation of how it differs from other approaches. By sampling for meaning, the authors indicate the selection of subjects in research that has as its goal the understanding of individuals' naturalistic perceptions of self, society, and the environment. Stated in another way, this is research that takes the insider's perspective. Meaning is defined as the process of reference and connotation, undertaken by individuals, to evoke key symbols, values, and ideas that shape, make coherent, and inform experience ( D'Andrade 1984 ; Good & Good 1982 ; Luborsky and Rubinstein 1987 ; Mishler 1986 ; Rubinstein 1990 ; Williams 1984 ). Clearly, the qualitative approach to meaning stands in marked contrast to other approaches to assessing meaning by virtue of its focus on naturalistic data and the discovery of the informant's own evaluations and categories. For example, one approach assesses meaning by using standardized lists of predefined adjectives or phrases (e.g., semantic differential scale methods, Osgood, Succi, and Tannenbaum 1957 ); another approach uses diagnostic markers to assign individuals to predefined general types (e.g., depressed, anxious) as a way to categorize people rather than describe personal meaning (e.g., the psychiatric diagnostic manual, DSMEI-R, APA 1987 ).

The difference between the me of that night and the me of tonight is the difference between the cadaver and the surgeon doing the cutting. (Flaubert, quoted in Crapanzano 1982 , p. 181)

It is important to understand that meanings and contexts (including an individual's sense of identity), the basic building blocks of qualitative research, are not fixed, constant objects with immutable traits. Rather, meanings and identities are fluid and changeable according to the situation and the persons involved. Gustave Flaubert precisely captures the sense of active personal meaning-making and remaking across time. Cohler (1991) describes such meaning-making and remaking as the personal life history self, a self that interprets, experiences, and marshals meanings as a means to manage adversity. A classic illustration of the fluidity of meanings is the case presented by Evans-Pritchard (1940) who explains the difficulty he had determining the names of his informants at the start of his fieldwork in Africa. He was repeatedly given entirely different names by the same people. In the kinship-based society, the name or identity one provides to another person depends on factors relative to each person's respective clan membership, age, and community. Now known as the principle of segmentary opposition, the situated and contextual nature of identities was illustrated once the fieldworker discovered the informants were indexing their names to provide an identity at an equal level of social organization. For example, to explain who we are when we travel outside the United States, we identify ourselves as Americans, not as someone from 1214 Oakdale Road. When we introduce ourselves to a new neighbor at a neighborhood block party, we identify ourselves by our apartment building or house on the block, not by reference to our identity as residents at the state or national level.

Themes and personal meanings are markers of processes not fixed structures. Life stories, whose narration is organized around a strongly held personal theme(s) as opposed to a chronology of events from birth to present day, have been linked with distress and clinical depression ( Luborsky 1993b ). Williams (1984) suggests that the experience of being ill from a chronic medical disease arises when the disease disrupts the expected trajectory of one's biography. Some researchers argue that a break in the sense of continuity in personal meaning ( Becker 1993 ), rather than any particular meaning (theme), precedes illness and depression ( Atchley 1988 ; Antonovsky 1987 ).

Another example of fluid meaning is ethnicity. Ethnic identity is a set of meanings that can be fluid and vary according to the social situation, historical time period, and its personal salience over the lifetime ( Luborsky and Rubinstein 1987 , 1990 ). Ethnic identity serves as a source of fixed, basic family values during child socialization; more fluidly, as an ascribed family identity to redefine or even reject as part of psychological processes of individuation in early adulthood; sometimes a source of social stigma in communities or in times of war with foreign countries (e.g., “being Italian” during World War II); and a source of continuity of meaning and pride in later life that may serve to help adapt to bereavement and losses.

From the qualitative perspective, there are a number of contrasts that emerge between sampling for meaning and more traditional, survey-style sampling, which has different goals. Those who are not familiar with the sampling-for-meaning approach often voice concerns over such aspects as size ( Lieberson 1992 ), adequacy and, most tellingly, purpose of the sampling. Why, for example, are sample sizes often relatively small? What is elicited and why? What is the relationship between meanings and other traditional categories of analyses, such as age, sex, class, social statuses, or particular diseases?

What is perhaps the most important contrast between the sampling-for-meaning approach and more standard survey sampling is found in the model of the person that underlies elicitation strategies. The model of the person in standard research suggests that important domains of life can be tapped by a relatively small number of standardized “one size fits all” questions, organized and presented in a scientific manner, and that most responses are relatively objective, capable of being treated as a decontextualized trait, and are quantifiable ( Mishler 1986 ; Trotter 1991 ). From this perspective, individuals are viewed as sets of fixed traits and not as carriers and makers of meaning.

Sampling for meaning, in contrast, is based on four very distinct notions. The first is that responses have contexts and carry referential meaning. Thus questions about events, activities, or other categories of experience cannot be understood without some consideration of how these events implicate other similar or contrasting events in a person's life ( Scheer and Luborsky 1991 ). This is particularly important for older people.

Second, individuals often actively interpret experience. That is to say, many people—but not all—actively work to consider their experience, put it in context, and understand it. Experience is not a fixed response. Further, the concern with meanings or of remaking meaning can be more emergent during some life stages and events or attention to certain kinds of meanings than others. Examples of this include bereavement, retirement, ethnic identity, and personal life themes in later life.

Third, certain categories of data do not have a separable existence apart from their occurrences embodied within routines and habits of the day and the body. Although certain categories of elicited data may have a relatively objective status and be relatively “at hand” for a person's stock of knowledge, other topics may never have been considered in a way that enables a person to have ready access to them ( Alexander, Rubinstein, Goodman, and Luborsky 1992 ). Consequently, qualitative research provides a context and facilitates a process of collaboration between researcher and informant.

Fourth, interpretation, either as natural for the informant or facilitated in the research interview, is basically an action of interpretation of experience that makes reference to both sociocultural standards, be they general cultural standards or local community ones, as well as the ongoing template or matrix of individual experience. Thus, for example, a person knows cultural ideals about a marriage, has some knowledge of other people's marriages, and has intimate knowledge of one's own. In the process of interpretation, all these levels come into play.

These issues occur over a variety of sampling frames and processing frameworks. There are three such sampling contexts. First, sampling for meaning occurs in relation to individuals as representatives of experiential types. Here, the goal is the elucidation of particular types of meaning or experience (personal, setting-based, sociocultural), through inquiry about, discussion of, and conversation concerning experiences and the interpretation of events and social occur-rences. The goal of sampling, in this case, is to produce collections of individuals from whom the nature of experience can be elicited through verbal descriptions and narrations.

Second, sampling for meaning can occur in the context of an individual in a defined social process. An example here could include understanding the entry of a person into a medical practice as a patient, for the treatment of a disorder. Qualitatively, we might wish to follow this person as she moves through medical channels, following referrals, tests, and the like. Even beginning this research at a single primary physician, or with a sample of individuals who have a certain disorder, the structure of passage through a processing system may vary widely and complexly. However, given a fixed point of entry (a medical practice or a single disease), sampling for meaning is nested in ongoing social processes. Researchers wish to understand not only the patient's experience of this setting as she moves through it (e.g., Esteroff 1982 ) but also the perspectives of the various social actors involved.

Finally, researchers may wish to consider sampling for meaning in a fixed social setting. In a certain way, sampling for meaning in a fixed social setting is what is meant, in anthropology and other social sciences, by “participant observation.” The social setting is more or less fixed, as is the population of research informants. An example might be a nursing home unit, with a more or less fixed number of residents, some stability but some change, and regular staff of several types representing distinctive organizational strata and interests (administration, medicine, nursing, social work, aides, volunteers, family, or environmental services).

It is important to note that even though qualitative research focuses on the individual, subjectivity or individuality is not the only goal of study. Qualitative research can focus on the macrolevel. One basic goal of qualitative research in aging is to describe the contents of people's experiences of life, health, and disability. It is true that much of the research to date treats the individual as the basic unit of analysis. Yet, the development of insights into the cultural construction of life experiences is an equal priority because cultural beliefs and values instill and shape powerful experiences, ideals, and motivations and shape how individuals make sense of and respond to events.

Studying how macrolevel cultural and community ideologies pattern the microlevel of individual life is part of a tradition stretching from Margaret Mead, Max Weber, Robert Merton, Talcott Parsons, to studies of physical and mental disabilities by Edgerton (1967) , Esteroff (1982) , and Murphy (1987) . For example, Stouffer's (1949) pioneering of survey methods revealed that American soldiers in World War II responded to the shared adversity of combat differently according to personal expectations based on sociocultural value patterns and lived experiences. These findings further illustrate Merton's theories of relative deprivation and reference groups, which point to the basis of individual well-being in basic processes of social comparison.

The notion of stigma illustrates the micro- and the macrolevels of analyses. For example, stigma theory's long reign in the social and political sciences and in clinical practice illustrates the micro- and macroqualitative perspectives. Stigma theory posits that individuals are socially marked or stigmatized by negative cultural evaluations because of visible differences or deformities, as defined by the community. Patterns of avoidance and denial of the disabled mark the socially conditioned feelings of revulsion, fear, or contagion. Personal experiences of low self-esteem result when negative messages are internalized by, for example, persons with visible impairments, or the elderly in an ageist setting. Management of social stigma by individuals and family is as much a focus as is management of impairments. Stigma is related significantly to compliance with prescribed adaptive devices ( Zola 1982 ; Luborsky 1993a ). A graphic case of this phenomenon are polio survivors who were homebound due to dependence on massive bedside artificial ventilators. With the recent advent of portable ventilators, polio survivors gained the opportunity to become mobile and travel outside the home, but they did not adopt the new equipment, because the new independence was far outweighed by the public stigma they experienced ( Kaufert and Locker 1990 ).

A final point is that sampling for meaning can also be examined in terms of sampling within the data collected. For example, the entire corpus of materials and observations with informants needs to be examined in the discovery and interpretive processes aimed at describing relevant units for analyses and dimensions of meaning. This is in contrast to reading the texts to describe and confirm a finding without then systematically rereading the texts for sections that may provide alternative or contradictory interpretations.

Techniques for selecting a sample

As discussed earlier, probability sampling techniques cannot be used for qualitative research by definition, because the members of the universe to be sampled are not known a priori, so it is not possible to draw elements for study in proportion to an as yet unknown distribution in the universe sampled. A review of the few qualitative research publications that treat sampling issues at greater length (e.g., Depoy and Gitlin 1993 ; Miles and Huberman 1994 ; Morse 1994 ; Ragin and Becker 1992 ) identify five major types of nonprobability sampling techniques for qualitative research. A consensus among these authors is found in the paramount importance they assign to theory to guide the design and selection of samples ( Platt 1992 ). These are briefly reviewed as follows.

First, convenience (or opportunistic) sampling is a technique that uses an open period of recruitment that continues until a set number of subjects, events, or institutions are enrolled. Here, selection is based on a first-come, first-served basis. This approach is used in studies drawing on predefined populations such as participants in support groups or medical clinics. Second, purposive sampling is a practice where subjects are intentionally selected to represent some explicit predefined traits or conditions. This is analogous to stratified samples in probability-based approaches. The goal here is to provide for relatively equal numbers of different elements or people to enable exploration and description of the conditions and meanings occurring within each of the study conditions. The objective, however, is not to determine prevalence, incidence, or causes. Third, snowballing or word-of-mouth techniques make use of participants as referral sources. Participants recommend others they know who may be eligible. Fourth, quota sampling is a method for selecting numbers of subjects to represent the conditions to be studied rather than to represent the proportion of people in the universe. The goal of quota sampling is to assure inclusion of people who may be underrepresented by convenience or purposeful sampling techniques. Fifth, case study ( Ragin and Becker 1992 ; Patton 1990 ) samples select a single individual, institution, or event as the total universe. A variant is the key-informant approach ( Spradley 1979 ), or intensity sampling ( Patton 1990 ) where a subject who is expert in the topic of study serves to provide expert information on the specialized topic. When qualitative perspectives are sought as part of clinical or survey studies, the purposive, quota, or case study sampling techniques are generally the most useful.

How many subjects is the perennial question. There is seldom a simple answer to the question of sample or cell size in qualitative research. There is no single formula or criterion to use. A “gold standard” that will calculate the number of people to interview is lacking (cf. Morse 1994 ). The question of sample size cannot be determined by prior knowledge of effect sizes, numbers of variables, or numbers of analyses—these will be reported as findings. Sample sizes in qualitative studies can only be set by reference to the specific aims and the methods of study, not in the abstract. The answer only emerges within a framework of clearly stated aims, methods, and goals and is conditioned by the availability of staff and economic resources.

Rough “rules of thumb” exist, but these derive from three sources: traditions within social science research studies of all kinds, commonsense ideas about how many will be enough, and practical concerns about how many people can be interviewed and analyzed in light of financial and personnel resources. In practice, from 12 to 26 people in each study cell seems just about right to most authors. In general, it should be noted that Americans have a propensity to define bigger as better and smaller as inferior. Quantitative researchers, in common with the general population, question such small sample sizes because they are habituated to opinion polls or epidemiology surveys based on hundreds or thousands of subjects. However, sample sizes of less than 10 are common in many quantitative clinical and medical studies where statistical power analyses are provided based on the existence of very large effect sizes for the experimental versus control conditions.

Other considerations in evaluating sample sizes are the resources, times, and reporting requirements. In anthropological field research, a customary formula is that of the one to seven: for every 1 year of fieldwork by one researcher, 7 years are required to conduct the analysis. Thus, in studies that use more than one interviewer, the ability to collect data also increases the burden for analyses.

An outstanding volume exploring the logic, contributions, and dilemmas of case study research ( Ragin and Becker 1992 ) reports that survey researchers resort to case examples to explain ambiguities in their data, whereas qualitative researchers reach for descriptive statistics when they do not have a clear explanation for their observations. Again, the choice of sample size and group design is guided by the qualitative goal of describing the nature and contents of cultural, social, and personal values and experiences within specific conditions or circumstances, rather than of determining incidence and prevalence.

Who and who not?

In the tradition of informant-based and of participatory research, it is assumed that all members of a community can provide useful information about the values, beliefs, or practices in question. Experts provide detailed, specialized information, whereas nonexperts do so about daily life. In some cases, the choice is obvious, dictated by the topic of study, for example, childless elderly, retirees, people with chronic diseases or new disabilities. In other cases, it is less obvious, as in studies of disease, for example, that require insights from sufferers but also from people not suffering to gain an understanding for comparison with the experiences and personal meanings of similar people without the condition. Comparisons can be either on a group basis or matched more closely on a one-to-one basis for many traits (e.g., age, sex, disease, severity), sometimes referred to as yoked pairs. However, given the labor-intensive nature of qualitative work, sometimes the rationale for including control groups of people who do not have the experiences is not justifiable.

Homogeneity or diversity

Currently, when constructing samples for single study groups, qualitative research appears to be about equally split in terms of seeking homogeneity or diversity. There is little debate or attention to these contrasting approaches. For example, some argue that it is more important to represent a wide range of different types of people and experiences in order to represent the similarities and diversity in human experience, beliefs, and conditions (e.g., Kaufman 1987 , 1989 ) than it is to include sufficient numbers of people sharing an experience or condition to permit evaluation of within-group similarities. In contrast, others select informants to be relatively homogeneous on several characteristics to strengthen comparability within the sample as an aid to identifying similarities and diversity.

Summary and Reformulation for Practice

To review, the authors suggest that explicit objective criteria to use for evaluating qualitative research designs do exist, but many of these focus on different issues and aspects of the research process, in comparison to issues for quantitative studies. This article has discussed the guiding principles, features, and practices of sampling in qualitative research. The guiding rationale is that of the discovery of the insider's view of cultural and personal meanings and experience. Major features of sampling in qualitative research concern the issues of identifying the scope of the universe for sampling and the discovery of valid units for analyses. The practices of sampling, in comparison to quantitative research, are rooted in the application of multiple conceptual perspectives and interpretive stances to data collection and analyses that allow the development and evaluation of a multitude of meanings and experiences.

This article noted that sampling concerns are widespread in American culture rather than in the esoteric specialized concern of scientific endeavors ( Luborsky and Sankar 1993 ). Core scientific research principles are also basic cultural ideals ( Luborsky 1994 ). For example, “control” (statistical, personal, machinery), dependence and independence (variables and individual), a reliable person with a valid driver's license matches reliability and validity concerns about assessment scales. Knowledge about the rudimentary principles of research sampling is widespread outside of the research laboratory, particularly with the relatively new popularity of economic, political, and community polls as a staple of news reporting and political process in democratic governance. Core questions about the size, sources, and features of participants are applied to construct research populations, courtroom juries, and districts to serve as electoral universes for politicians.

The cultural contexts and popular notions about sampling and sample size have an impact on scientific judgments. It is important to acknowledge the presence and influence of generalized social sensibilities or awareness about sampling issues. Such notions may have less direct impact on research in fields with long-established and formalized criteria and procedures for determining sample size and composition. The generalized social notions may come to exert a greater influence as one moves across the spectrum of knowledge-building strategies to more qualitative and humanistic approaches. Even though such studies also have a long history of clearly articulated traditions of formal critiques (e.g., in philosophy and literary criticism), they have not been amenable to operationalization and quantification.

The authors suggested that some of the rancor between qualitative and quantitative approaches is rooted in deeper cultural tensions. Prototypic questions posed to qualitative research in interdisciplinary settings derive from both the application of frameworks derived from other disciplines' approaches to sampling as well as those of the reviewers as persons socialized into the community where the study is conceived and conducted. Such concerns may be irrelevant or even counterproductive.

Qualitative Clarity as an Analog to Statistical Power

The guiding logic of qualitative research, by design, generally prevents it from being able to fulfill the assumptions underlying statistical power analyses of research designs. The discovery-oriented goals, use of meanings as units of analyses, and interpretive methods of qualitative research dictate that the exact factors, dimensions, and distribution of phenomena identified as important for analyses may not always be specified prior to data analyses activities. These emerge from the data analyses and are one of the major contributions of qualitative study. No standardized scales or tests exist yet to identify and describe new arenas of cultural, social, or personal meanings. Meaning does not conform to normative distributions by known factors. No probability models exist that would enable prediction of distributions of meanings needed to perform statistical power analyses.

Qualitative studies however can, and should, be judged in terms of how well they meet the explicit goals and purposes relevant to such research.

The authors have suggested that the concept of qualitative clarity be developed to guide evaluations of sampling as an analog to the concept of statistical power. Qualitative clarity refers to principles that are relevant to the concerns of this type of research. That is, the adequacy of the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation can be evaluated even if the factors to be measured cannot be specified. The term clarity conveys the aim of making explicit, for open discussion, the details of how the sample was assembled, the theoretical assumptions and the pragmatic constraints that influenced the sampling process. Qualitative clarity should include at least two components, theoretical grounding and sensitivity to context. These are briefly described next.

Rich and diverse theoretical grounding

In the absence of standardized measures for assessing meaning, the analogous qualitative research tools are theory and discovery processes. Strong and well-developed theoretical preparation is necessary to provide multiple and alternative interpretations of the data. Traditionally, in qualitative study, it is the richness and sophistication of the analytic perspectives or “lenses” focused on the data that lends richness, credibility, and validity to the analyses. The relative degree of theoretical development in a research proposal or manuscript is readily apparent in the text, for example, in terms of extended descriptions of different schools of thought and possible multiple contrasting of interpretive explanations for phenomena at hand. In brief, the authors argue that given the stated goal of sampling for meaning, qualitative research can be evaluated to assess if it has adequate numbers of conceptual perspectives that will enable the study to identify a variety of meanings and to critique multiple rich interpretations of the meanings.

Sampling within the data is another important design feature. The discovery of meaning should also include sampling within the data collected. The entire set of qualitative materials should be examined rather than selectively read after identifying certain parts of the text to describe and confirm a finding without reading for sections that may provide alternative or contradictory interpretations.

Sensitivity to contexts

As a second component of qualitative clarity, sensitivity to context refers to the contextual dimensions shaping the meanings studied. It also refers to the historical settings of the scientific concepts used to frame the research questions and the methods. Researchers need to be continually attentive to examining the meanings and categories discovered for elements from the researchers' own cultural and personal backgrounds. The first of these contexts is familiar to gerontologists: patterns constructed by the individual's life history; generation; cohort; psychological, developmental, and social structure; and health. Another more implicit contextual aspect to examine as part of the qualitative clarity analysis is evidence of a critical view of the methods and theories introduced by the investigators. Because discovery of the insiders' perspective on cultural and personal meanings is a goal of qualitative study, it is important to keep an eye to biases derived from the intrusion of the researcher's own scientific categories. Qualitative research requires a critical stance as to both the kinds of information and the meanings discovered, and to the analytic categories guiding the interpretations. One example is recent work that illustrates how traditional gerontological constructs for data collection and analyses do not correspond to the ways individuals themselves interpret their own activities, conditions, or label their identities (e.g., “caregiver,” Abel 1991 ; “disabled,” Murphy 1987 ; “old and alone,” Rubinstein, 1986 ; “Alzheimer's disease,” Gubrium 1992 ; “life themes,” Luborsky 1993b ). A second example is the growing awareness of the extent to which past research tended to define problems of disability or depression narrowly in terms of the individual's ability, or failure, to adjust, without giving adequate attention to the societal level sources of the individual's distress ( Cohen and Sokolovsky 1989 ). Thus researchers need to demonstrate an awareness of how the particular questions guiding qualitative research, the methods and styles of analyses, are influenced by cultural and historical settings of the research ( Luborsky and Sankar 1993 ) in order to keep clear whose meanings are being reported.

To conclude, our outline for the concept of qualitative clarity, which is intended to serve as the qualitatively appropriate analog to statistical power, is offered to gerontologists as a summary of the main points that need to be considered when evaluating samples for qualitative research. The descriptions of qualitative sampling in this article are meant to extend the discussion and to encourage the continued development of more explicit methods for qualitative research.

Acknowledgments

Support for the first author by the National Institute of Child Health and Human Development (#RO1 HD31526) and the National Institute on Aging (#RO1 AG09065) is gratefully acknowledged. Ongoing support for the second author from the National Institute of Aging is also gratefully acknowledged.

Biographies

Mark R. Luborsky, Ph.D., is a senior research anthropologist and assistant director of research at the Philadelphia Geriatric Center. Federal and foundation grants support his studies of sociocultural values and personal meanings in early and late adulthood, and how these relate to mental and physical health, and to disability and rehabilitation processes. He also consults and teaches on these topics.

Robert L. Rubinstein, Ph.D., is a senior research anthropologist and director of research at the Philadelphia Geriatric Center. He has conducted research in the United States and Vanuatu, South Pacific Islands. His gerontological research interests include social relations of the elderly, childlessness in later life, and the home environments of old people.

• Abel Emily. Who Cares for the Elderly. Temple University Press; Philadelphia: 1991. [ Google Scholar ]
• Alexander Baine, Rubinstein Robert, Goodman Marcene, Luborsky Mark. A Path Not Taken: A Cultural Analysis of Regrets and Childlessness in the Lives of Older Women. The Gerontologist. 1992; 32 (5):618–26. [ PubMed ] [ Google Scholar ]
• American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders DSMIII-R revised. APA; Washington, DC: 1987. [ Google Scholar ]
• Antonovsky Aaron. Unraveling the Mystery of Health. Jossey-Bass; San Francisco: 1987. [ Google Scholar ]
• Atchley Robert. A Continuity Theory of Aging. The Gerontologist. 1988; 29 (2):183–90. [ PubMed ] [ Google Scholar ]
• Becker Gaylene. Continuity After a Stroke: Implications of Life-Course Disruptions in Old Age. The Gerontologist. 1993; 33 (2):148–58. [ PubMed ] [ Google Scholar ]
• Bott Elizabeth. Family and Social Networks. Tavistock; London: 1971. [ Google Scholar ]
• Cohen Carl, Sokolovsky Jay. Old Men of the Bowery. Guilford; New York: 1989. [ Google Scholar ]
• Cohler Bertram. The Life Story and the Study of Resilience and Response to Adversity. Journal of Life History and Narrative. 1991; 1 (2&3):169–200. [ Google Scholar ]
• Crapanzano Vincent. The Self, the Third, and Desire. In: Lee B, editor. Psychosocial Theories of the Self. Plenum; New York: 1982. [ Google Scholar ]
• D'Andrade Roy. Cultural Meaning Systems. In: Shweder R, LeVine R, editors. Culture Theory: Essays on Mind, Self, and Emotion. Cambridge Press; New York: 1984. [ Google Scholar ]
• Denzin Norman, Lincoln Yolanda. Handbook of Qualitative Research. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• DePoy Elizabeth, Gitlin Laura. Introduction to Research: Multiple Strategies for Health and Human Services. Mosby; St. Louis, MO: 1993. [ Google Scholar ]
• Edgerton Robert. The Cloak of Competence. University of California Press; Berkeley: 1967. [ Google Scholar ]
• Esteroff Susan. Making It Crazy: An Ethnography of Psychiatric Patients in an American Community. University of California Press; Berkeley: 1982. [ Google Scholar ]
• Evans-Pritchard Edmund E. The Nuer: A Description of the Livelihood and Political Institutions of a Nilotic People. Cambridge University Press; Cambridge, England: 1940. [ Google Scholar ]
• Fortes Meyer. Kinship and the Social Order. Aldine; Chicago: 1969. [ Google Scholar ]
• Geertz Clifford. The Interpretation of Culture. Basic Books; New York: 1973. [ Google Scholar ]
• Good Byron, Good Mary-Jo Delveechio. Toward a Meaning-Centered Analysis of Popular Illness Categories. In: Marsella A, White G, editors. Cultural Conceptions of Mental Health and Therapy. Reidel; Dordrecht, Holland: 1982. [ Google Scholar ]
• Goodenough Ward. Residence Rules. Southwestern Journal of Anthropology. 1956; 12 (1):22–37. [ Google Scholar ]
• Gross Daniel. Time Allocation: A Tool for the Study of Cultural Behavior. Annual Review of Anthropology. 1984; 13 :519–58. [ Google Scholar ]
• Gubrium Jay. The Mosaic of Care. Springer; New York: 1992. [ Google Scholar ]
• Gubrium Jaber, Sankar Andrea. Qualitative Methods in Aging Research. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• Johnson John. Selecting Ethnographic Informants. Sage; Thousand Oaks, CA: 1990. [ Google Scholar ]
• Kaufert Joseph, Locker David. Rehabilitation Ideology and Respiratory Support Technology. Social Science and Medicine. 1990; 30 (8):867–77. [ PubMed ] [ Google Scholar ]
• Kaufman Sharon. The Ageless Self: Sources of Meaning in Late Life. University of Wisconsin Press; Madison: 1987. [ Google Scholar ]
• Kaufman Sharon. Long-Term Impact of Injury on Individuals, Families, and Society: Personal Narratives and Policy Implications. In: Rich D, MacKenzie Ellen, Associates, editors. Cost of Injury in the United States: A Report to Congress. Institute for Health and Aging, University of California Press; Injury Prevention Center, Johns Hopkins University Press; San Francisco, CA: 1989. [ Google Scholar ]
• Lieberson Stanley. Small N's and Big Conclusions. In: Ragin C, Becker H, editors. What is a Case? Cambridge University Press; Cambridge, England: 1992. [ Google Scholar ]
• Luborsky Mark. Sociocultural Factors Shaping Technology Usage: Fulfilling the Promise. Technology and Disability. 1993a; 2 (1):71–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Luborsky Mark. The Romance With Personal Meaning in Gerontology: Cultural Aspects of Life Themes. The Gerontologist. 1993b; 33 (4):445–52. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Luborsky Mark. The Identification and Analysis of Themes and Patterns. In: Gubrium J, Sankar A, editors. Qualitative Methods in Aging Research. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• Luborsky Mark, Rubinstein Robert. Ethnicity and Lifetimes: Self Concepts and Situational Contexts of Ethnic Identity in Late Life. In: Gelfand D, Barresi C, editors. Ethnic Dimensions of Aging. Springer; New York: 1987. [ Google Scholar ]
• Luborsky Mark, Rubinstein Robert. Ethnic Identity and Bereavement in Later Life: The Case of Older Widowers. In: Sokolovsky J, editor. The Cultural Context of Aging: Worldwide Perspectives. Bergin and Garvey; New York: 1990. [ Google Scholar ]
• Luborsky Mark, Sankar Andrea. Extending the Critical Gerontology Perspective: Cultural Dimensions. The Gerontologist. 1993; 33 (4):440–4. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mead Margaret. National Character. In: Kroeber A, editor. Anthropology Today. University of Chicago Press; Chicago: 1953. [ Google Scholar ]
• Miles M, Huberman A. Qualitative Data Analysis. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• Mishler Elliott. Research Interviewing. Harvard University Press; Cambridge, MA: 1986. [ Google Scholar ]
• Morse Janet. Designing Funded Qualitative Research. In: Denzin N, Lincoln Y, editors. Handbook of Qualitative Research. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• Murphy Robert. The Body Silent. Columbia University Press; New York: 1987. [ Google Scholar ]
• Osgood Charles, Succi G, Tannenbaum P. The Measurement of Meaning. University of Illinois Press; Urbana: 1957. [ Google Scholar ]
• Patton Michael. Qualitative Evaluation and Research Methods. Sage; Thousand Oaks, CA: 1990. [ Google Scholar ]
• Pelto Peter, Pelto Gertrude. Anthropological Research: The Structure of Inquiry. 2nd ed. Cambridge University Press; Cambridge, England: 1978. [ Google Scholar ]
• Platt Joseph. Cases of Cases. In: Ragin C, Becker H, editors. What is a Case? Cambridge University Press; Cambridge, England: 1992. [ Google Scholar ]
• Ragin Charles, Becker Howard. What is a Case?: Exploring the Foundations of Social Inquiry. Cambridge University Press; Cambridge, England: 1992. [ Google Scholar ]
• Rubinstein Robert. Singular Paths: Old Men Living Alone. Columbia University Press; New York: 1986. [ Google Scholar ]
• Rubinstein Robert. The Environmental Representation of Personal Themes by Older People. Journal of Aging Studies. 1990; 4 (2):131–8. [ Google Scholar ]
• Rubinstein Robert. Proposal Writing. In: Gubrium J, Sankar A, editors. Qualitative Research Methods in Aging Research. Sage; Thousand Oaks, CA: 1994. [ Google Scholar ]
• Scheer Jessica, Luborsky Mark. The Cultural Context of Polio Biographies. Orthopedics. 1991; 14 (11):1173–81. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Spradley James. The Ethnographic Interview. Holt, Rinehart & Winston; New York: 1979. [ Google Scholar ]
• Spradley James. Participant Observation. Holt, Rinehart & Winston; New York: 1980. [ Google Scholar ]
• Stouffer SA. The American Soldier. Vols. 1 & 2. Wiley; New York: 1949. 1965. [ Google Scholar ]
• Strauss Anselm, Corbin Juliet. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage; Thousand Oaks, CA: 1990. [ Google Scholar ]
• Trotter Robert. Ethnographic Research Methods for Applied Medical Anthropology. In: Hill C, editor. Training Manual in Applied Medical Anthropology. American Anthropological Association; Washington, DC: 1991. [ Google Scholar ]
• Werner Oswald, Schoepfle George. Systematic Fieldwork. Vols. 1 & 2. Sage; Thousand Oaks, CA: 1987. [ Google Scholar ]
• Williams Gareth. The Genesis of Chronic Illness: Narrative Reconstruction. Sociology of Health and Illness. 1984; 6 (2):175–200. [ PubMed ] [ Google Scholar ]
• Zola Irving. Missing Pieces: Chronicle of Living With a Disability. Temple University Press; Philadephia: 1982. [ Google Scholar ]

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Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

Student resources, research proposal tools and sample student proposals.

Sample research proposals written by doctoral students in each of the key areas covered in Research Design --quantitative, qualitative, and mixed methods—are provided as a useful reference. A Research Proposal checklist also serves to help guide your own proposal-writing.

›   Morales Proposal_Qualitative Study

›   Kottich Proposal_Quantitative Study

›   Guetterman Proposal_Mixed Methods Study

›   Research Proposal Checklist