Sago

What We Offer

With a comprehensive suite of qualitative and quantitative capabilities and 55 years of experience in the industry, Sago powers insights through adaptive solutions.

  • Recruitment
  • Communities
  • Methodify® Automated research
  • QualBoard® Digital Discussions
  • QualMeeting® Digital Interviews
  • Global Qualitative
  • Global Quantitative
  • In-Person Facilities
  • Research Consulting
  • Europe Solutions
  • Clinical Research
  • Human Factors
  • Neuromarketing Tools
  • Trial & Jury Consulting

Who We Serve

Form deeper customer connections and make the process of answering your business questions easier. Sago delivers unparalleled access to the audiences you need through adaptive solutions and a consultative approach.

  • Consumer Packaged Goods
  • Financial Services
  • Media Technology
  • Marketing Research

With a 55-year legacy of impact, Sago has proven we have what it takes to be a long-standing industry leader and partner. We continually advance our range of expertise to provide our clients with the highest level of confidence.​

  • Global Offices
  • Partnerships & Certifications
  • News & Media
  • Researcher Events

Steve Schlesinger, Quirks Lifetime Achievement Award

Sago Executive Chairman Steve Schlesinger to Receive Quirk’s Lifetime Achievement Award

16 ways to prepare for the next holiday campaign

16 Ways to Prepare for the Next Holiday Campaign

electric vehicle panel at sago

Sago Unveils Electric Vehicle Panel to Drive Industry Success

Drop into your new favorite insights rabbit hole and explore content created by the leading minds in market research.

  • Case Studies
  • Knowledge Kit

hands holding phone and touching page with data on it

Steps to High-Quality Data: Mitigating the Challenges of Data Quality in Quantitative Research

How Leading Brands Use Insights to Win Market Share

How Leading Brands Use Insights to Win Market Share

Get in touch

types of sampling methods for qualitative research

  • Account Logins

types of sampling methods for qualitative research

Different Types of Sampling Techniques in Qualitative Research

  • Resources , Blog

clock icon

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 is 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 used in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques used 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.

Request a consultation

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 used 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 commonly used in qualitative research. 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 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, 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.

Find the Right Sample for Your Qualitative Research

Trust our team to recruit the participants you need using the appropriate techniques. Book a consultation with our team to get started .

people standing in line to vote

The Swing Voter Project in Michigan – February 2024

the deciders january 2024

The Deciders January 2024: Trump-Voting Women Who Oppose Dobbs

new years resolutions

The State of New Year’s Resolutions: A Closer Look at Mindsets and Habits

screener best practices

It’s Time to Change the Way We Write Screeners

A Closer Look at New Year’s Resolutions

A Closer Look at New Year’s Resolutions

swing voters project january 2024

The Swing Voter Project in Nevada – January 2024

recruiting participants webinar

OnDemand: The Secret Sauce to Finding Perfect Participants: Strategies for Recruiting Hard-to-Reach Audiences

hybrid research qualboard quallink

Unlock the Power of Hybrid Research with QualLink and External Survey Link

mod minutes webinar feb 2024

OnDemand: Moderator Minutes: Capitalize on the Power of Visual Collaboration

market research recruitment

Market Research Recruitment: Which Approach Is Right for You?

Take a deep dive into your favorite market research topics

types of sampling methods for qualitative research

How can we help support you and your research needs?

types of sampling methods for qualitative research

BEFORE YOU GO

Have you considered how to harness AI in your research process? Check out our on-demand webinar for everything you need to know

types of sampling methods for qualitative research

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Methodology
  • Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

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

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

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

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

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

Table of contents

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

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

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

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

Population vs sample

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

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

Sampling frame

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

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

Sample size

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

Prevent plagiarism, run a free check.

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

There are four main types of probability sample.

Probability sampling

1. Simple random sampling

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

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

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

2. Systematic sampling

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

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

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

3. Stratified sampling

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

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

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

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

4. Cluster sampling

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

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

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

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

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

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

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

Non probability sampling

1. Convenience sampling

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

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

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

2. Voluntary response sampling

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

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

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

3. Purposive sampling

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

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

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

4. Snowball sampling

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

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

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Sampling Methods | Types, Techniques, & Examples. Scribbr. Retrieved 26 February 2024, from https://www.scribbr.co.uk/research-methods/sampling/

Is this article helpful?

Shona McCombes

Shona McCombes

Other students also liked, what is quantitative research | definition & methods, a quick guide to experimental design | 5 steps & examples, controlled experiments | methods & examples of control.

Logo for Open Library Publishing Platform

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

10.2 Sampling in qualitative research

Learning objectives.

  • Define nonprobability sampling, and describe instances in which a researcher might choose a nonprobability sampling technique
  • Describe the different types of nonprobability samples

Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of whatever phenomenon it is that they are studying. In this section, we’ll examine the techniques that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability sampling

Nonprobability sampling refers to sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample is truly representative of a larger population. But that’s okay. Generalizing to a larger population is not the goal with nonprobability samples or qualitative research. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind (that would mean committing one of the errors of informal inquiry discussed in Chapter 1). We’ll take a closer look at the process of selecting research elements when drawing a nonprobability sample. But first, let’s consider why a researcher might choose to use a nonprobability sample.

two people filling out a clipboard survey in a crowd of people

When are nonprobability samples ideal? One instance might be when we’re starting a big research project. For example, if we’re conducting survey research, we may want to administer a draft of our survey to a few people who seem to resemble the folks we’re interested in studying in order to help work out kinks in the survey. We might also use a nonprobability sample if we’re conducting a pilot study or some exploratory research. This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research, even quantitative research. But it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques. Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding.

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods, we include them in this section of the chapter.

To draw a purposive sample , a researcher selects participants from their sampling frame because they have characteristics that the researcher desires. A researcher begins with specific characteristics in mind that she wishes to examine and then seeks out research participants who cover that full range of characteristics. For example, if you are studying mental health supports on your campus, you may want to be sure to include not only students, but mental health practitioners and student affairs administrators. You might also select students who currently use mental health supports, those who dropped out of supports, and those who are waiting to receive supports. The purposive part of purposive sampling comes from selecting specific participants on purpose because you already know they have characteristics—being an administrator, dropping out of mental health supports—that you need in your sample.

Note that these are different than inclusion criteria, which are more general requirements a person must possess to be a part of your sample. For example, one of the inclusion criteria for a study of your campus’ mental health supports might be that participants had to have visited the mental health center in the past year. That is different than purposive sampling. In purposive sampling, you know characteristics of individuals and recruit them because of those characteristics. For example, I might recruit Jane because she stopped seeking supports this month, JD because she has worked at the center for many years, and so forth.

Also, it’s important to recognize that purposive sampling requires you to have prior information about your participants before recruiting them because you need to know their perspectives or experiences before you know whether you want them in your sample. This is a common mistake that many students make. What I often hear is, “I’m using purposive sampling because I’m recruiting people from the health center,” or something like that. That’s not purposive sampling. Purposive sampling is recruiting specific people because of the various characteristics and perspectives they bring to your sample. Imagine we were creating a focus group. A purposive sample might gather clinicians, patients, administrators, staff, and former patients together so they can talk as a group. Purposive sampling would seek out people that have each of those attributes.

Quota sampling is another nonprobability sampling strategy that takes purposive sampling one step further. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation. Subgroups are created based on each category, and the researcher decides how many people to include from each subgroup and collects data from that number for each subgroup. Let’s consider a study of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves and instead eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each of the four subgroups.

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The leading polling entity at the time, The Literary Digest, predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide, but Gallup’s polling disagreed. Gallup successfully predicted Roosevelt’s win and subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election.  [1] Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007)  [2] underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods.  [3] While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings. For that, you need probability sampling, which we will discuss in the next section.

Qualitative researchers can also use snowball sampling techniques to identify study participants. In snowball sampling , a researcher identifies one or two people she’d like to include in her study but then relies on those initial participants to help identify additional study participants. Thus, the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow. Snowball sampling is an especially useful strategy when a researcher wishes to study a stigmatized group or behavior. For example, a researcher who wanted to study how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting a call for interviewees in the newspaper or making an announcement about the study at some large social gathering. Instead, the researcher might know someone with the condition, interview that person, and ask the person to refer others they may know with the genital herpes to contact you to participate in the study. Having a previous participant vouch for the researcher may help new potential participants feel more comfortable about being included in the study.

a person pictured next to a network of associates and their interrelationships noted through lines connecting the photos

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. In addition to using this sampling strategy for potentially stigmatized populations, it is also a useful strategy to use when the researcher’s group of interest is likely to be difficult to find, not only because of some stigma associated with the group, but also because the group may be relatively rare. This was the case for Steven Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011)  [4] who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received $50 for participating in the study and an additional $20 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Finally, convenience sampling is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which she has most convenient access. This method, also sometimes referred to as availability sampling, is most useful in exploratory research or in student projects in which probability sampling is too costly or difficult. If you’ve ever been interviewed by a fellow student for a class project, you have likely been a part of a convenience sample. While convenience samples offer one major benefit—convenience—they do not offer the rigor needed to make conclusions about larger populations. That is the subject of our next section on probability sampling.

Key Takeaways

  • Nonprobability samples might be used when researchers are conducting qualitative (or idiographic) research, exploratory research, student projects, or pilot studies.
  • There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.
  • Convenience sample- researcher gathers data from whatever cases happen to be convenient
  • Nonprobability sampling- sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown
  • Purposive sample- when a researcher seeks out participants with specific characteristics
  • Quota sample- when a researcher selects cases from within several different subgroups
  • Snowball sample- when a researcher relies on participant referrals to recruit new participants

Image attributions

business by helpsg CC-0

network by geralt CC-0

  • For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm. ↵
  • Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. ↵
  • If you are interested in the history of polling, I recommend reading Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. ↵
  • Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research , 26 , 30–60. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Book cover

Principles of Social Research Methodology pp 415–426 Cite as

Sampling Techniques for Qualitative Research

  • Heather Douglas 4  
  • First Online: 27 October 2022

2082 Accesses

2 Citations

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.

  • Phenomenon. Methodology. Research Question. Methods. Tools and Techniques. Purposive Sampling. Sampling Frame. Trustworthiness

This is a preview of subscription content, log in via an institution .

Buying options

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

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.

Chapter   Google Scholar  

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

Article   Google Scholar  

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.

Google Scholar  

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.

Download references

Author information

Authors and affiliations.

The University of Queensland, The Royal Society of Queensland, Activation Australia, Brisbane, Australia

Heather Douglas

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Heather Douglas .

Editor information

Editors and affiliations.

Centre for Family and Child Studies, Research Institute of Humanities and Social Sciences, University of Sharjah, Sharjah, United Arab Emirates

M. Rezaul Islam

Department of Development Studies, University of Dhaka, Dhaka, Bangladesh

Niaz Ahmed Khan

Department of Social Work, School of Humanities, University of Johannesburg, Johannesburg, South Africa

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter.

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

Download citation

DOI : https://doi.org/10.1007/978-981-19-5441-2_29

Published : 27 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5219-7

Online ISBN : 978-981-19-5441-2

eBook Packages : Social Sciences

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research
  • Subject List
  • Take a Tour
  • For Authors
  • Subscriber Services
  • Publications
  • African American Studies
  • African Studies
  • American Literature
  • Anthropology
  • Architecture Planning and Preservation
  • Art History
  • Atlantic History
  • Biblical Studies
  • British and Irish Literature
  • Childhood Studies
  • Chinese Studies
  • Cinema and Media Studies
  • Communication
  • Criminology
  • Environmental Science
  • Evolutionary Biology
  • International Law
  • International Relations
  • Islamic Studies
  • Jewish Studies
  • Latin American Studies
  • Latino Studies
  • Linguistics
  • Literary and Critical Theory
  • Medieval Studies
  • Military History
  • Political Science
  • Public Health
  • Renaissance and Reformation
  • Social Work
  • Urban Studies
  • Victorian Literature
  • Browse All Subjects

How to Subscribe

  • Free Trials

In This Article Expand or collapse the "in this article" section Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Introduction.

  • Sampling Strategies
  • Sample Size
  • Qualitative Design Considerations
  • Discipline Specific and Special Considerations
  • Sampling Strategies Unique to Mixed Methods Designs

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

  • Mixed Methods Research
  • Qualitative Research Design
  • Quantitative Research Designs in Educational Research

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

  • Gender, Power, and Politics in the Academy
  • Non-Formal & Informal Environmental Education
  • Motherscholars
  • Find more forthcoming articles...
  • Export Citations
  • Share This Facebook LinkedIn Twitter

Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.

Sampling in Qualitative Research

Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

  • About Education »
  • Meet the Editorial Board »
  • Academic Achievement
  • Academic Audit for Universities
  • Academic Freedom and Tenure in the United States
  • Action Research in Education
  • Adjuncts in Higher Education in the United States
  • Administrator Preparation
  • Adolescence
  • Advanced Placement and International Baccalaureate Courses
  • Advocacy and Activism in Early Childhood
  • African American Racial Identity and Learning
  • Alaska Native Education
  • Alternative Certification Programs for Educators
  • Alternative Schools
  • American Indian Education
  • Animals in Environmental Education
  • Art Education
  • Artificial Intelligence and Learning
  • Assessing School Leader Effectiveness
  • Assessment, Behavioral
  • Assessment, Educational
  • Assessment in Early Childhood Education
  • Assistive Technology
  • Augmented Reality in Education
  • Beginning-Teacher Induction
  • Bilingual Education and Bilingualism
  • Black Undergraduate Women: Critical Race and Gender Perspe...
  • Blended Learning
  • Case Study in Education Research
  • Changing Professional and Academic Identities
  • Character Education
  • Children’s and Young Adult Literature
  • Children's Beliefs about Intelligence
  • Children's Rights in Early Childhood Education
  • Citizenship Education
  • Civic and Social Engagement of Higher Education
  • Classroom Learning Environments: Assessing and Investigati...
  • Classroom Management
  • Coherent Instructional Systems at the School and School Sy...
  • College Admissions in the United States
  • College Athletics in the United States
  • Community Relations
  • Comparative Education
  • Computer-Assisted Language Learning
  • Computer-Based Testing
  • Conceptualizing, Measuring, and Evaluating Improvement Net...
  • Continuous Improvement and "High Leverage" Educational Pro...
  • Counseling in Schools
  • Critical Approaches to Gender in Higher Education
  • Critical Perspectives on Educational Innovation and Improv...
  • Critical Race Theory
  • Crossborder and Transnational Higher Education
  • Cross-National Research on Continuous Improvement
  • Cross-Sector Research on Continuous Learning and Improveme...
  • Cultural Diversity in Early Childhood Education
  • Culturally Responsive Leadership
  • Culturally Responsive Pedagogies
  • Culturally Responsive Teacher Education in the United Stat...
  • Curriculum Design
  • Data Collection in Educational Research
  • Data-driven Decision Making in the United States
  • Deaf Education
  • Desegregation and Integration
  • Design Thinking and the Learning Sciences: Theoretical, Pr...
  • Development, Moral
  • Dialogic Pedagogy
  • Digital Age Teacher, The
  • Digital Citizenship
  • Digital Divides
  • Disabilities
  • Distance Learning
  • Distributed Leadership
  • Doctoral Education and Training
  • Early Childhood Education and Care (ECEC) in Denmark
  • Early Childhood Education and Development in Mexico
  • Early Childhood Education in Aotearoa New Zealand
  • Early Childhood Education in Australia
  • Early Childhood Education in China
  • Early Childhood Education in Europe
  • Early Childhood Education in Sub-Saharan Africa
  • Early Childhood Education in Sweden
  • Early Childhood Education Pedagogy
  • Early Childhood Education Policy
  • Early Childhood Education, The Arts in
  • Early Childhood Mathematics
  • Early Childhood Science
  • Early Childhood Teacher Education
  • Early Childhood Teachers in Aotearoa New Zealand
  • Early Years Professionalism and Professionalization Polici...
  • Economics of Education
  • Education For Children with Autism
  • Education for Sustainable Development
  • Education Leadership, Empirical Perspectives in
  • Education of Native Hawaiian Students
  • Education Reform and School Change
  • Educational Statistics for Longitudinal Research
  • Educator Partnerships with Parents and Families with a Foc...
  • Emotional and Affective Issues in Environmental and Sustai...
  • Emotional and Behavioral Disorders
  • Environmental and Science Education: Overlaps and Issues
  • Environmental Education
  • Environmental Education in Brazil
  • Epistemic Beliefs
  • Equity and Improvement: Engaging Communities in Educationa...
  • Equity, Ethnicity, Diversity, and Excellence in Education
  • Ethical Research with Young Children
  • Ethics and Education
  • Ethics of Teaching
  • Ethnic Studies
  • Evidence-Based Communication Assessment and Intervention
  • Family and Community Partnerships in Education
  • Family Day Care
  • Federal Government Programs and Issues
  • Feminization of Labor in Academia
  • Finance, Education
  • Financial Aid
  • Formative Assessment
  • Future-Focused Education
  • Gender and Achievement
  • Gender and Alternative Education
  • Gender-Based Violence on University Campuses
  • Gifted Education
  • Global Mindedness and Global Citizenship Education
  • Global University Rankings
  • Governance, Education
  • Grounded Theory
  • Growth of Effective Mental Health Services in Schools in t...
  • Higher Education and Globalization
  • Higher Education and the Developing World
  • Higher Education Faculty Characteristics and Trends in the...
  • Higher Education Finance
  • Higher Education Governance
  • Higher Education Graduate Outcomes and Destinations
  • Higher Education in Africa
  • Higher Education in China
  • Higher Education in Latin America
  • Higher Education in the United States, Historical Evolutio...
  • Higher Education, International Issues in
  • Higher Education Management
  • Higher Education Policy
  • Higher Education Research
  • Higher Education Student Assessment
  • High-stakes Testing
  • History of Early Childhood Education in the United States
  • History of Education in the United States
  • History of Technology Integration in Education
  • Homeschooling
  • Inclusion in Early Childhood: Difference, Disability, and ...
  • Inclusive Education
  • Indigenous Education in a Global Context
  • Indigenous Learning Environments
  • Indigenous Students in Higher Education in the United Stat...
  • Infant and Toddler Pedagogy
  • Inservice Teacher Education
  • Integrating Art across the Curriculum
  • Intelligence
  • Intensive Interventions for Children and Adolescents with ...
  • International Perspectives on Academic Freedom
  • Intersectionality and Education
  • Knowledge Development in Early Childhood
  • Leadership Development, Coaching and Feedback for
  • Leadership in Early Childhood Education
  • Leadership Training with an Emphasis on the United States
  • Learning Analytics in Higher Education
  • Learning Difficulties
  • Learning, Lifelong
  • Learning, Multimedia
  • Learning Strategies
  • Legal Matters and Education Law
  • LGBT Youth in Schools
  • Linguistic Diversity
  • Linguistically Inclusive Pedagogy
  • Literacy Development and Language Acquisition
  • Literature Reviews
  • Mathematics Identity
  • Mathematics Instruction and Interventions for Students wit...
  • Mathematics Teacher Education
  • Measurement for Improvement in Education
  • Measurement in Education in the United States
  • Meta-Analysis and Research Synthesis in Education
  • Methodological Approaches for Impact Evaluation in Educati...
  • Methodologies for Conducting Education Research
  • Mindfulness, Learning, and Education
  • Multiliteracies in Early Childhood Education
  • Multiple Documents Literacy: Theory, Research, and Applica...
  • Multivariate Research Methodology
  • Museums, Education, and Curriculum
  • Music Education
  • Narrative Research in Education
  • Native American Studies
  • Note-Taking
  • Numeracy Education
  • One-to-One Technology in the K-12 Classroom
  • Online Education
  • Open Education
  • Organizing for Continuous Improvement in Education
  • Organizing Schools for the Inclusion of Students with Disa...
  • Outdoor Play and Learning
  • Outdoor Play and Learning in Early Childhood Education
  • Pedagogical Leadership
  • Pedagogy of Teacher Education, A
  • Performance Objectives and Measurement
  • Performance-based Research Assessment in Higher Education
  • Performance-based Research Funding
  • Phenomenology in Educational Research
  • Philosophy of Education
  • Physical Education
  • Podcasts in Education
  • Policy Context of United States Educational Innovation and...
  • Politics of Education
  • Portable Technology Use in Special Education Programs and ...
  • Post-humanism and Environmental Education
  • Pre-Service Teacher Education
  • Problem Solving
  • Productivity and Higher Education
  • Professional Development
  • Professional Learning Communities
  • Program Evaluation
  • Programs and Services for Students with Emotional or Behav...
  • Psychology Learning and Teaching
  • Psychometric Issues in the Assessment of English Language ...
  • Qualitative Data Analysis Techniques
  • Qualitative, Quantitative, and Mixed Methods Research Samp...
  • Queering the English Language Arts (ELA) Writing Classroom
  • Race and Affirmative Action in Higher Education
  • Reading Education
  • Refugee and New Immigrant Learners
  • Relational and Developmental Trauma and Schools
  • Relational Pedagogies in Early Childhood Education
  • Reliability in Educational Assessments
  • Religion in Elementary and Secondary Education in the Unit...
  • Researcher Development and Skills Training within the Cont...
  • Research-Practice Partnerships in Education within the Uni...
  • Response to Intervention
  • Restorative Practices
  • Risky Play in Early Childhood Education
  • Scale and Sustainability of Education Innovation and Impro...
  • Scaling Up Research-based Educational Practices
  • School Accreditation
  • School Choice
  • School Culture
  • School District Budgeting and Financial Management in the ...
  • School Improvement through Inclusive Education
  • School Reform
  • Schools, Private and Independent
  • School-Wide Positive Behavior Support
  • Science Education
  • Secondary to Postsecondary Transition Issues
  • Self-Regulated Learning
  • Self-Study of Teacher Education Practices
  • Service-Learning
  • Severe Disabilities
  • Single Salary Schedule
  • Single-sex Education
  • Single-Subject Research Design
  • Social Context of Education
  • Social Justice
  • Social Network Analysis
  • Social Pedagogy
  • Social Science and Education Research
  • Social Studies Education
  • Sociology of Education
  • Standards-Based Education
  • Statistical Assumptions
  • Student Access, Equity, and Diversity in Higher Education
  • Student Assignment Policy
  • Student Engagement in Tertiary Education
  • Student Learning, Development, Engagement, and Motivation ...
  • Student Participation
  • Student Voice in Teacher Development
  • Sustainability Education in Early Childhood Education
  • Sustainability in Early Childhood Education
  • Sustainability in Higher Education
  • Teacher Beliefs and Epistemologies
  • Teacher Collaboration in School Improvement
  • Teacher Evaluation and Teacher Effectiveness
  • Teacher Preparation
  • Teacher Training and Development
  • Teacher Unions and Associations
  • Teacher-Student Relationships
  • Teaching Critical Thinking
  • Technologies, Teaching, and Learning in Higher Education
  • Technology Education in Early Childhood
  • Technology, Educational
  • Technology-based Assessment
  • The Bologna Process
  • The Regulation of Standards in Higher Education
  • Theories of Educational Leadership
  • Three Conceptions of Literacy: Media, Narrative, and Gamin...
  • Tracking and Detracking
  • Traditions of Quality Improvement in Education
  • Transformative Learning
  • Transitions in Early Childhood Education
  • Tribally Controlled Colleges and Universities in the Unite...
  • Understanding the Psycho-Social Dimensions of Schools and ...
  • University Faculty Roles and Responsibilities in the Unite...
  • Using Ethnography in Educational Research
  • Value of Higher Education for Students and Other Stakehold...
  • Virtual Learning Environments
  • Vocational and Technical Education
  • Wellness and Well-Being in Education
  • Women's and Gender Studies
  • Young Children and Spirituality
  • Young Children's Learning Dispositions
  • Young Children's Working Theories
  • Privacy Policy
  • Cookie Policy
  • Legal Notice
  • Accessibility

Powered by:

  • [66.249.64.20|195.216.135.184]
  • 195.216.135.184

Logo for Open Educational Resources

Chapter 5. Sampling

Introduction.

Most Americans will experience unemployment at some point in their lives. Sarah Damaske ( 2021 ) was interested in learning about how men and women experience unemployment differently. To answer this question, she interviewed unemployed people. After conducting a “pilot study” with twenty interviewees, she realized she was also interested in finding out how working-class and middle-class persons experienced unemployment differently. She found one hundred persons through local unemployment offices. She purposefully selected a roughly equal number of men and women and working-class and middle-class persons for the study. This would allow her to make the kinds of comparisons she was interested in. She further refined her selection of persons to interview:

I decided that I needed to be able to focus my attention on gender and class; therefore, I interviewed only people born between 1962 and 1987 (ages 28–52, the prime working and child-rearing years), those who worked full-time before their job loss, those who experienced an involuntary job loss during the past year, and those who did not lose a job for cause (e.g., were not fired because of their behavior at work). ( 244 )

The people she ultimately interviewed compose her sample. They represent (“sample”) the larger population of the involuntarily unemployed. This “theoretically informed stratified sampling design” allowed Damaske “to achieve relatively equal distribution of participation across gender and class,” but it came with some limitations. For one, the unemployment centers were located in primarily White areas of the country, so there were very few persons of color interviewed. Qualitative researchers must make these kinds of decisions all the time—who to include and who not to include. There is never an absolutely correct decision, as the choice is linked to the particular research question posed by the particular researcher, although some sampling choices are more compelling than others. In this case, Damaske made the choice to foreground both gender and class rather than compare all middle-class men and women or women of color from different class positions or just talk to White men. She leaves the door open for other researchers to sample differently. Because science is a collective enterprise, it is most likely someone will be inspired to conduct a similar study as Damaske’s but with an entirely different sample.

This chapter is all about sampling. After you have developed a research question and have a general idea of how you will collect data (observations or interviews), how do you go about actually finding people and sites to study? Although there is no “correct number” of people to interview, the sample should follow the research question and research design. You might remember studying sampling in a quantitative research course. Sampling is important here too, but it works a bit differently. Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research.

Quick Terms Refresher

  • The population is the entire group that you want to draw conclusions about.
  • The sample is the specific group of individuals that you will collect data from.
  • Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
  • Sample size is how many individuals (or units) are included in your sample.

The “Who” of Your Research Study

After you have turned your general research interest into an actual research question and identified an approach you want to take to answer that question, you will need to specify the people you will be interviewing or observing. In most qualitative research, the objects of your study will indeed be people. In some cases, however, your objects might be content left by people (e.g., diaries, yearbooks, photographs) or documents (official or unofficial) or even institutions (e.g., schools, medical centers) and locations (e.g., nation-states, cities). Chances are, whatever “people, places, or things” are the objects of your study, you will not really be able to talk to, observe, or follow every single individual/object of the entire population of interest. You will need to create a sample of the population . Sampling in qualitative research has different purposes and goals than sampling in quantitative research. Sampling in both allows you to say something of interest about a population without having to include the entire population in your sample.

We begin this chapter with the case of a population of interest composed of actual people. After we have a better understanding of populations and samples that involve real people, we’ll discuss sampling in other types of qualitative research, such as archival research, content analysis, and case studies. We’ll then move to a larger discussion about the difference between sampling in qualitative research generally versus quantitative research, then we’ll move on to the idea of “theoretical” generalizability, and finally, we’ll conclude with some practical tips on the correct “number” to include in one’s sample.

Sampling People

To help think through samples, let’s imagine we want to know more about “vaccine hesitancy.” We’ve all lived through 2020 and 2021, and we know that a sizable number of people in the United States (and elsewhere) were slow to accept vaccines, even when these were freely available. By some accounts, about one-third of Americans initially refused vaccination. Why is this so? Well, as I write this in the summer of 2021, we know that some people actively refused the vaccination, thinking it was harmful or part of a government plot. Others were simply lazy or dismissed the necessity. And still others were worried about harmful side effects. The general population of interest here (all adult Americans who were not vaccinated by August 2021) may be as many as eighty million people. We clearly cannot talk to all of them. So we will have to narrow the number to something manageable. How can we do this?

Null

First, we have to think about our actual research question and the form of research we are conducting. I am going to begin with a quantitative research question. Quantitative research questions tend to be simpler to visualize, at least when we are first starting out doing social science research. So let us say we want to know what percentage of each kind of resistance is out there and how race or class or gender affects vaccine hesitancy. Again, we don’t have the ability to talk to everyone. But harnessing what we know about normal probability distributions (see quantitative methods for more on this), we can find this out through a sample that represents the general population. We can’t really address these particular questions if we only talk to White women who go to college with us. And if you are really trying to generalize the specific findings of your sample to the larger population, you will have to employ probability sampling , a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. Why randomly? If truly random, all the members have an equal opportunity to be a part of the sample, and thus we avoid the problem of having only our friends and neighbors (who may be very different from other people in the population) in the study. Mathematically, there is going to be a certain number that will be large enough to allow us to generalize our particular findings from our sample population to the population at large. It might surprise you how small that number can be. Election polls of no more than one thousand people are routinely used to predict actual election outcomes of millions of people. Below that number, however, you will not be able to make generalizations. Talking to five people at random is simply not enough people to predict a presidential election.

In order to answer quantitative research questions of causality, one must employ probability sampling. Quantitative researchers try to generalize their findings to a larger population. Samples are designed with that in mind. Qualitative researchers ask very different questions, though. Qualitative research questions are not about “how many” of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling . By definition, not everyone has an equal opportunity to be included in the sample. The researcher might select White women they go to college with to provide insight into racial and gender dynamics at play. Whatever is found by doing so will not be generalizable to everyone who has not been vaccinated, or even all White women who have not been vaccinated, or even all White women who have not been vaccinated who are in this particular college. That is not the point of qualitative research at all. This is a really important distinction, so I will repeat in bold: Qualitative researchers are not trying to statistically generalize specific findings to a larger population . They have not failed when their sample cannot be generalized, as that is not the point at all.

In the previous paragraph, I said it would be perfectly acceptable for a qualitative researcher to interview five White women with whom she goes to college about their vaccine hesitancy “to provide insight into racial and gender dynamics at play.” The key word here is “insight.” Rather than use a sample as a stand-in for the general population, as quantitative researchers do, the qualitative researcher uses the sample to gain insight into a process or phenomenon. The qualitative researcher is not going to be content with simply asking each of the women to state her reason for not being vaccinated and then draw conclusions that, because one in five of these women were concerned about their health, one in five of all people were also concerned about their health. That would be, frankly, a very poor study indeed. Rather, the qualitative researcher might sit down with each of the women and conduct a lengthy interview about what the vaccine means to her, why she is hesitant, how she manages her hesitancy (how she explains it to her friends), what she thinks about others who are unvaccinated, what she thinks of those who have been vaccinated, and what she knows or thinks she knows about COVID-19. The researcher might include specific interview questions about the college context, about their status as White women, about the political beliefs they hold about racism in the US, and about how their own political affiliations may or may not provide narrative scripts about “protective whiteness.” There are many interesting things to ask and learn about and many things to discover. Where a quantitative researcher begins with clear parameters to set their population and guide their sample selection process, the qualitative researcher is discovering new parameters, making it impossible to engage in probability sampling.

Looking at it this way, sampling for qualitative researchers needs to be more strategic. More theoretically informed. What persons can be interviewed or observed that would provide maximum insight into what is still unknown? In other words, qualitative researchers think through what cases they could learn the most from, and those are the cases selected to study: “What would be ‘bias’ in statistical sampling, and therefore a weakness, becomes intended focus in qualitative sampling, and therefore a strength. The logic and power of purposeful sampling like in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling” ( Patton 2002:230 ; emphases in the original).

Before selecting your sample, though, it is important to clearly identify the general population of interest. You need to know this before you can determine the sample. In our example case, it is “adult Americans who have not yet been vaccinated.” Depending on the specific qualitative research question, however, it might be “adult Americans who have been vaccinated for political reasons” or even “college students who have not been vaccinated.” What insights are you seeking? Do you want to know how politics is affecting vaccination? Or do you want to understand how people manage being an outlier in a particular setting (unvaccinated where vaccinations are heavily encouraged if not required)? More clearly stated, your population should align with your research question . Think back to the opening story about Damaske’s work studying the unemployed. She drew her sample narrowly to address the particular questions she was interested in pursuing. Knowing your questions or, at a minimum, why you are interested in the topic will allow you to draw the best sample possible to achieve insight.

Once you have your population in mind, how do you go about getting people to agree to be in your sample? In qualitative research, it is permissible to find people by convenience. Just ask for people who fit your sample criteria and see who shows up. Or reach out to friends and colleagues and see if they know anyone that fits. Don’t let the name convenience sampling mislead you; this is not exactly “easy,” and it is certainly a valid form of sampling in qualitative research. The more unknowns you have about what you will find, the more convenience sampling makes sense. If you don’t know how race or class or political affiliation might matter, and your population is unvaccinated college students, you can construct a sample of college students by placing an advertisement in the student paper or posting a flyer on a notice board. Whoever answers is your sample. That is what is meant by a convenience sample. A common variation of convenience sampling is snowball sampling . This is particularly useful if your target population is hard to find. Let’s say you posted a flyer about your study and only two college students responded. You could then ask those two students for referrals. They tell their friends, and those friends tell other friends, and, like a snowball, your sample gets bigger and bigger.

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

My early experience with qualitative research was rather unique. At that time, I needed to do a project that required me to interview first-generation college students, and my friends, with whom I had been sharing a dorm for two years, just perfectly fell into the sample category. Thus, I just asked them and easily “gained my access” to the research subject; I know them, we are friends, and I am part of them. I am an insider. I also thought, “Well, since I am part of the group, I can easily understand their language and norms, I can capture their honesty, read their nonverbal cues well, will get more information, as they will be more opened to me because they trust me.” All in all, easy access with rich information. But, gosh, I did not realize that my status as an insider came with a price! When structuring the interview questions, I began to realize that rather than focusing on the unique experiences of my friends, I mostly based the questions on my own experiences, assuming we have similar if not the same experiences. I began to struggle with my objectivity and even questioned my role; am I doing this as part of the group or as a researcher? I came to know later that my status as an insider or my “positionality” may impact my research. It not only shapes the process of data collection but might heavily influence my interpretation of the data. I came to realize that although my inside status came with a lot of benefits (especially for access), it could also bring some drawbacks.

—Dede Setiono, PhD student focusing on international development and environmental policy, Oregon State University

The more you know about what you might find, the more strategic you can be. If you wanted to compare how politically conservative and politically liberal college students explained their vaccine hesitancy, for example, you might construct a sample purposively, finding an equal number of both types of students so that you can make those comparisons in your analysis. This is what Damaske ( 2021 ) did. You could still use convenience or snowball sampling as a way of recruitment. Post a flyer at the conservative student club and then ask for referrals from the one student that agrees to be interviewed. As with convenience sampling, there are variations of purposive sampling as well as other names used (e.g., judgment, quota, stratified, criterion, theoretical). Try not to get bogged down in the nomenclature; instead, focus on identifying the general population that matches your research question and then using a sampling method that is most likely to provide insight, given the types of questions you have.

There are all kinds of ways of being strategic with sampling in qualitative research. Here are a few of my favorite techniques for maximizing insight:

  • Consider using “extreme” or “deviant” cases. Maybe your college houses a prominent anti-vaxxer who has written about and demonstrated against the college’s policy on vaccines. You could learn a lot from that single case (depending on your research question, of course).
  • Consider “intensity”: people and cases and circumstances where your questions are more likely to feature prominently (but not extremely or deviantly). For example, you could compare those who volunteer at local Republican and Democratic election headquarters during an election season in a study on why party matters. Those who volunteer are more likely to have something to say than those who are more apathetic.
  • Maximize variation, as with the case of “politically liberal” versus “politically conservative,” or include an array of social locations (young vs. old; Northwest vs. Southeast region). This kind of heterogeneity sampling can capture and describe the central themes that cut across the variations: any common patterns that emerge, even in this wildly mismatched sample, are probably important to note!
  • Rather than maximize the variation, you could select a small homogenous sample to describe some particular subgroup in depth. Focus groups are often the best form of data collection for homogeneity sampling.
  • Think about which cases are “critical” or politically important—ones that “if it happens here, it would happen anywhere” or a case that is politically sensitive, as with the single “blue” (Democratic) county in a “red” (Republican) state. In both, you are choosing a site that would yield the most information and have the greatest impact on the development of knowledge.
  • On the other hand, sometimes you want to select the “typical”—the typical college student, for example. You are trying to not generalize from the typical but illustrate aspects that may be typical of this case or group. When selecting for typicality, be clear with yourself about why the typical matches your research questions (and who might be excluded or marginalized in doing so).
  • Finally, it is often a good idea to look for disconfirming cases : if you are at the stage where you have a hypothesis (of sorts), you might select those who do not fit your hypothesis—you will surely learn something important there. They may be “exceptions that prove the rule” or exceptions that force you to alter your findings in order to make sense of these additional cases.

In addition to all these sampling variations, there is the theoretical approach taken by grounded theorists in which the researcher samples comparative people (or events) on the basis of their potential to represent important theoretical constructs. The sample, one can say, is by definition representative of the phenomenon of interest. It accompanies the constant comparative method of analysis. In the words of the funders of Grounded Theory , “Theoretical sampling is sampling on the basis of the emerging concepts, with the aim being to explore the dimensional range or varied conditions along which the properties of the concepts vary” ( Strauss and Corbin 1998:73 ).

When Your Population is Not Composed of People

I think it is easiest for most people to think of populations and samples in terms of people, but sometimes our units of analysis are not actually people. They could be places or institutions. Even so, you might still want to talk to people or observe the actions of people to understand those places or institutions. Or not! In the case of content analyses (see chapter 17), you won’t even have people involved at all but rather documents or films or photographs or news clippings. Everything we have covered about sampling applies to other units of analysis too. Let’s work through some examples.

Case Studies

When constructing a case study, it is helpful to think of your cases as sample populations in the same way that we considered people above. If, for example, you are comparing campus climates for diversity, your overall population may be “four-year college campuses in the US,” and from there you might decide to study three college campuses as your sample. Which three? Will you use purposeful sampling (perhaps [1] selecting three colleges in Oregon that are different sizes or [2] selecting three colleges across the US located in different political cultures or [3] varying the three colleges by racial makeup of the student body)? Or will you select three colleges at random, out of convenience? There are justifiable reasons for all approaches.

As with people, there are different ways of maximizing insight in your sample selection. Think about the following rationales: typical, diverse, extreme, deviant, influential, crucial, or even embodying a particular “pathway” ( Gerring 2008 ). When choosing a case or particular research site, Rubin ( 2021 ) suggests you bear in mind, first, what you are leaving out by selecting this particular case/site; second, what you might be overemphasizing by studying this case/site and not another; and, finally, whether you truly need to worry about either of those things—“that is, what are the sources of bias and how bad are they for what you are trying to do?” ( 89 ).

Once you have selected your cases, you may still want to include interviews with specific people or observations at particular sites within those cases. Then you go through possible sampling approaches all over again to determine which people will be contacted.

Content: Documents, Narrative Accounts, And So On

Although not often discussed as sampling, your selection of documents and other units to use in various content/historical analyses is subject to similar considerations. When you are asking quantitative-type questions (percentages and proportionalities of a general population), you will want to follow probabilistic sampling. For example, I created a random sample of accounts posted on the website studentloanjustice.org to delineate the types of problems people were having with student debt ( Hurst 2007 ). Even though my data was qualitative (narratives of student debt), I was actually asking a quantitative-type research question, so it was important that my sample was representative of the larger population (debtors who posted on the website). On the other hand, when you are asking qualitative-type questions, the selection process should be very different. In that case, use nonprobabilistic techniques, either convenience (where you are really new to this data and do not have the ability to set comparative criteria or even know what a deviant case would be) or some variant of purposive sampling. Let’s say you were interested in the visual representation of women in media published in the 1950s. You could select a national magazine like Time for a “typical” representation (and for its convenience, as all issues are freely available on the web and easy to search). Or you could compare one magazine known for its feminist content versus one antifeminist. The point is, sample selection is important even when you are not interviewing or observing people.

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

We have already discussed some of the differences in the goals of quantitative and qualitative sampling above, but it is worth further discussion. The quantitative researcher seeks a sample that is representative of the population of interest so that they may properly generalize the results (e.g., if 80 percent of first-gen students in the sample were concerned with costs of college, then we can say there is a strong likelihood that 80 percent of first-gen students nationally are concerned with costs of college). The qualitative researcher does not seek to generalize in this way . They may want a representative sample because they are interested in typical responses or behaviors of the population of interest, but they may very well not want a representative sample at all. They might want an “extreme” or deviant case to highlight what could go wrong with a particular situation, or maybe they want to examine just one case as a way of understanding what elements might be of interest in further research. When thinking of your sample, you will have to know why you are selecting the units, and this relates back to your research question or sets of questions. It has nothing to do with having a representative sample to generalize results. You may be tempted—or it may be suggested to you by a quantitatively minded member of your committee—to create as large and representative a sample as you possibly can to earn credibility from quantitative researchers. Ignore this temptation or suggestion. The only thing you should be considering is what sample will best bring insight into the questions guiding your research. This has implications for the number of people (or units) in your study as well, which is the topic of the next section.

What is the Correct “Number” to Sample?

Because we are not trying to create a generalizable representative sample, the guidelines for the “number” of people to interview or news stories to code are also a bit more nebulous. There are some brilliant insightful studies out there with an n of 1 (meaning one person or one account used as the entire set of data). This is particularly so in the case of autoethnography, a variation of ethnographic research that uses the researcher’s own subject position and experiences as the basis of data collection and analysis. But it is true for all forms of qualitative research. There are no hard-and-fast rules here. The number to include is what is relevant and insightful to your particular study.

That said, humans do not thrive well under such ambiguity, and there are a few helpful suggestions that can be made. First, many qualitative researchers talk about “saturation” as the end point for data collection. You stop adding participants when you are no longer getting any new information (or so very little that the cost of adding another interview subject or spending another day in the field exceeds any likely benefits to the research). The term saturation was first used here by Glaser and Strauss ( 1967 ), the founders of Grounded Theory. Here is their explanation: “The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he [or she] sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. [They go] out of [their] way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category” ( 61 ).

It makes sense that the term was developed by grounded theorists, since this approach is rather more open-ended than other approaches used by qualitative researchers. With so much left open, having a guideline of “stop collecting data when you don’t find anything new” is reasonable. However, saturation can’t help much when first setting out your sample. How do you know how many people to contact to interview? What number will you put down in your institutional review board (IRB) protocol (see chapter 8)? You may guess how many people or units it will take to reach saturation, but there really is no way to know in advance. The best you can do is think about your population and your questions and look at what others have done with similar populations and questions.

Here are some suggestions to use as a starting point: For phenomenological studies, try to interview at least ten people for each major category or group of people . If you are comparing male-identified, female-identified, and gender-neutral college students in a study on gender regimes in social clubs, that means you might want to design a sample of thirty students, ten from each group. This is the minimum suggested number. Damaske’s ( 2021 ) sample of one hundred allows room for up to twenty-five participants in each of four “buckets” (e.g., working-class*female, working-class*male, middle-class*female, middle-class*male). If there is more than one comparative group (e.g., you are comparing students attending three different colleges, and you are comparing White and Black students in each), you can sometimes reduce the number for each group in your sample to five for, in this case, thirty total students. But that is really a bare minimum you will want to go. A lot of people will not trust you with only “five” cases in a bucket. Lareau ( 2021:24 ) advises a minimum of seven or nine for each bucket (or “cell,” in her words). The point is to think about what your analyses might look like and how comfortable you will be with a certain number of persons fitting each category.

Because qualitative research takes so much time and effort, it is rare for a beginning researcher to include more than thirty to fifty people or units in the study. You may not be able to conduct all the comparisons you might want simply because you cannot manage a larger sample. In that case, the limits of who you can reach or what you can include may influence you to rethink an original overcomplicated research design. Rather than include students from every racial group on a campus, for example, you might want to sample strategically, thinking about the most contrast (insightful), possibly excluding majority-race (White) students entirely, and simply using previous literature to fill in gaps in our understanding. For example, one of my former students was interested in discovering how race and class worked at a predominantly White institution (PWI). Due to time constraints, she simplified her study from an original sample frame of middle-class and working-class domestic Black and international African students (four buckets) to a sample frame of domestic Black and international African students (two buckets), allowing the complexities of class to come through individual accounts rather than from part of the sample frame. She wisely decided not to include White students in the sample, as her focus was on how minoritized students navigated the PWI. She was able to successfully complete her project and develop insights from the data with fewer than twenty interviewees. [1]

But what if you had unlimited time and resources? Would it always be better to interview more people or include more accounts, documents, and units of analysis? No! Your sample size should reflect your research question and the goals you have set yourself. Larger numbers can sometimes work against your goals. If, for example, you want to help bring out individual stories of success against the odds, adding more people to the analysis can end up drowning out those individual stories. Sometimes, the perfect size really is one (or three, or five). It really depends on what you are trying to discover and achieve in your study. Furthermore, studies of one hundred or more (people, documents, accounts, etc.) can sometimes be mistaken for quantitative research. Inevitably, the large sample size will push the researcher into simplifying the data numerically. And readers will begin to expect generalizability from such a large sample.

To summarize, “There are no rules for sample size in qualitative inquiry. Sample size depends on what you want to know, the purpose of the inquiry, what’s at stake, what will be useful, what will have credibility, and what can be done with available time and resources” ( Patton 2002:244 ).

How did you find/construct a sample?

Since qualitative researchers work with comparatively small sample sizes, getting your sample right is rather important. Yet it is also difficult to accomplish. For instance, a key question you need to ask yourself is whether you want a homogeneous or heterogeneous sample. In other words, do you want to include people in your study who are by and large the same, or do you want to have diversity in your sample?

For many years, I have studied the experiences of students who were the first in their families to attend university. There is a rather large number of sampling decisions I need to consider before starting the study. (1) Should I only talk to first-in-family students, or should I have a comparison group of students who are not first-in-family? (2) Do I need to strive for a gender distribution that matches undergraduate enrollment patterns? (3) Should I include participants that reflect diversity in gender identity and sexuality? (4) How about racial diversity? First-in-family status is strongly related to some ethnic or racial identity. (5) And how about areas of study?

As you can see, if I wanted to accommodate all these differences and get enough study participants in each category, I would quickly end up with a sample size of hundreds, which is not feasible in most qualitative research. In the end, for me, the most important decision was to maximize the voices of first-in-family students, which meant that I only included them in my sample. As for the other categories, I figured it was going to be hard enough to find first-in-family students, so I started recruiting with an open mind and an understanding that I may have to accept a lack of gender, sexuality, or racial diversity and then not be able to say anything about these issues. But I would definitely be able to speak about the experiences of being first-in-family.

—Wolfgang Lehmann, author of “Habitus Transformation and Hidden Injuries”

Examples of “Sample” Sections in Journal Articles

Think about some of the studies you have read in college, especially those with rich stories and accounts about people’s lives. Do you know how the people were selected to be the focus of those stories? If the account was published by an academic press (e.g., University of California Press or Princeton University Press) or in an academic journal, chances are that the author included a description of their sample selection. You can usually find these in a methodological appendix (book) or a section on “research methods” (article).

Here are two examples from recent books and one example from a recent article:

Example 1 . In It’s Not like I’m Poor: How Working Families Make Ends Meet in a Post-welfare World , the research team employed a mixed methods approach to understand how parents use the earned income tax credit, a refundable tax credit designed to provide relief for low- to moderate-income working people ( Halpern-Meekin et al. 2015 ). At the end of their book, their first appendix is “Introduction to Boston and the Research Project.” After describing the context of the study, they include the following description of their sample selection:

In June 2007, we drew 120 names at random from the roughly 332 surveys we gathered between February and April. Within each racial and ethnic group, we aimed for one-third married couples with children and two-thirds unmarried parents. We sent each of these families a letter informing them of the opportunity to participate in the in-depth portion of our study and then began calling the home and cell phone numbers they provided us on the surveys and knocking on the doors of the addresses they provided.…In the end, we interviewed 115 of the 120 families originally selected for the in-depth interview sample (the remaining five families declined to participate). ( 22 )

Was their sample selection based on convenience or purpose? Why do you think it was important for them to tell you that five families declined to be interviewed? There is actually a trick here, as the names were pulled randomly from a survey whose sample design was probabilistic. Why is this important to know? What can we say about the representativeness or the uniqueness of whatever findings are reported here?

Example 2 . In When Diversity Drops , Park ( 2013 ) examines the impact of decreasing campus diversity on the lives of college students. She does this through a case study of one student club, the InterVarsity Christian Fellowship (IVCF), at one university (“California University,” a pseudonym). Here is her description:

I supplemented participant observation with individual in-depth interviews with sixty IVCF associates, including thirty-four current students, eight former and current staff members, eleven alumni, and seven regional or national staff members. The racial/ethnic breakdown was twenty-five Asian Americans (41.6 percent), one Armenian (1.6 percent), twelve people who were black (20.0 percent), eight Latino/as (13.3 percent), three South Asian Americans (5.0 percent), and eleven people who were white (18.3 percent). Twenty-nine were men, and thirty-one were women. Looking back, I note that the higher number of Asian Americans reflected both the group’s racial/ethnic composition and my relative ease about approaching them for interviews. ( 156 )

How can you tell this is a convenience sample? What else do you note about the sample selection from this description?

Example 3. The last example is taken from an article published in the journal Research in Higher Education . Published articles tend to be more formal than books, at least when it comes to the presentation of qualitative research. In this article, Lawson ( 2021 ) is seeking to understand why female-identified college students drop out of majors that are dominated by male-identified students (e.g., engineering, computer science, music theory). Here is the entire relevant section of the article:

Method Participants Data were collected as part of a larger study designed to better understand the daily experiences of women in MDMs [male-dominated majors].…Participants included 120 students from a midsize, Midwestern University. This sample included 40 women and 40 men from MDMs—defined as any major where at least 2/3 of students are men at both the university and nationally—and 40 women from GNMs—defined as any may where 40–60% of students are women at both the university and nationally.… Procedure A multi-faceted approach was used to recruit participants; participants were sent targeted emails (obtained based on participants’ reported gender and major listings), campus-wide emails sent through the University’s Communication Center, flyers, and in-class presentations. Recruitment materials stated that the research focused on the daily experiences of college students, including classroom experiences, stressors, positive experiences, departmental contexts, and career aspirations. Interested participants were directed to email the study coordinator to verify eligibility (at least 18 years old, man/woman in MDM or woman in GNM, access to a smartphone). Sixteen interested individuals were not eligible for the study due to the gender/major combination. ( 482ff .)

What method of sample selection was used by Lawson? Why is it important to define “MDM” at the outset? How does this definition relate to sampling? Why were interested participants directed to the study coordinator to verify eligibility?

Final Words

I have found that students often find it difficult to be specific enough when defining and choosing their sample. It might help to think about your sample design and sample recruitment like a cookbook. You want all the details there so that someone else can pick up your study and conduct it as you intended. That person could be yourself, but this analogy might work better if you have someone else in mind. When I am writing down recipes, I often think of my sister and try to convey the details she would need to duplicate the dish. We share a grandmother whose recipes are full of handwritten notes in the margins, in spidery ink, that tell us what bowl to use when or where things could go wrong. Describe your sample clearly, convey the steps required accurately, and then add any other details that will help keep you on track and remind you why you have chosen to limit possible interviewees to those of a certain age or class or location. Imagine actually going out and getting your sample (making your dish). Do you have all the necessary details to get started?

Table 5.1. Sampling Type and Strategies

Further Readings

Fusch, Patricia I., and Lawrence R. Ness. 2015. “Are We There Yet? Data Saturation in Qualitative Research.” Qualitative Report 20(9):1408–1416.

Saunders, Benjamin, Julius Sim, Tom Kinstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.”  Quality & Quantity  52(4):1893–1907.

  • Rubin ( 2021 ) suggests a minimum of twenty interviews (but safer with thirty) for an interview-based study and a minimum of three to six months in the field for ethnographic studies. For a content-based study, she suggests between five hundred and one thousand documents, although some will be “very small” ( 243–244 ). ↵

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

The actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).  Sampling frames can differ from the larger population when specific exclusions are inherent, as in the case of pulling names randomly from voter registration rolls where not everyone is a registered voter.  This difference in frame and population can undercut the generalizability of quantitative results.

The specific group of individuals that you will collect data from.  Contrast population.

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A sampling strategy in which the sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the sample.  This is often done through a lottery or other chance mechanisms (e.g., a random selection of every twelfth name on an alphabetical list of voters).  Also known as random sampling .

The selection of research participants or other data sources based on availability or accessibility, in contrast to purposive sampling .

A sample generated non-randomly by asking participants to help recruit more participants the idea being that a person who fits your sampling criteria probably knows other people with similar criteria.

Broad codes that are assigned to the main issues emerging in the data; identifying themes is often part of initial coding . 

A form of case selection focusing on examples that do not fit the emerging patterns. This allows the researcher to evaluate rival explanations or to define the limitations of their research findings. While disconfirming cases are found (not sought out), researchers should expand their analysis or rethink their theories to include/explain them.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

The result of probability sampling, in which a sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the random sample.  This is often done through a lottery or other chance mechanisms (e.g., the random selection of every twelfth name on an alphabetical list of voters).  This is typically not required in qualitative research but rather essential for the generalizability of quantitative research.

A form of case selection or purposeful sampling in which cases that are unusual or special in some way are chosen to highlight processes or to illuminate gaps in our knowledge of a phenomenon.   See also extreme case .

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

The accuracy with which results or findings can be transferred to situations or people other than those originally studied.  Qualitative studies generally are unable to use (and are uninterested in) statistical generalizability where the sample population is said to be able to predict or stand in for a larger population of interest.  Instead, qualitative researchers often discuss “theoretical generalizability,” in which the findings of a particular study can shed light on processes and mechanisms that may be at play in other settings.  See also statistical generalization and theoretical generalization .

A term used by IRBs to denote all materials aimed at recruiting participants into a research study (including printed advertisements, scripts, audio or video tapes, or websites).  Copies of this material are required in research protocols submitted to IRB.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

English Editing Research Services

types of sampling methods for qualitative research

Qualitative Research Sampling Methods: Pros and Cons to Help You Choose

qualitative sampling – Edanz

Your choice of sampling strategy can deeply impact your research findings, especially in qualitative studies, where every person counts.

There’s so much written on methods that it can sometimes feel overwhelming when you’re first discovering what’s out there. Even if you’re well into your research career, you may find yourself sticking with the same methodology again and again.

Many researchers focus on quantitative methodology. But they can greatly benefit from knowing qualitative methodology for use in mixed-methods studies and to better understand other studies.

This article aims to help you dive into the most widely recognized qualitative sampling strategies shortly and objectively.

What you ’ ll learn in this post

• All the most common types of qualitative research sampling methods.

• When to use each method.

• Pros and cons of each method.

• Specific examples of these qualitative sampling methods in use.

• Where to get your research both critiqued and edited, be it qualitative, quantitative, or mixed methods.

Your first step in choosing a qualitative sampling strategy

So, where do you start when you know you need to do more than grab students walking by your office? One of the first and most important decisions you must make about your sampling strategy is defining a clear sampling frame .

The cases you choose for your sample need to cover the various issues and variables you want to explore in your research. A fundamental aspect of your sample is that it should always contain the cases most likely to provide you with the richest data (Gray, 2004).

Owing to time and expense, qualitative research often works with small samples of people, cases, or phenomena in particular contexts. Therefore, unlike in quantitative research, samples tend to be more purposive (using your judgment) than they are random (Flick, 2009). This post will cover those main purposive sampling strategies.

It’s also important to keep in mind that qualitative samples are sometimes predetermined ­– what’s known as a priori determination, and other times follow more flexible determination (Flick, 2009).

So this article is organized based on those two parameters: a priori and more flexible determination.

And take note that in certain strategies it’s possible to start with a predetermined sample and end up extending it, or even varying it, for a valid reason.

Qualitative research is much more flexible than quantitative research. You iterate, you run another round, you seek saturation.

OK? Let’s see what’s on the qualitative menu. Hope you find something tasty.

A priori determination

Comprehensive sampling.

Comprehensive (or total population) sampling is a strategy that examines every case or instance of a given population that has specific characteristics (e.g., attributes, traits, experience, knowledge) you’re interested in for your study (Gray, 2004).

This sampling strategy is somewhat unusual because it’s often hard to sample the entire population of interest.

When to use it

It’s ideal for studies that focus on a specific organization or people with such specific characteristics that it’s possible to contact the whole population that has them (Gray, 2004).

Basically, two aspects are key to using this method

  • population size being somewhat small
  • having uncommon characteristics

One example would be studying perceptions about leadership within a small company (e.g., 10–30 people), where your sample could easily be every employee within the company.

  • Ideal for further analyzing, differentiating, and perhaps testing (Flick, 2009).
  • It might facilitate confidence in the validity of the results of research that use this method because it covers every case in a given population.
  • Reduced risk of missing valuable insights.
  • Only applicable to very specific studies because it requires the targeted population to be small and have uncommon characteristics.
  • Very limited potential for generalizability.

Practical example: Gerhard (as cited in Flick, 2009, p. 117) used this strategy to study the careers of patients with chronic renal failure. The sample was a complete collection of all patients with predetermined characteristics (male, married, age 30­–50 years, at the start of treatment at five hospitals in the UK).

Note that for this particular study, sampling was limited to several criteria: a specific sex, disease, marital status, age, region, and a limited period.

These predetermined characteristics were what allowed the researchers to achieve a comprehensive (total population) sample.

Extreme/deviant sampling

Extreme/deviant sampling is intentionally selecting extremes and trying to identify the factors that affect them (Gray, 2004).

It’s usually used to focus on special or uncommon cases such as noteworthy successes or failures. For instance, if you’re conducting a study about a reform program, you can include particularly successful examples and/or cases of big failures – these are two extremes, which is where the “extreme/deviant” name comes from (Flick, 2009).

It’s ideal for studying special/unusual cases in a particular context.

  • Allows you to collect focused information on a very particular phenomenon.
  • It’s sometimes regarded as producing the “purest” form of insight into a particular phenomenon.
  • Lets you collect insights from two very distinct perspectives, which will help you get an understanding of the phenomena as a whole.
  • The danger of mistakenly generalizing from extreme cases.
  • Selection bias

Practical example: Perhaps one of the most widely recognized studies that used this sampling method was Waterman and Peters’ In Search of Excellence: Lessons from America’s Best-Run Companies , published in 1982.

The researchers chose 62 companies based on their outstanding (extreme) success in terms of innovation and excellence (see Peters & Waterman [2004]).

Intensity sampling

Intensity sampling fundamentally involves the same logic as extreme/deviant case sampling, but it has less emphasis on the extremes.

Cases chosen for an intensity sample should be information-rich, manifesting the phenomenon intensely but not extremely; therefore capturing more typical cases compared with those at the extremes (Patton, 2002; Gray, 2004; Benoot, Hannes & Bilsen, 2016).

Patton (2002) argues that ideally, you should use this when you already have prior information about the variation of the subject you want to study. Some exploratory research might be needed depending on what you are researching.

  • Great for heuristic research/inquiry (Patton, 2002).
  • By choosing intensive cases that aren’t extreme/deviant, you can avoid the distortion that extreme cases sometimes bring (Patton, 2002).
  • Involves some prior information and considerable judgment. The researcher must do some exploratory work to grasp the nature of the variation of the specific situation he is researching about (Patton, 2002)
  • It requires an extended knowledge of the phenomena being studied to not mix cases that have sufficient intensity with the ones at the extremes (Patton, 2002).

Practical example: Researching above average/below average students would be a time to use this sampling method. This is because they experience the educational system intensely but aren’t extreme cases.

Maximum variation sampling

The maximum variation sampling strategy aims at capturing and describing a wide range of variations and that cut across what you want to research (Patton, 2002; Gray, 2004). How can you proceed to guarantee that you capture a high level of variation?

You can start by setting specific characteristics where you’ll look for variation that the literature (or you) identify as relevant for the phenomenon you’re researching. These may be education level, ethnicity, age, or socioeconomic status.

For small samples, having too much heterogeneity can be a problem because each case may be very different from the other.

But according to Patton (2002), this method might turn that weakness into a strength.

It does so by applying this logic: any common pattern that emerges from this kind of sample is of particular interest and value in capturing the core experiences and central, shared dimensions of a setting or phenomenon.

When to use it: Whenever you want to explore the variation of perceptions/practices concerning a broad phenomenon.

  • Allows the researcher to capture all variations of a phenomenon (Patton, 2002; Schreier, 2018).
  • Finds detailed insights about each variation (Patton, 2002; Schreier, 2018).
  • In small samples, sometimes cases are so different from one another that no common patterns emerge (Patton, 2002).

Practical example: Ziebland et al. (2004) was about how the internet affects patients’ experiences with cancer. It used a maximum variation sample to maximize the variety of insights.

The researchers purposively looked for people that differed in: type of cancer they had, stage of cancer, age, and sex.

Homogenous sampling

The homogenous sampling strategy can be seen as the exact opposite of maximum variation sampling because it seeks homogenous groups of people, settings, or contexts to be studied in-depth.

With this kind of sample, using focus group interviewing might prove extremely productive (Gray, 2004).

Use it if your research aims to specifically focus on a group with shared characteristics.

  • Produces highly detailed insights regarding a specific group (Patton, 2002).
  • Highly compatible with focus group interviews (Patton, 2002).
  • Can simplify the analysis (Patton, 2002).
  • Doesn’t let the researcher capture much variation (Patton, 2002).

Practical example: Nestbitt et al. (2012) was a study about Canadian adolescent mothers’ perceptions of influences on breastfeeding decisions. The researchers purposefully collected 16 homogenous cases of adolescent mothers (15­–19 years) that lived in the Durham region and had children up to 12 months old.

Other criteria included speaking English fluently and breastfeeding their infant at least once.

The aim of the researchers by using this method was to produce an in-depth look at this very specific group.

qualitative sampling – Edanz

Theory-based sampling

Theory-based sampling is basically a more formal type of criterion sampling, it’s more conceptually oriented, and the cases are chosen on the basis that they represent a theoretical construct (Patton, 2002; Gray, 2004).

The researcher samples incidents, periods of someone’s life, time periods, or people based on the potential manifestation or representation of important theoretical constructs.

Use this one when you want to study a pre-existing theory-derived concept that is of interest to your research.

  • Elaborating on previous theoretical and established concepts can facilitate the analysis.
  • Working on established theoretical concepts allows you to contribute new insights for an established theory.
  • The odds of finding out something entirely “new” are somewhat limited.
  • It might be harder to determine the population of interest because it’s hard to find people, programs, organizations, or communities of interest to a specific theoretical construct. This is unlike what happens when sampling based on determined people’s characteristics (Patton, 2002).

Practical example: Buckhold (as cited in Patton [2002, p. 238]) researched people who met specific theory-derived criteria for being “resilient.” She aimed to analyze the resilience of women who were victims of abuse and were able to survive.

Stratified purposive sampling

In stratified purposive sampling, decisions about the sample’s composition are made before data collection .

Schreier (2018) notes that it can be done in four steps:

  • Deciding which factors are known or likely to cause variation in the phenomenon of interest.
  • Selecting from two to a maximum of four factors for constructing a sampling guide.
  • Combining the factors of choice in a cross-table, though when picking more than two factors, it might be impossible to conduct sampling for all factor combinations.
  • Deciding on how many units for each cell/or factor combination.

Use this method when you want to explore known factors that influence the phenomenon of your interest.

These might be hypothesized in theory while having no empirical data supporting them. You can also purpose a factor and by including it on your sampling you might grasp its importance regarding the phenomena you’re researching.

  • Allows you to focus on several known factors that of interest for your research (Schreier, 2018).
  • Predetermining the composition of your sample might facilitate finding the cases/people/groups to research.
  • Sticking to the predetermined composition might have trouble with new factors discovered from your first cases that are left unresearched.
  • Finding the cases with the factors that are of most interest for your research might be challenging.

Practical example: Palacic (2017) examined entrepreneurial leadership and business performance in “gazelles” and “MICE” (business/market terms to describe a type of company). The sample was purposively constituted to contain cases from both types of companies that were involved in three major industrial sectors – manufacturing, sales, and services.

More flexible determination

Theoretical sampling.

Theoretical sampling was developed in the context of grounded theory methodology.

Fundamentally, it’s a process of data collection that aims to generate theory. It takes place in a constant interrelation between data collection and data analysis, and it’s guided by the concepts and/or theory emerging from the research process (Gray, 2004; Flick, 2009).

The sample is usually composed of heterogeneous cases that allow comparison of different instantiations (Schreier, 2018).

You can use this when you’re aiming to generate a new theory about a certain phenomenon.

  • May bring more innovation to your research (Schreier, 2018).
  • Your sample is more flexible compared with many other methods because there are no “static” criteria for your sample’s population.
  • Not ideal for inexperienced researchers because generating a new theory is very challenging.
  • Very time-consuming and complex.

Practical example: Glaser and Strauss (as cited in Flick, 2009, pp. 118–119) famously used this method to research awareness of dying in hospitals.

The researchers chose to conduct participant observation in different hospitals to develop a new theory about the way dying in a hospital is organized as a social process.

They built their sample through a step-by-step process while in direct contact with the field. First they studied awareness of dying in conditions that minimized patient awareness (e.g., comatose). Then they moved to situations where staff’s and patients’ awareness was high and death often was quick (e.g., intensive care). Then to situations where staff expectations of terminality were high, but dying tended to be slow (e.g., cancer). And ultimately to situations where death was unforeseen and rapid (e.g., emergency services).

Snowball sampling

Snowball sampling (or, chain referral sampling) is a method widely used in qualitative sociological research (Biernacki & Waldorf, 1981; Gray, 2004; Flick, 2009; Heckathorn, 2011). It’s used a lot because it’s effective at getting numbers. It’s premised on the idea that people know people similar to themselves.

Snowballing especially useful for studying hard-to-reach populations. Snowball sampling has been most applicable in studies where the focus relies on a sensitive issue, something that might be a private matter that requires knowing insiders so you can locate, contact, and receive consent from the true target population (Biernacki & Waldorf, 1981; Heckathorn, 2011).

The researcher forms a study sample through referrals made among people who are acquainted with others who have the characteristics of interest for the research. It begins through a convenience sample of someone of a hard-to-reach population.

qualitative sampling - snowball sampling

After successfully interviewing/communicating with this person, the researcher will ask them to introduce other people with the same characteristics. After acquiring contacts, the research proceeds in the same way (Heckathorn, 2011).

As hard-to-reach groups are, well, hard to reach, snowball sampling is effective when you need an inroad and cannot easily recruit and sample.

  • Ideal for studying hard-to-reach groups (Biernacki & Waldorf, 1981; Gray, 2004; Flick, 2009; Heckathorn, 2011).
  • Able to produce highly detailed insights regarding a specific group through the sampling of, in principle, information-rich cases (Patton, 2002).
  • If the researcher is studying a topic that involves moral, legal, or socially sensitive issues (e.g., prostitution, drug addiction) and does not know anyone from this group, it might be hard to start the first “chain” that bring in more recruits.
  • Very limited generalization potential.

Practical example: Cloud and Granfield (1994) used snowball sampling to study drug and alcohol addicts who beat their addictions without resorting to a treatment.

Using the snowballing method was fundamental to the authors because they were researching a widely distributed population (unlike those who participate in self-help groups or in treatment), and because the participants did not wish to expose their past as former drug addicts (i.e., sensitive issue).

Convenience sampling

Convenience sampling is a strategy that involves simply choosing cases in a way that is fast and convenient.

It’s probably the most common sampling strategy and, according to Patton (2002), the least desirable because it can’t be regarded as purposeful or strategic.

Many researchers choose this method thinking that their sample size is too small to generalize anyway, so they might as well pick cases that are easy to access and inexpensive to study (Patton, 2002).

This is a very common strategy among master’s students ­– asking fellow students to be part of the sample of their dissertation. That’s convenience sampling (Schreier, 2018). Also notable is that online surveying makes convenience sampling even simpler, beyond geographic limitations.

When you have few resources (mainly time and money) for your qualitative research, this is the go-to method. This is why so many studies are conducted on university students – they’re literally all over the place, whether you’re a student or researcher. As students, they’re also easier to incentivize with small compensation and they often are in the same boat.

  • Saves time, money, and effort (Patton, 2002).
  • Might be optimal for unfinanced and strictly timed qualitative research (often in master’s theses and in many doctoral dissertations).
  • Something of a “bad reputation” (Schreier, 2018).
  • Lowest credibility (Patton, 2002).
  • Might yield information-poor cases (Patton, 2002).

Practical example: Augusto and Simões (2017) used a convenience sampling strategy to capture perceptions and prevention strategies on Facebook surveillance.

As the original fieldwork was part of a master’s dissertation, convenience sampling was chosen because of the main author’s limited time and resources. This is in no way to discredit the study and findings – it was simply the most feasible way to get the research done.

Confirming and disconfirming cases

Confirming and disconfirming cases is frequently a second-stage sampling strategy.

Cases are chosen on the premise that they can confirm or disconfirm emerging patterns from the first stage of sampling (Gray, 2004).

After an exploratory process, one might consider testing ideas, confirming the importance and/or meaning of eventual patterns, and ultimately the viability of the findings through collecting new data and/or sampling additional cases (Patton, 2002).

As the name indicates, generally, it’s ideal for testing emergent findings from your data.

  • Strengthens emergent findings.
  • Allows you to identify possible “exceptions that prove the rule” or exceptions that might disconfirm a finding (Patton, 2002).
  • Usually requires a “first stage” of sampling.
  • While definitely useful, one can certainly make an argument about quantitative research being better able to test certain findings.

Practical example: If you were researching students’ motives for applying for college, and on the first interviews you found out the interviewees’ main reason for pursuing their education was to avoid having a routine day-job, this might be a good sampling method to use. The findings, however, would have to carefully look at trends and check for outliers.

So, how’s your research going?

Here’s hoping you find the right qualitative sampling method(s) that work for you. Putting this together was a lesson for me as well.

And when you’re ready for a professional edit or scientific review, check out Edanz’s author-guidance services , which have been leading the way since 1995. Good luck with your research!

This is a guest post from Adam Goulston, PsyD, MBA, MS, MISD, ELS. Adam runs science marketing firm Scize and has worked an in-house Senior Language Editor, as well as a manuscript editor, with Edanz.

Augusto, F. R., & Simões, M. J. (2017). To see and be seen, to know and be known : Perceptions and prevention strategies on Facebook surveillance. Social Science Information , 56 (4), 596–618. https://doi.org/10.1177/0539018417734974

Benoot, C., Hannes, K., & Bilsen, J. (2016). The use of purposeful sampling in a qualitative evidence synthesis : A worked example on sexual adjustment to a cancer trajectory. BMC Medical Research Methodology, 16 (21), 1–12. https://doi.org/10.1186/s12874-016-0114-6

Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10 (2), 141–163.

Cloud, W., & Granfield, R. (1994). Terminating Addiction Naturally : Post-Addict Identity and the Avoidance of Treatment Terminating Addiction Naturally : Post-Addict Identity and the Avoidance of Treatment. Clinical Sociology Review , 12 (1), 159–174.

Flick, U. (2009). An Introduction To Qualitative Research . SAGE Publications (4th ed.). London: Sage Publications, Inc. https://doi.org/978-1-84787-323-1

Gray, D. E. (2004). Doing Research in the Real World . London: Sage Publications, Inc.

Heckathorn, D. D. (2011). Comment: snowball versus respondent-driven sampling, 355–366. https://doi.org/10.1111/j.1467-9531.2011.01244.x

Nesbitt, S. A., Campbell, K. A., Jack, S. M., Robinson, H., Piehl, K., & Bogdan, J. C. (2012). Canadian adolescent mothers’ perceptions of influences on breastfeeding decisions: a qualitative descriptive study, 1–14.

Palacic, R. (2017). The phenomenon of entrepreneurial leadership in gazelles and mice : a qualitative study from Bosnia and Herzegovina. World Review of Entrepreneurship, Management and Sustainable Development , 13 (2/3).

Patton, M. Q. (2002). Qualitative Research & Evaluation Methods (3rd ed.). California: Sage Publications, Inc.

Peters, T. J., & Waterman, R. (2004). In Search of Excellence: Lessons from America’s Best-Run Companies . New York: First Harper Business Essentials.

Schreier, M. (2018). Sampling and Generalization In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Collection (pp. 84­­­–98). London, Sage Publications, Inc.

Ziebland, S., Chapple, A., Dumelow, C., Evans, J., Prinjha, S., & Rozmovits, L. (2004). Information in practice study: How the internet affects patients’ experience of cancer: A qualitative study. The BMJ, 328 (7434).

Qualitative Sampling Methods

Affiliation.

  • 1 14742 School of Nursing, University of Texas Health Science Center, San Antonio, TX, USA.
  • PMID: 32813616
  • DOI: 10.1177/0890334420949218

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

Keywords: breastfeeding; qualitative methods; sampling; sampling methods.

  • Evaluation Studies as Topic*
  • Research Design / standards
  • Sample Size*

types of sampling methods for qualitative research

7.2 Sampling in Qualitative Research

Learning objectives.

  • Define nonprobability sampling, and describe instances in which a researcher might choose a nonprobability sampling technique.
  • Describe the different types of nonprobability samples.

Qualitative researchers typically make sampling choices that enable them to deepen understanding of whatever phenomenon it is that they are studying. In this section we’ll examine the strategies that qualitative researchers typically employ when sampling as well as the various types of samples that qualitative researchers are most likely to use in their work.

Nonprobability Sampling

Nonprobability sampling Sampling techniques for which a person’s likelihood of being selected for membership in the sample is unknown. refers to sampling techniques for which a person’s (or event’s or researcher’s focus’s) likelihood of being selected for membership in the sample is unknown. Because we don’t know the likelihood of selection, we don’t know with nonprobability samples whether a sample represents a larger population or not. But that’s OK, because representing the population is not the goal with nonprobability samples. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind (once again, that would mean committing one of the errors of informal inquiry discussed in Chapter 1 "Introduction" ). In the following subsection, “Types of Nonprobability Samples,” we’ll take a closer look at the process of selecting research elements The individual unit that is the focus of a researcher’s investigation; possible elements in social science include people, documents, organizations, groups, beliefs, or behaviors. when drawing a nonprobability sample. But first, let’s consider why a researcher might choose to use a nonprobability sample.

So when are nonprobability samples ideal? One instance might be when we’re designing a research project. For example, if we’re conducting survey research, we may want to administer our survey to a few people who seem to resemble the folks we’re interested in studying in order to help work out kinks in the survey. We might also use a nonprobability sample at the early stages of a research project, if we’re conducting a pilot study or some exploratory research. This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research. But it isn’t just early stage research that relies on and benefits from nonprobability sampling techniques.

Researchers also use nonprobability samples in full-blown research projects. These projects are usually qualitative in nature, where the researcher’s goal is in-depth, idiographic understanding rather than more general, nomothetic understanding. Evaluation researchers whose aim is to describe some very specific small group might use nonprobability sampling techniques, for example. Researchers interested in contributing to our theoretical understanding of some phenomenon might also collect data from nonprobability samples. Maren Klawiter (1999) Klawiter, M. (1999). Racing for the cure, walking women, and toxic touring: Mapping cultures of action within the Bay Area terrain of breast cancer. Social Problems, 46 , 104–126. relied on a nonprobability sample for her study of the role that culture plays in shaping social change. Klawiter conducted participant observation in three very different breast cancer organizations to understand “the bodily dimensions of cultural production and collective action.” Her intensive study of these three organizations allowed Klawiter to deeply understand each organization’s “culture of action” and, subsequently, to critique and contribute to broader theories of social change and social movement organization. Thus researchers interested in contributing to social theories, by either expanding on them, modifying them, or poking holes in their propositions, may use nonprobability sampling techniques to seek out cases that seem anomalous in order to understand how theories can be improved.

In sum, there are a number and variety of instances in which the use of nonprobability samples makes sense. We’ll examine several specific types of nonprobability samples in the next subsection.

Types of Nonprobability Samples

There are several types of nonprobability samples that researchers use. These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods, we include them in this section of the chapter.

To draw a purposive sample A nonprobability sample type for which a researcher seeks out particular study elements that meet specific criteria that the researcher has identified. , a researcher begins with specific perspectives in mind that he or she wishes to examine and then seeks out research participants who cover that full range of perspectives. For example, if you are studying students’ satisfaction with their living quarters on campus, you’ll want to be sure to include students who stay in each of the different types or locations of on-campus housing in your study. If you only include students from 1 of 10 dorms on campus, you may miss important details about the experiences of students who live in the 9 dorms you didn’t include in your study. In my own interviews of young people about their workplace sexual harassment experiences, I and my coauthors used a purposive sampling strategy; we used participants’ prior responses on a survey to ensure that we included both men and women in the interviews and that we included participants who’d had a range of harassment experiences, from relatively minor experiences to much more severe harassment.

While purposive sampling is often used when one’s goal is to include participants who represent a broad range of perspectives, purposive sampling may also be used when a researcher wishes to include only people who meet very narrow or specific criteria. For example, in their study of Japanese women’s perceptions of intimate partner violence, Miyoko Nagae and Barbara L. Dancy (2010) Nagae, M., & Dancy, B. L. (2010). Japanese women’s perceptions of intimate partner violence (IPV). Journal of Interpersonal Violence, 25 , 753–766. limited their study only to participants who had experienced intimate partner violence themselves, were at least 18 years old, had been married and living with their spouse at the time that the violence occurred, were heterosexual, and were willing to be interviewed. In this case, the researchers’ goal was to find participants who had had very specific experiences rather than finding those who had had quite diverse experiences, as in the preceding example. In both cases, the researchers involved shared the goal of understanding the topic at hand in as much depth as possible.

Qualitative researchers sometimes rely on snowball sampling A nonprobability sample type for which a researcher recruits study participants by asking prior participants to refer others. techniques to identify study participants. In this case, a researcher might know of one or two people she’d like to include in her study but then relies on those initial participants to help identify additional study participants. Thus the researcher’s sample builds and becomes larger as the study continues, much as a snowball builds and becomes larger as it rolls through the snow.

Snowball sampling is an especially useful strategy when a researcher wishes to study some stigmatized group or behavior. For example, a researcher who wanted to study how people with genital herpes cope with their medical condition would be unlikely to find many participants by posting a call for interviewees in the newspaper or making an announcement about the study at some large social gathering. Instead, the researcher might know someone with the condition, interview that person, and then be referred by the first interviewee to another potential subject. Having a previous participant vouch for the trustworthiness of the researcher may help new potential participants feel more comfortable about being included in the study.

Snowball sampling is sometimes referred to as chain referral sampling. One research participant refers another, and that person refers another, and that person refers another—thus a chain of potential participants is identified. In addition to using this sampling strategy for potentially stigmatized populations, it is also a useful strategy to use when the researcher’s group of interest is likely to be difficult to find, not only because of some stigma associated with the group, but also because the group may be relatively rare. This was the case for Steven M. Kogan and colleagues (Kogan, Wejnert, Chen, Brody, & Slater, 2011) Kogan, S. M., Wejnert, C., Chen, Y., Brody, G. H., & Slater, L. M. (2011). Respondent-driven sampling with hard-to-reach emerging adults: An introduction and case study with rural African Americans. Journal of Adolescent Research, 26 , 30–60. who wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received not only $50.00 for participating in the study but also $20.00 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

Quota sampling A nonprobability sample type for which a researcher identifies subgroups within a population of interest and then selects some predetermined number of elements from within each subgroup. is another nonprobability sampling strategy. This type of sampling is actually employed by both qualitative and quantitative researchers, but because it is a nonprobability method, we’ll discuss it in this section. When conducting quota sampling, a researcher identifies categories that are important to the study and for which there is likely to be some variation. Subgroups are created based on each category and the researcher decides how many people (or documents or whatever element happens to be the focus of the research) to include from each subgroup and collects data from that number for each subgroup.

Let’s go back to the example we considered previously of student satisfaction with on-campus housing. Perhaps there are two types of housing on your campus: apartments that include full kitchens and dorm rooms where residents do not cook for themselves but eat in a dorm cafeteria. As a researcher, you might wish to understand how satisfaction varies across these two types of housing arrangements. Perhaps you have the time and resources to interview 20 campus residents, so you decide to interview 10 from each housing type. It is possible as well that your review of literature on the topic suggests that campus housing experiences vary by gender. If that is that case, perhaps you’ll decide on four important subgroups: men who live in apartments, women who live in apartments, men who live in dorm rooms, and women who live in dorm rooms. Your quota sample would include five people from each subgroup.

In 1936, up-and-coming pollster George Gallup made history when he successfully predicted the outcome of the presidential election using quota sampling methods. The leading polling entity at the time, The Literary Digest , predicted that Alfred Landon would beat Franklin Roosevelt in the presidential election by a landslide. When Gallup’s prediction that Roosevelt would win, turned out to be correct, “the Gallup Poll was suddenly on the map” (Van Allen, 2011). Van Allen, S. (2011). Gallup corporate history. Retrieved from http://www.gallup.com/corporate/1357/Corporate-History.aspx#2 Gallup successfully predicted subsequent elections based on quota samples, but in 1948, Gallup incorrectly predicted that Dewey would beat Truman in the US presidential election. For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “The first measured century” at http://www.pbs.org/fmc/timeline/e1948election.htm . Among other problems, the fact that Gallup’s quota categories did not represent those who actually voted (Neuman, 2007) Neuman, W. L. (2007). Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson. underscores the point that one should avoid attempting to make statistical generalizations from data collected using quota sampling methods. If you are interested in the history of polling, I recommend a recent book: Fried, A. (2011). Pathways to polling: Crisis, cooperation, and the making of public opinion professions . New York, NY: Routledge. While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, it would be a mistake to think of this strategy as yielding statistically representative findings.

Finally, convenience sampling A nonprobability sample type for which a researcher gathers data from the elements that happen to be convenient; also referred to as haphazard sampling. is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which he or she has most convenient access. This method, also sometimes referred to as haphazard sampling, is most useful in exploratory research. It is also often used by journalists who need quick and easy access to people from their population of interest. If you’ve ever seen brief interviews of people on the street on the news, you’ve probably seen a haphazard sample being interviewed. While convenience samples offer one major benefit—convenience—we should be cautious about generalizing from research that relies on convenience samples.

Table 7.1 Types of Nonprobability Samples

Key Takeaways

  • Nonprobability samples might be used when researchers are conducting exploratory research, by evaluation researchers, or by researchers whose aim is to make some theoretical contribution.
  • There are several types of nonprobability samples including purposive samples, snowball samples, quota samples, and convenience samples.
  • Imagine you are about to conduct a study of people’s use of the public parks in your hometown. Explain how you could employ each of the nonprobability sampling techniques described previously to recruit a sample for your study.
  • Of the four nonprobability sample types described, which seems strongest to you? Which seems weakest? Explain.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base

Methodology

  • What Is Purposive Sampling? | Definition & Examples

What Is Purposive Sampling? | Definition & Examples

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

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

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

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

Table of contents

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

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

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

How should academia deal with AI writing platforms? Free webinar

AI is transforming academia. In collaboration with QuillBot, we’ll explore how appropriate use of AI can help you achieve higher levels of success.

  • The AI revolution for academic success
  • Learn with industry experts and ask your questions
  • Using AI to enhance writing, not replace it

Sign up for this session

February 29th, 10AM CST

types of sampling methods for qualitative research

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

  • Maximum variation (or heterogeneous) sampling

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

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

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

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

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

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

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

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

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

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

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

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

Step 1: Define your research problem

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

Step 2: Determine your population

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

Step 3: Define the characteristics of your sample

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

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

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

Step 4: Collect your data using an appropriate method

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

Step 5: Analyze and interpret your results

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

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

Advantages of purposive sampling

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

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

Disadvantages of purposive sampling

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

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

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

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

Research bias

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

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

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

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

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Nikolopoulou, K. (2023, June 22). What Is Purposive Sampling? | Definition & Examples. Scribbr. Retrieved February 26, 2024, from https://www.scribbr.com/methodology/purposive-sampling/

Is this article helpful?

Kassiani Nikolopoulou

Kassiani Nikolopoulou

Other students also liked, what is non-probability sampling | types & examples, mixed methods research | definition, guide & examples, what is qualitative research | methods & examples, what is your plagiarism score.

Focus Groups

Sampling Methods in Qualitative Research

Choosing what insight to gather is usually half the work in research. Browsing a potential market’s demographics can offer some idea to customer expectations. Gaining a full idea, however, requires some specialized sampling . A process that influences market representation and analysis, sampling includes a slew of methods with their own strengths and prerequisites. 

Common Sampling Methods

Often for a specific type of input, sampling methods cover a wide variety of types and subtypes. Homogeneity, input type, trends, and pattern following all factor in. Important for direction, a study should already have clear goals before selecting a sampling method. For instance, coordinating a market subset is different than gauging overall reaction. The methods below are among the most common, typically due to their applicability.

  • Stratified Purposeful Sampling – Researchers sample a larger group by divvying them through certain categories like setting, location, background, etc.    
  • Random Purposeful Sampling – A form of sampling that evolves from input and avoids systematizing advanced knowledge of the participants
  • Systematic Sampling – Sampling type that selects participants through a set sequence  
  • Snow or Chain Sampling – A way to follow specified, rare instances via identifying perspectives or chain of contacts
  • Criterion Sampling – A method that bases selection off predetermined criteria

Using Multiple Sampling Methods Per Study  

Multiple samples are common in qualitative research . Important for verifying and averaging responses, focusing on similar representations offers a better understanding of input and helps pinpoint variations. While ideal for understanding responses, cohesive insight typically requires a more comprehensive application of sampling.

Using different sampling methods in the same project or study often uncovers insight that would otherwise remain hidden. Organization is key. Using different types in the wrong manner or sequence will impact accuracy and outcome. Qualitative research starts inwards or outwards starting research, and then either expands or specifies its group. So long as the sampling is consistent with requirements , simultaneous approaches can prove more efficient while also matching the reliability of individual methods.     

Online Capabilities Changing Methods and Access  

New technological capabilities are changing the pragmatics of qualitative research. Until recently limited to same-space dialogue, online focus groups and interviews are now feasible through video-streams offering the same observation detail as the human eye. Radically faster and more streamlined than traditional methods, online options are also changing how sampling is processed.

Greater access to participants enables researchers to examine targets with far more details and within far more parameters. While enabling far more specificity, finding samples online is also far more efficient. Discuss.io, for instance, offers a pool of 20 million volunteers from which researchers can select respondents. One previous obstacle of running multiple or hybrid sampling methods, online qualitative methods’ superior turnarounds allow for more analysis, stratified or random. Sampling, in turn, can become far more complex without any significant burden. Hybrid quant/qual methods, meanwhile, enable far more detailed projections into a release’s success. Learn more by checking out our demo or write us a message .    

Sign Up for our Newsletter

Related articles.

types of sampling methods for qualitative research

Maximize the number of research projects completed by year’s end: Yes, it can be done

Ask most agencies managing enterprise-level market research (MRX) projects, and they’ll tell you they have a…

Woman in a video conference

Seven Tips and Tricks for Better Online Focus Groups

The pandemic accelerated adoption of digital experiences at an unprecedented rate. It seemed like the switch…

types of sampling methods for qualitative research

Online Focus Groups

Focus groups consist of multiple participants sharing their opinions. Rightfully popular within qualitative research, the study…

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • HHS Author Manuscripts

Logo of nihpa

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 ]

Verywell Mind

How Snowball Sampling Used in Psychology Research

An effective method for recruiting study participants

Snowball sampling is a recruitment technique in which current research participants are enlisted to help recruit other potential study participants. This involves tapping into each participant's social network to find more subjects for a study. It allows researchers to find subjects who belong to a specific population who might not otherwise volunteer or seek out study participation.

As the name suggests, snowball sampling starts small and slowly "snowballs" into a larger sample. It is sometimes referred to as chain sampling, referral sampling, respondent-driven sampling, or chain-referral sampling.

At a Glance

Snowball sampling is a non-probability method allowing researchers to tap into hard-to-reach populations. Often used in qualitative designs, it allows researchers to recruit participants through referrals. This can be beneficial because it helps connect researchers with individuals they might not otherwise reach, but it can also contribute to sample bias and make it difficult to generalize the results to a larger population.

When to Use Snowball Sampling in Psychology Research

In most cases, researchers want to draw a sample that is both random and representative. Random selection ensures that each member of a group has an equal chance of being chosen, while representativeness ensures that the sample is an accurate reflection of the population as a whole.

While ideal, getting a random, representative sample isn't always possible. In such cases, researchers might turn to another method such as snowball sampling.

There are a number of situations where snowball sampling might be appropriate. These include:

  • When researchers are working with populations that are difficult to reach, including marginalized or hidden groups, such as drug users or sex workers
  • When research is in the exploratory stage, and scientists are still trying to learn more about an emerging phenomenon
  • When researchers are working to generate a hypothesis before they conduct more comprehensive studies
  • When recruiting through social networks makes the most sense in terms of cost and available resources
  • When researchers are studying communities that are highly connected via shared characteristics of interest

Is Snowball Sampling Qualitative or Quantitative?

Snowball sampling is commonly used in qualitative research. It uses a non-probability sampling method and is often used in studies where researchers are trying to explore different psychological phenomena and gain insights. Sample sizes may be smaller in this type of research, but often results in contextually-rich data. This can help researchers understand the nuances of what they are studying in a specific population.

How Snowball Sampling Works

Snowball sampling starts by finding a few individuals who meet the necessary criteria for a research sample. These individuals are sometimes known as the "seeds." The researcher then asks each participant to provide the names of additional people who meet those criteria.

The seed participants are interviewed and provided with a reward for their participation. They may then be given "coupons" that they can give to other eligible individuals. Each coupon contains information that allows recruiters to trace its origins. Potential participants can then redeem these coupons by enrolling in the study.

Each individual approached for participation is also asked to provide information on potential candidates. This process is continued until enough subjects have been located.

Pros and Cons of Snowball Sampling

Snowball sampling can have some pros and cons. Before using this approach, researchers should carefully weigh the potential advantages against the possible disadvantages and be transparent about any resulting limitations of the findings.

Advantages of Snowball Sampling

Snowball sampling can be particularly important when researchers are dealing with an uncommon or rare phenomenon. Traditional recruitment methods might simply not be able to locate a sufficient sample size .

It can also be helpful when participants are difficult to locate. This can include situations where people might be reticent about volunteering information about themselves or identifying themselves publicly. Because snowball sampling relies on recruiting people via trusted individuals, people may be more willing to participate.

Because snowball sampling provides essential information about the structure of social networks and connections, it can also be a helpful way of looking at the dynamics of the group itself.

Limitations of Snowball Sampling

The problem with snowball sampling is that it can contribute to bias . The opinions and characteristics of the initial members of the sample influence all of the subsequent subjects who are chosen to become part of the study.

This can make it more difficult for researchers to determine who might be missing from their sample and the factors contributing to that exclusion. Some variables might make it less likely for certain people to be referred, which can bias the study outcomes.

Another problem with snowball sampling is that it is difficult to know the size of the total overall population. It's also challenging to determine whether the sample accurately represents the population. If the sample only reflects a few people in the group, it might not be indicative of what is actually going on within the larger group.

Research suggests this sampling method can be a cost-effective way to collect data. However, researchers also caution that it can introduce bias, which means that caution must be used when interpreting the results of studies relying on snowball sampling.

Examples of Snowball Sampling

To understand how snowball sampling can be used in psychology research, looking at a few different examples can be helpful.

LGBTQIA+ Youth

Imagine a study where researchers want to investigate the experiences of LGBTQIA+ youth who live in rural areas. Because this population might be more difficult to reach due to discrimination , researchers might start by recruiting participants through local LGBTQIA+ organizations. Once they have an initial sample, the researchers can ask the current participants to introduce them to other people who are also LGBTQIA+.

Mental Health of Specific Populations

Consider a situation where researchers want to study the mental health of people in a particular profession, such as first responders who work in high-stress settings. The researchers might start by recruiting participants through professional organizations and then ask participants to refer them to colleagues who might also be interested in taking part.

Online Communities

Researchers might interested in learning more about phenomena that affect people who belong to specific online communities. They might reach initial participants by contacting them through online forums or websites and then ask if these participants are willing to share contact information for other members of the community.

Steps to Conduct Snowball Sampling

To conduct a snowball sample, researchers often use the following steps:

  • Create a research question and define the objectives of the study.
  • Identify the initial participants based on specific pre-determined criteria.
  • Obtain informed consent that clearly explains the purpose, benefits, and potential risks of participating in the research.
  • Collect data from the initial participants using surveys , interviews, observations , or other techniques.
  • Ask participants to refer you to other potential participants and obtain contact information if possible.
  • Contact the potential participants who have been referred to you. Explain the study and invite them to participate.
  • Repeat the same process with each subsequent participant. 
  • Continue the process until a sufficient sample has been obtained.

The Role of Snowball Sampling in Modern Research

While snowball sampling has its limitations, it plays an important role in modern psychology research . In particular, it can help researchers make contact with vulnerable or marginalized populations who are often overlooked and left out of more traditional sampling methods.

This technique can help researchers connect with the members of communities who may be hesitant to participate due to discrimination or the stigma associated with their condition.

It can also be a way for researchers to investigate phenomena that may be newly emerging and that might not yet be detectable using other sampling techniques.

Given the importance of social networks in today's highly connected work, snowball sampling also gives researchers a unique opportunity to examine how individuals connect to their communities. Researchers can use the information they collect to re-trace connections, providing valuable insights into how relationships and social dynamics affect the phenomena they study.

Snowball sampling is one method that psychology researchers may use to recruit study participants. While it has a greater risk of bias than drawing a random , representative sample , it does have some essential benefits. In particular, it can be a cost-effective way for researchers to find participants who belong to hidden or hard-to-reach populations. Despite the limitations of snowball sampling, it can play an important role in helping scientists learn more about emerging phenomena and populations that face stigma and marginalization.

Related: Convenience Sampling in Psychology Research

Read the original article on Verywell Mind .

Esa Hiltula/iStock/Getty Images

IMAGES

  1. Sampling Methods

    types of sampling methods for qualitative research

  2. Qualitative Research: Definition, Types, Methods and Examples

    types of sampling methods for qualitative research

  3. SAMPLING IN QUALITATIVE RESEARCH Definition Sampling is the

    types of sampling methods for qualitative research

  4. Discover How To Choose Appropriate Sampling Technique, Sample Size and

    types of sampling methods for qualitative research

  5. Types of Sampling Methods in Research: Briefly Explained in 2022

    types of sampling methods for qualitative research

  6. Qualitative Research Methods

    types of sampling methods for qualitative research

VIDEO

  1. Sampling Frame

  2. Sampling in Research

  3. QUANTITATIVE VS. QUALITATIVE VS. MIXED METHOD VS. ACTION RESEARCH

  4. Sampling In Research Methods| Unit:2 |#ugcnet #psychology_questions #jrf

  5. Sampling Techniques (Part 1): Random Sampling Techniques

  6. SAMPLING METHODS #Shorts #Research

COMMENTS

  1. Different Types of Sampling Techniques in Qualitative Research

    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.

  2. Sampling Methods

    There are two primary types of sampling methods that you can use in your research: Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

  3. (PDF) Sampling in Qualitative Research

    Two types of sampling techniques are discussed in the past qualitative studies—the theoretical and the purposeful sampling techniques. The chapter illustrates these two types of...

  4. Series: Practical guidance to qualitative research. Part 3: Sampling

    The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions.

  5. PDF Sampling Strategies in Qualitative Research

    However, in qualitative research the central resource through which sampling decisions are made is a focus on specific people, situations or sites because they offer a specific - 'biased' or 'information-rich' - perspective (Patton, 2002). Irrespective of the approach, sampling requires prior knowledge of the phenomenon.

  6. Sampling Methods

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

  7. 10.2 Sampling in qualitative research

    These include purposive samples, snowball samples, quota samples, and convenience samples. While the latter two strategies may be used by quantitative researchers from time to time, they are more typically employed in qualitative research, and because they are both nonprobability methods, we include them in this section of the chapter.

  8. Qualitative Sampling Methods

    Abstract Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies.

  9. Sampling Techniques for Qualitative Research

    2 Citations Abstract 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).

  10. Qualitative, Quantitative, and Mixed Methods Research Sampling

    The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e ...

  11. 10.2 Sampling in qualitative research

    Nonprobability sampling refers to sampling techniques for which a person's likelihood of being selected for membership in the sample is unknown. Since we don't know the likelihood of selection, we don't know whether a nonprobability sample is truly representative of a larger population. That's okay because generalizing to a larger ...

  12. Big enough? Sampling in qualitative inquiry

    Mine tends to start with a reminder about the different philosophical assumptions undergirding qualitative and quantitative research projects ( Staller, 2013 ). As Abrams (2010) points out, this difference leads to "major differences in sampling goals and strategies." (p.537). Patton (2002) argues, "perhaps nothing better captures the ...

  13. Chapter 5. Sampling

    Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research. Quick Terms Refresher The population is the entire group that you want to draw conclusions about.

  14. Qualitative Research Sampling Methods: Pros and Cons to Help You Choose

    • All the most common types of qualitative research sampling methods. • When to use each method. • Pros and cons of each method. • Specific examples of these qualitative sampling methods in use. • Where to get your research both critiqued and edited, be it qualitative, quantitative, or mixed methods. Table of contents show

  15. How to use and assess qualitative research methods

    The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software.

  16. Purposeful sampling for qualitative data collection and analysis in

    Principles of Purposeful Sampling. Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources (Patton, 2002).This involves identifying and selecting individuals or groups of individuals that are especially knowledgeable about or experienced with a phenomenon of interest ...

  17. Qualitative Sampling Methods

    Abstract Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies.

  18. Sampling Methods

    Multi-Stage Sampling: This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster. Non-probability Sampling

  19. Sampling in Qualitative Research

    Qualitative researchers sometimes rely on snowball sampling techniques to identify study participants. In this case, a researcher might know of one or two people she'd like to include in her study but then relies on those initial participants to help identify additional study participants. Thus the researcher's sample builds and becomes ...

  20. Qualitative Sampling Methods

    Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of ...

  21. What Is Purposive Sampling?

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

  22. Sampling Methods in Qualitative Research

    Focus Groups Sampling Methods in Qualitative Research By Discuss Choosing what insight to gather is usually half the work in research. Browsing a potential market's demographics can offer some idea to customer expectations. Gaining a full idea, however, requires some specialized sampling.

  23. Sampling in Qualitative Research

    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. ... Major features of sampling in qualitative research concern ...

  24. Types of Purposive Sampling Techniques with Their Examples and

    This article reconceptualizes sampling in social research. It is argued that three inter-related a priori assumptions limit on the possibility of sample design, namely: (a) the ontology of the ...

  25. How Snowball Sampling Used in Psychology Research

    Snowball sampling is a non-probability method allowing researchers to tap into hard-to-reach populations. Often used in qualitative designs, it allows researchers to recruit participants through ...