snowball sampling methods in qualitative research

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• What Is Snowball Sampling? | Definition & Examples

What Is Snowball Sampling? | Definition & Examples

Published on August 17, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Snowball sampling is a non-probability sampling method where new units are recruited by other units to form part of the sample . Snowball sampling can be a useful way to conduct research about people with specific traits who might otherwise be difficult to identify (e.g., people with a rare disease).

Also known as chain sampling or network sampling , snowball sampling begins with one or more study participants. It then continues on the basis of referrals from those participants. This process continues until you reach the desired sample, or a saturation point.

A number of criteria are used for the selection:

• The couple must have been together for a period of at least five years.
• The couple must live together now.
• The couple must live within a certain geographic area.
• The couple must have examples of changes or challenges they have experienced together (e.g., long-distance, illness or loss of a loved one).

Table of contents

When to use snowball sampling, types of snowball sampling, advantages and disadvantages of snowball sampling, other interesting articles, frequently asked questions about snowball sampling.

Snowball sampling is a widely employed method in qualitative research , specifically when studying hard-to-reach populations .

These may include:

• Populations that are small relative to the general population
• Geographically dispersed populations
• Populations possessing a social stigma or particular shared characteristic of interest

In all these cases, accessing members of the population can be difficult for non-members, as there is no sampling frame available.

Research in the fields of public health (e.g., drug users), public policy (e.g., undocumented immigrants), or niche genres (e.g., buskers) often uses snowball sampling.

This sampling method is also used to study sensitive topics, or topics that people may prefer not to discuss publicly. This is usually due to a perceived risk associated with self-disclosure. Snowball sampling allows you to access these populations while considering ethical issues , such as protecting their privacy and ensuring confidentiality.

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Snowball sampling begins with a convenience sample of one or more initial participants. Multiple data collection points (or waves) follow. These initial participants, called “seeds,” are used to recruit the first wave’s participants.

Wave 1 participants recruit wave 2 participants, and the sample expands, wave by wave, like a snowball growing in size as it rolls down a hill.

Depending on your research objectives , there are three different types of snowball sampling methods to choose from:

Linear snowball sampling

Exponential non-discriminative snowball sampling, exponential discriminative snowball sampling.

Linear snowball sampling relies on one referral per participant. In other words, the researcher recruits only one participant, and this participant, in turn, recruits only one participant. This process goes on until you have included enough participants in the sample.

Linear snowball sampling works best when there are few restrictions (called inclusion and exclusion criteria ) as to who is included in the sample.

As you finish up the interview , you ask them if they can refer someone else who also owns a tiny house. They happen to know someone, and pass the contact details to you. You interview them as well. Towards the end of the interview, you ask them to introduce you to one more person.

If more than two names are mentioned, it is a good idea to ask the interviewee how well they know those people, and then interview the person who is least known to them.

In exponential non-discriminative snowball sampling , the first participant provides multiple referrals. In other words, the researcher recruits the first participant, and this participant in turn recruits several others. The researcher includes all referrals in the sample. This type of snowball sampling is best used when you want to reach a larger sample.

In this method, participants give multiple referrals. However, the researcher screens those referrals, choosing only those who meet specific criteria to participate in the sample. The key difference between this and exponential non-discriminative snowball sampling is that not all referrals are included in the sample.

Exponential discriminative snowball sampling is most used when screening participants according to specific criteria is vital to your research goals.

As you inquire with your acquaintances, you find someone who bought a tiny house a year ago. At the end of the interview, you ask them if they know of other owners. You do not specify that the purchase has to be in the past three years.

As it happens, they do know of two more people who bought tiny houses in the same area as they did. You contact both, and find out that one bought the house four years ago and the other eight months ago. Since the one who bought the house four years ago does not meet your criteria, you only interview the other.

Like all research methods , snowball sampling has distinct advantages and disadvantages. It is important to be aware of these in order to determine whether it’s the best approach for your research design .

Advantages of snowball sampling

Depending on your research goals, there are advantages to using snowball sampling.

• Snowball sampling helps you research populations that you would not be able to access otherwise . Members of stigmatized groups (e.g., people experiencing homelessness) may hesitate to participate in a research study due to fear of exposure. Snowball sampling helps in this situation, as participants refer others whom they know and trust to the researcher.
• Since snowball sampling involves individuals recruiting other individuals, it is low-cost and easy to recruit a sample in this way.
• Unlike probability sampling , where you draw your sample following specific rules and some form of random selection , snowball sampling is flexible . All you need is to identify someone who is willing to participate and introduce you to others.

Disadvantages of convenience sampling

Snowball sampling has disadvantages, too, and is not a good fit for every research design.

• As the sample is not chosen through random selection , it is not representative of the population being studied. This means that you cannot make statistical inferences about the entire population and there is a high chance of research bias .
• The researcher has little or no control over the sampling process and relies mainly on referrals from already-identified participants. Since people refer others whom they know (and share traits with), this sampling method has a high potential for sampling bias .
• Relying on referrals may lead to difficulty reaching your sample . People may not want to cooperate with you, hesitate to reveal their identities, or mistrust researchers in general.

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

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Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

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Snowball Sampling Method: Techniques & Examples

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Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Snowball sampling, also known as chain-referral sampling, is a non-probability sampling method where currently enrolled research participants help recruit future subjects for a study.

Snowball sampling is often used in qualitative research when the population is hard-to-reach or hidden. It’s particularly useful when studying sensitive topics or when the members of a population are difficult to locate.

This sampling technique is called “snowball” because the sample group grows like a rolling snowball.

Non-probability sampling means that researchers, or other participants, choose the sample instead of randomly selecting it, so not all population members have an equal chance of being selected for the study.

Linear Snowball Sampling

• Linear snowball sampling depends on a straight-line referral sequence, beginning with only one subject. This individual subject will provide one new referral, which is then recruited into the sample group.
• This referral will provide another new referral, and this pattern continues until the ideal sample size is reached.

Exponential Non-Discriminative Snowball Sampling

• In exponential non-discriminative snowball sampling, the first subject recruited to the sample provides multiple referrals. Each new referral will then provide the researchers with more potential research subjects.
• This geometric chain sampling sequence continues until there are enough participants for the study.

Exponential Discriminative Snowball Sampling

• This type of snowball sampling is very similar to exponential non-discriminative snowball sampling in that each subject provides multiple referrals.
• However, in this case, only one subject is recruited from each referral. Researchers determine which referral to recruit based on the objectives and goals of the study.
• First, researchers will form an initial sample by drafting any potential subjects from a population (seeds).
• Even if only a couple of subjects are found at first, researchers will ask those subjects to recruit other individuals for the study. They recruit subjects by encouraging them to come forward on their own. Study participants will only provide specific names of recruited individuals if there is no risk of embarrassment or a violation of privacy. Otherwise, study participants do not identify any names of other potential participants.
• Current participants will continue to recruit others until the necessary sample size has been reached.

Snowball sampling requires special approval by an Institutional Review Board (IRB), whereby the researchers must provide a valid justification for using this method.

Researchers must also take precautions to protect the privacy of potential subjects, especially if the topic is sensitive or personal, such as studies of networks of drug users or prostitutes.

In addition, each respondent has the opportunity to participate or decline. Current participants in studies using this method do not receive any compensation for providing referrals, and study participants are not required to identify any names of other potential participants.

Example Situations

Snowball sampling is used when researchers have difficulty finding participants for their studies. This typically occurs in studies on hidden populations, such as criminals, drug dealers, or sex workers, as these individuals are difficult for researchers to access.

For example, a researcher studying the experiences of undocumented immigrants in a particular city. This population might be difficult to reach through traditional sampling methods due to fear of legal repercussions, lack of formal records, and other barriers.

The researcher might start by contacting a local organization that provides services to immigrants. Through this organization, the researcher could connect with a few willing individuals to participate in the study.

These initial participants (the “seeds”) would then be asked to refer the researcher to other undocumented immigrants they know who might also be willing to participate.

The new participants would then refer the researcher to others, and so on, creating a “snowball” effect where the number of participants grows as each person refers the researcher to others in their network.

The snowball sampling method is beneficial because current participants are likely to know others who share similar characteristics relevant to the study.

Members of these hidden populations tend to be closely connected as they share interests or are involved in the same groups, and they can inform others about the benefits of the study and reassure them of confidentiality.

Research Examples

• Researching non‐heterosexual women using social networks (Browne, 2002).
• Investigating lifestyles of heroin users (Kaplan, Korf, & Sterk, 1987).
• Identifying Argentinian immigrant entrepreneurs in Spain (Baltar & Brunet, 2012).
• Studying illegal drug users over the age of 40 (Waters, 2015).
• Assess the prevalence of irritable bowel syndrome in South China and its impact on health-related quality of life (Xiong, 2004).
• Obtaining samples of populations at risk for HIV (Kendall et al., 2008).

Enables access to hidden populations

Snowball sampling enables researchers to conduct studies when finding participants might otherwise be challenging. Concealed individuals, such as drug users or sex workers, are difficult for researchers to access, but snowball sampling helps researchers to connect to these hidden populations.

Avoids risk

Snowball sampling requires the approval of an Institutional Review Board to ensure the study is conducted ethically. In addition, each respondent has the opportunity to participate or to decline participation.

Saves money and time

Since current subjects are used to locate other participants, researchers will invest less money and time in planning and sampling.

Limitations

Difficult to determine sampling error.

Snowball sampling is a non-probability sampling method, so researchers cannot calculate the sampling error.

Bias is possible

Since current participants select other members for the sample, bias is likely. The initial participants will strongly impact the rest of the sample. In addition, an individual who is well-known and sociable is more strongly impacted by one who is more introverted.

Not always representative of the greater population

Because researchers are not selecting the participants themselves, they have little control over the sample. Researchers will thus have minimal knowledge as to whether the sample is representative of the target population.

• A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
• Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics (e.g. gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize various sampling methods.
• Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Felix-Medina, M. H., & Thompson, S. K. (2004). Combining link-tracing sampling and cluster sampling to estimate the size of hidden populations. JOURNAL OF OFFICIAL STATISTICS-STOCKHOLM- , 20 (1), 19-38.

Henderson, R. H., & Sundaresan, T. (1982). Cluster sampling to assess immunization coverage: a review of experience with a simplified sampling method. Bulletin of the World Health Organization , 60 (2), 253–260.

Malilay, J., Flanders, W. D., & Brogan, D. (1996). A modified cluster-sampling method for post-disaster rapid assessment of needs. Bulletin of the World Health Organization , 74 (4), 399–405.

Roesch, F. A. (1993). Adaptive cluster sampling for forest inventories. Forest Science , 39 (4), 655-669.

Smith, D. R., Conroy, M. J., & Brakhage, D. H. (1995). Efficiency of Adaptive Cluster Sampling for Estimating Density of Wintering Waterfowl. Biometrics , 51 (2), 777–788. https://doi.org/10.2307/2532964

Steven K. Thompson (1990) Adaptive Cluster Sampling, Journal of the American Statistical Association, 85:412,1050-1059, DOI: 10.1080/01621459.1990.10474975

Xiong, L. S., Chen, M. H., Chen, H. X., Xu, A. G., Wang, W. A., & Hu, P. J. (2004). A population‐based epidemiologic study of irritable bowel syndrome in South China: stratified randomized study by cluster sampling. Alimentary pharmacology & therapeutics , 19 (11), 1217-1224.

Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia

Affiliations.

• 1 Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands.
• 2 School of Geography and the Environment, University of Oxford, Oxford, United Kingdom.
• PMID: 30133457
• PMCID: PMC6104950
• DOI: 10.1371/journal.pone.0201710

Snowball sampling is a commonly employed sampling method in qualitative research; however, the diversity of samples generated via this method has repeatedly been questioned. Scholars have posited several anecdotally based recommendations for enhancing the diversity of snowball samples. In this study, we performed the first quantitative, medium-N analysis of snowball sampling to identify pathways to sample diversity, analysing 211 reach-outs conducted via snowball sampling, resulting in 81 interviews; these interviews were administered between April and August 2015 for a research project on anti-dam movements in Southeast Asia. Based upon this analysis, we were able to refine and enhance the previous recommendations (e.g., showcasing novel evidence on the value of multiple seeds or face-to-face interviews). This paper may thus be of particular interest to scholars employing or intending to employ snowball sampling.

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How Snowball Sampling Used in Psychology Research

An effective method for recruiting study participants

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Esa Hiltula/iStock/Getty Images

• When to Use It

Is Snowball Sampling Qualitative or Quantitative?

• How It Works
• Pros and Cons
• Snowball Sampling Steps
• Role in Modern Research

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

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.

Crawford FW, Wu J, Heimer R. Hidden population size estimation from respondent-driven sampling: a network approach . J Am Stat Assoc . 2018;113(522):755-766. doi:10.1080/01621459.2017.1285775

Raina SK. Establishing association . Indian J Med Res . 2015;141(1):127. doi:10.4103/0971-5916.154519

Kirchherr J, Charles K. Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia . PLoS One . 2018;13(8):e0201710. doi:10.1371/journal.pone.0201710

Martínez-Mesa J, González-Chica DA, Duquia RP, Bonamigo RR, Bastos JL. Sampling: how to select participants in my research study ?  An Bras Dermatol . 2016;91(3):326-330. doi:10.1590/abd1806-4841.20165254

Badowski G, Somera LP, Simsiman B, et al. The efficacy of respondent-driven sampling for the health assessment of minority populations . Cancer Epidemiol . 2017;50(Pt B):214-220. doi:10.1016/j.canep.2017.07.006

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Snowball Sampling – Method, Types and Examples

Table of Contents

Snowball Sampling

Definition:

Snowball sampling is a non-probability sampling technique in which participants are recruited through referrals from other participants. The idea behind snowball sampling is to start with a small group of participants, often referred to as “seeds,” and then have them refer other people they know who meet the study’s eligibility criteria.

Types of Snowball Sampling

Types of Snowball Sampling are as follows:

• Linear snowball sampling : In linear snowball sampling, each participant is asked to identify only one additional participant, and the process stops once the desired sample size is reached. This method is useful when the population of interest is small, and the researcher wants to ensure that each participant has an equal chance of being selected.
• Exponential non-discriminative snowball sampling : In exponential non-discriminative snowball sampling, each participant is asked to identify multiple individuals, but there is no restriction on the number of individuals they can identify. This method is useful when the population of interest is large, and the researcher wants to increase the sample size quickly.
• Exponential discriminative snowball sampling : In exponential discriminative snowball sampling, participants are asked to identify individuals who meet specific criteria. For example, if the researcher is interested in studying individuals who have a particular medical condition, participants are asked to identify individuals who have that condition. This method is useful when the population of interest is rare or difficult to identify, and the researcher wants to ensure that the sample is representative of that population.
• Network-based snowball sampling : In network-based snowball sampling, participants are selected based on their connections to other individuals. For example, if the researcher is interested in studying drug use among adolescents, they might start with a few individuals who are known to use drugs and ask them to identify other individuals in their social network who also use drugs. This method is useful when the population of interest is connected in some way, such as through social networks or communities.
• Time-location-based snowball sampling: In time-location-based snowball sampling, participants are selected based on their location at a particular time. For example, if the researcher is interested in studying the experiences of homeless individuals, they might start by visiting a particular location where homeless individuals are known to gather and ask them to identify other homeless individuals who might be willing to participate. This method is useful when the population of interest is difficult to reach through other means.
• Maximum variation snowball sampling: In maximum variation snowball sampling, participants are selected to represent a broad range of characteristics or experiences. For example, if the researcher is interested in studying the experiences of individuals with mental illness, they might select participants who have different diagnoses, are at different stages of recovery, and have different levels of support. This method is useful when the researcher wants to capture the diversity of experiences within a particular population.
• Criterion-based snowball sampling: In criterion-based snowball sampling, participants are selected based on certain criteria, such as age, gender, or occupation. For example, if the researcher is interested in studying the experiences of female healthcare workers during the COVID-19 pandemic, they might start by identifying a few female healthcare workers and ask them to identify other female healthcare workers who are also working during the pandemic. This method is useful when the researcher wants to study a specific subgroup within a larger population.
• Volunteer snowball sampling : In volunteer snowball sampling, participants are recruited through existing networks or organizations, such as online forums or community groups. For example, if the researcher is interested in studying the experiences of individuals with a rare medical condition, they might reach out to patient advocacy groups or online support groups to recruit participants. This method is useful when the researcher wants to reach a specific population that is difficult to access through other means.
• Respondent-driven sampling : Respondent-driven sampling (RDS) is a variant of snowball sampling that is often used to study hard-to-reach populations, such as individuals who use drugs or engage in high-risk behaviors. In RDS, participants are given incentives to recruit other participants, and the sample is weighted to account for the biases that can occur in snowball sampling. This method is useful when the researcher wants to obtain a representative sample of a hard-to-reach population.

Snowball Sampling Method

In this sampling method, the researcher starts with a small group of individuals who are already known to have some characteristics of interest, and then asks them to identify others who share those same characteristics. This process of expanding the sample through referrals continues until the desired sample size is reached.

The snowball sampling method is often used when the population of interest is small or hidden, or when there is a lack of comprehensive sampling frames. For example, it can be used to study populations of drug users, homeless individuals, or people engaged in illegal activities. It is also useful when the researcher is studying a rare phenomenon or a group that is difficult to access, such as people with a specific medical condition.

How to Conduct Snowball Sampling

Here are the steps to conduct snowball sampling:

• Identify your initial participants : Identify a small group of participants who fit the criteria for your research. They should be willing and able to refer others to participate in the study.
• Ask for referrals: Ask your initial participants to refer others who may be interested in participating in the study. Encourage them to reach out to their social networks and spread the word.
• Screen the referrals : Screen the referred participants to ensure that they meet the criteria for your study. If they do, invite them to participate.
• Repeat the process : After the referred participants have completed the study, ask them to refer others to participate. Repeat the process until you have reached your desired sample size.
• Analyze the data : Once you have collected data from your participants, analyze it to draw conclusions and insights.

Examples of Snowball Sampling

Here are some examples of how snowball sampling can be used in different research contexts:

• Studying stigmatized groups: Researchers who want to study stigmatized groups, such as people living with HIV/AIDS or members of the LGBTQ+ community, may use snowball sampling to identify participants. In this case, the initial participants may be recruited through outreach programs or community centers, and they may refer others who they know are also part of the community.
• Exploring hidden populations: Researchers who want to study populations that are difficult to access, such as drug users or sex workers, may also use snowball sampling. In this case, the initial participants may be recruited through outreach programs or contacts in the community, and they may refer others who they know are also part of the population.
• Conducting market research: Snowball sampling can also be used in market research to identify potential customers or clients. In this case, the initial participants may be recruited through social media or online forums, and they may refer others who they know are also interested in the product or service being offered.
• Collecting historical data: Snowball sampling can also be used to collect historical data about a particular community or event. For example, researchers may use snowball sampling to identify and interview survivors of a natural disaster, political conflict, or war.

Applications of Snowball Sampling

Snowball sampling can be applied in various research contexts, particularly in studies that aim to explore hard-to-reach populations or phenomena. Here are some common applications of snowball sampling:

• Studying hidden or stigmatized populations : Snowball sampling can be used to recruit participants from populations that may be difficult to reach through traditional sampling methods, such as drug users, sex workers, or refugees. This method can help researchers gain insights into the experiences and perspectives of these populations.
• Exploring social networks: Snowball sampling can be used to explore social networks by asking participants to refer others who they know. This method can help researchers understand how social networks operate and how they influence individuals’ attitudes and behaviors.
• Collecting historical data: Snowball sampling can be used to collect historical data by identifying individuals who have experienced a particular event or phenomenon. This method can help researchers gain insights into the long-term effects of historical events on individuals and communities.
• Conducting market research : Snowball sampling can be used to recruit participants for market research studies. This method can help researchers identify potential customers or clients who are interested in a particular product or service.
• Investigating rare phenomena: Snowball sampling can be used to study rare phenomena or behaviors that occur in specific populations. For example, researchers may use snowball sampling to identify individuals who have experienced a rare medical condition or who engage in a particular type of behavior.

When to use Snowball Sampling

Here are some situations where snowball sampling may be a suitable approach:

• Studying hard-to-reach populations : Snowball sampling can be used to study populations that may be difficult to access through traditional sampling methods, such as refugees, homeless individuals, or people living with HIV/AIDS. This method can help researchers gain insights into the experiences and perspectives of these populations.
• Exploring sensitive topics : Snowball sampling can be used to explore sensitive topics that individuals may not want to discuss with strangers. For example, researchers may use snowball sampling to study experiences of sexual assault or domestic violence.
• Collecting data on rare phenomena : Snowball sampling can be used to study rare phenomena or behaviors that occur in specific populations. For example, researchers may use snowball sampling to identify individuals who have experienced a rare medical condition or who engage in a particular type of behavior.
• Conducting exploratory research: Snowball sampling can be used in exploratory research when the goal is to identify new themes or areas of inquiry. This method can help researchers identify potential participants who can provide insights into the research question.
• Conducting research with limited resources : Snowball sampling can be a cost-effective method for conducting research with limited resources. Since participants are recruited through referrals, researchers may not need to spend resources on advertising or recruiting participants.

Purpose of Snowball Sampling

The purpose of snowball sampling is to identify and recruit participants for a research study when traditional sampling methods are not feasible or appropriate. Snowball sampling involves asking initial participants to refer others who they know and who meet the criteria for the study, which creates a “snowball” effect as the sample size grows.

The purpose of snowball sampling is to gain insights into populations or phenomena that may be difficult to access through traditional sampling methods. This method is often used to study hard-to-reach or stigmatized populations, such as drug users, workers, or refugees, who may be hesitant to participate in research studies. Snowball sampling can also be used to study rare phenomena or behaviors that occur in specific populations.

Snowball sampling is a useful research tool when the research question requires a non-random sample, and when the population of interest is small or hard to reach. However, researchers must be mindful of the potential biases that can arise from participant referrals, and take steps to minimize them. The purpose of snowball sampling is to identify a diverse range of participants who can provide valuable insights into the research question, while also maintaining the ethical principles of informed consent, confidentiality, and protection from harm.

Characteristics of Snowball Sampling

Here are some characteristics of snowball sampling:

• Non-random sampling: Snowball sampling is a non-random sampling technique, which means that participants are not selected at random from a population. Instead, participants are recruited based on their connection to the initial participants or through referrals.
• Recruitment through referrals : Snowball sampling relies on referrals from initial participants to recruit additional participants for the study. Participants are asked to refer others who they know and who meet the criteria for the study, creating a “snowball” effect as the sample size grows.
• Sampling bias: Snowball sampling can be prone to sampling bias since the sample is not randomly selected from the population of interest. Participants may be more likely to refer others who share similar characteristics or experiences, leading to a non-representative sample.
• Limited generalizability : The findings of studies that use snowball sampling may have limited generalizability to the population of interest, as the sample may not be representative of the population.
• Useful for hard-to-reach populations: Snowball sampling can be a useful technique for recruiting participants from hard-to-reach populations, such as individuals with a rare disease or people who engage in stigmatized behaviors.
• Ethical considerations: Researchers using snowball sampling must take steps to ensure that participants are fully informed about the study, their rights, and the potential risks and benefits of participating. Researchers must also take steps to protect participant confidentiality and minimize any potential harm.

Advantages of Snowball Sampling

Here are some advantages of snowball sampling:

• Access to hard-to-reach populations: Snowball sampling is useful for accessing hard-to-reach populations that may be difficult to recruit through traditional sampling methods. For example, individuals who engage in stigmatized behaviors, such as drug use or sex work, may be more likely to participate in a study if they are referred by someone they trust.
• Cost-effective: Snowball sampling can be a cost-effective method for recruiting participants since it relies on referrals from initial participants rather than costly advertising or recruitment efforts.
• High levels of rapport and trust : Snowball sampling can result in high levels of rapport and trust between the researcher and the participants. Participants may feel more comfortable sharing personal or sensitive information with a researcher who has been referred by someone they know and trust.
• Can generate rich data: Snowball sampling can generate rich data since participants are often highly engaged and willing to share their experiences and perspectives. Participants may also provide detailed information about their social networks, which can be valuable for understanding social dynamics and relationships.
• Flexible : Snowball sampling is a flexible research method that can be adapted to the needs of the study. Researchers can use different strategies for identifying initial participants and can adjust the recruitment process as the study progresses.

Limitations of Snowball Sampling

Here are some limitations of snowball sampling:

• Sampling bias : Snowball sampling is susceptible to sampling bias because participants are not selected at random from the population of interest. Participants may be more likely to refer others who share similar characteristics or experiences, leading to a non-representative sample.
• Limited generalizability : The findings of studies that use snowball sampling may have limited generalizability to the population of interest because the sample may not be representative. Therefore, caution should be taken when generalizing the results of a study that uses snowball sampling to other populations.
• Difficulty in controlling sample size: Snowball sampling can result in unpredictable sample sizes, making it difficult to plan for sample size calculations or statistical power.
• Limited access to initial participants: Snowball sampling relies on the initial participants to identify and refer additional participants. However, if initial participants are difficult to access or unwilling to participate, the recruitment process may stall.
• Ethical considerations: Researchers using snowball sampling must take steps to ensure that participants are fully informed about the study, their rights, and the potential risks and benefits of participating. They must also take steps to protect participant confidentiality and minimize any potential harm.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Snowball Sampling Method in Research

Introduction

How does the snowball method work in research, what are examples of snowball sampling, types of snowball sampling, advantages of snowball sampling, limitations of snowball sampling, best practices for conducting snowball sampling.

Snowball sampling is a non-probability sampling method used in qualitative and social science research to gather data from hard-to-reach or specialized populations. It begins with a small sample group of known research participants who fit the study's criteria and then expands by asking those initial participants to recommend others who also qualify.

Snowball sampling helps when a sampling frame is not clearly defined, making traditional sampling methods challenging to implement. The snowball sampling method relies on the networks and connections within a community to identify potential study participants.

This article outlines the mechanics of snowball sampling, explores its various types, and discusses its significance, advantages, and limitations. Additionally, we will provide guidance on when snowball sampling is most appropriately employed in qualitative research methods .

The snowball sampling method operates on the principle of chain referral. Initially, a small group of participants who meet the research criteria is identified and recruited by the researcher. These initial participants are then asked to recommend others they know who also meet the criteria and might be interested in participating in the study.

The process continues as new participants also recommend further contacts. This method creates a growing network of participants, much like a snowball increasing in size as it rolls downhill, hence the name.

In practice, snowball sampling involves several key steps. First, snowball sampling begins by identifying and selecting the initial subjects, often called "seed" members, who have a strong connection or central role in the community of interest. These seeds are crucial for gaining access to and the trust of subsequent participants.

After interviewing the seed members, the researcher asks them for referrals to other potential participants. This referral process is repeated with each new participant, expanding the sample size progressively.

The iterative nature of snowball sampling allows researchers to reach individuals who are difficult to locate through conventional means due to their rare characteristics, privacy concerns, or membership in a closed community. As the sample grows, the researcher collects data from a broader segment of the target population, enhancing the depth and richness of the research findings.

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Snowball sampling has been utilized in various fields to study populations that are difficult to access through traditional sampling methods. Below are three examples illustrating how snowball sampling can be applied in different research contexts.

Studying rare diseases in the medical field

Researchers often employ snowball sampling to study rare diseases. For example, when investigating a specific rare genetic disorder, researchers might start with a small group of diagnosed patients.

These enrolled research participants are then asked to refer others they know with the same condition, perhaps through patient support groups or online communities. This approach helps in gathering a significant sample size for a condition that otherwise has a very low prevalence in the general population.

Understanding social networks in sociology

Sociologists use snowball sampling to understand how individuals are interconnected within social networks, especially in hard-to-reach communities. A study might begin with a few key informants within a specific social group, such as a subculture or a marginalized community.

These informants then refer the researcher to other members of the community, allowing the study to map out social connections and understand the group's dynamics, norms, and behaviors.

Researching migration patterns

Snowball sampling is beneficial for studying migration patterns, especially among undocumented migrants or refugees who might be reluctant to participate in research due to legal or personal safety concerns. Researchers might start with a small group of migrants willing to share their experiences and then ask them to refer others in their network.

Researchers can rely on multiple referrals to gather valuable insights from enough participants regarding the migration process, challenges faced during migration, and the social networks that support migrants.

Snowball sampling is a flexible approach that adapts to various research needs. Below are the key types, each facilitating access to specific populations and insights.

Linear snowball sampling

Linear snowball sampling is the simplest form where the process starts with one or a few individuals who then refer a single contact each. This method ensures a controlled, steady growth of the sample, making it easier to manage and analyze.

It's particularly useful for qualitative research where in-depth data from each participant is vital, but it can limit the sample diversity since it grows incrementally.

Exponential non-discriminative snowball sampling

This approach allows each participant to refer multiple new subjects, with no restrictions on who can be referred. The sample size increases rapidly, providing a broad dataset in a short amount of time.

Exponential non-discriminative snowball sampling is ideal for exploratory studies where a wide range of perspectives is desired, though it may include a more heterogeneous group, potentially complicating analysis.

Exponential discriminative snowball sampling

Similar to its non-discriminative counterpart, this method involves participants referring several contacts. However, the key difference is the application of specific criteria to select among the referred individuals.

This selective approach helps in focusing the research on a more targeted subset of the population, enhancing the relevance and depth of the data collected.

Respondent-driven sampling (RDS)

Respondent-driven sampling is a more sophisticated variant that combines the referral method with statistical techniques to create a sample that can represent the broader population, for example by giving more weight to responses of participants from marginalized groups. This method compensates for the biases inherent in the referral process, allowing researchers to make population-level inferences from the sampled data.

RDS is particularly valuable in studies involving hard-to-reach populations, such as marginalized or hidden groups.

Chain referral sampling

Acting as the backbone for all snowball sampling techniques, chain referral sampling emphasizes the process of participants referring others within their network.

This foundational strategy leverages existing social connections to reach individuals who are otherwise difficult to access, making it an effective method for qualitative research aiming to explore complex social phenomena or behaviors within specific communities.

Snowball sampling offers several benefits that make it a valuable method for researchers, especially when studying hard-to-reach populations. Below are three key advantages of employing this sampling strategy.

Access to hidden populations

One of the most significant advantages of snowball sampling is its ability to penetrate hidden or hard-to-reach populations. Traditional sampling methods often fail to reach individuals who are part of closed communities, such as undocumented immigrants, drug users, people with rare diseases, or members of underground subcultures. Snowball sampling leverages existing social networks to access these groups, enabling researchers to collect data that would otherwise be difficult or impossible to obtain.

Cost-effectiveness

Snowball sampling is notably cost-effective, particularly when resources are limited. By relying on participants to identify future subjects, researchers can minimize the expenses related to locating and recruiting participants. This method reduces the need for extensive outreach efforts and allows for the efficient allocation of resources, making it an attractive option for studies with limited funding.

Richness of data

This sampling method often results in a richness of data that is hard to achieve through other methods. As participants refer individuals within their networks, they tend to recommend contacts who share deep, nuanced experiences relevant to the research topic. This insider perspective can uncover detailed insights and complex dynamics within the population of interest, contributing to a more comprehensive understanding of the subject matter.

While snowball sampling is a powerful tool for accessing specific populations, it comes with several limitations that researchers must consider. Below are three notable drawbacks of this sampling method.

Potential for bias

One of the primary limitations of snowball sampling is the potential for bias . Since the sample grows based on participants' referrals, it is heavily influenced by their social networks and preferences. This can lead to a sample that is not representative of the broader population, as it may over-represent certain groups or opinions while under-representing others. Such bias can limit the generalizability of the research findings to the wider population.

Lack of randomness

The very nature of snowball sampling means it lacks randomness, a cornerstone of many traditional sampling methods aimed at ensuring representativeness. Participants are chosen based on their connections within a network rather than being randomly selected. This lack of randomness can further contribute to bias, making it challenging to make definitive conclusions about the population beyond the sample.

Difficulty in estimating sample size

Another challenge with snowball sampling is the difficulty in estimating the final sample size beforehand. The method's reliance on participants' willingness and ability to refer others introduces uncertainty into the research process. This unpredictability can complicate research planning and resource allocation, especially in studies where a specific sample size is crucial for statistical analysis or validity.

Effective snowball sampling hinges on meticulous planning and execution. By adhering to a set of best practices, researchers can navigate the method's complexities, ensuring the collection of rich and meaningful data. Below are crucial strategies to enhance the reliability and depth of research conducted through snowball sampling.

Define the target population clearly

A precise definition of the target population is foundational. It shapes the direction of the sampling process, guiding the selection of initial participants who are central to the networks of interest. This clarity is essential for reaching the most relevant and informative individuals, ensuring that the research effectively addresses its objectives.

Build trust and ensure confidentiality

Trust is paramount in snowball sampling, particularly when dealing with sensitive subjects or hard-to-reach populations. Researchers must establish a rapport with participants, emphasizing the confidentiality of their responses and the significance of their contribution. Clear communication about the study's goals and the protection of participants' information encourages cooperation and facilitates the referral process.

Recognize data saturation

Identifying when additional data no longer contributes new insights — data saturation — is key to determining the study's scope. This assessment aids in optimizing resource use, avoiding redundant data collection, and ensuring the research is both thorough and focused.

Implement a systematic referral process

A structured approach to managing referrals ensures efficiency and thoroughness. This involves organizing information on potential participants, tracking progress, and following up on leads promptly. Such a system helps maintain momentum in the sampling process and supports a comprehensive exploration of the population.

Monitor diversity and bias

Given snowball sampling's vulnerability to homogeneity and selection bias, continuous monitoring of the sample's composition is critical. Researchers should strive for diversity among participants to capture a wide range of perspectives within the target population. Adjusting recruitment strategies to include varied subgroups or using targeted referrals can help mitigate bias and enhance the representativeness of the sample.

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Snowball Sampling

It can be challenging to include people living in vulnerable circumstances and marginalised communities in research due to a lack of trust, time and reachability. Depending on the group, researchers may be perceived as the much-hated establishment, as spies, or simply as untrustworthy. Therefore, to plan the first contact and build trust is crucial before inviting people to participate in your study.

First contact could, for instance, be established through a trusted individual with similar experiences who can act as an intermediary. Such an intermediary can connect you to potential research participants. This approach to sampling is referred to as ‘snowball sampling’ and is considered a ‘non-probability method’.

Image source: (QuestionPro,2022)

Snowball sampling starts with a small number of persons who fit the research criteria,  e.g., mental health service users, refugees, homeless IV drug users. The researcher then asks each of these individuals to suggest several different individuals with the same characteristics among their acquaintances who will subsequently be contacted by the researcher to see if they would like to participate. Whoever volunteers for the study from this second stage, will be asked to do the same thing. Like a snowball, the sample grows with every round of interviews.

There are several downsides to snowball sampling that need to be taken into consideration:

• the sample can be biased towards individuals who have many social connections and strong networks and excludes those who are more isolated
• participants themselves may limit the sample by deciding who is ‘in’ or ‘out’ by acting as gatekeepers
• it is difficult to guarantee privacy and confidentiality where participants select their friends and acquaintances

Nevertheless, snowball sampling is a solid sampling method especially when one has difficulty finding participants for a study.

(Author: Hanna Kienzler)

What is it?

Snowball sampling by Ibpsychsurvival (2019)

This YouTube video offers a brief introduction to what snowballing is and when it should be used. It also discusses the different types of snowball sampling including linear snowball sampling, exponential non-discriminative sampling and exponential discriminative sampling

(Academic reference: Ibpsychsurvival (2019, Sept 06). Snowball Sampling . [Video]. YouTube. https://www.youtube.com/watch?v=Z-P8dazQVi0)

Snowball sampling: definition, method and examples by Simple Psychology (2012)

This webpage will introduce you to what snowball sampling is and the wider key terms needed to navigate this topic. Additionally in text format, it will explain the various types of Snowball sampling there are, a step-by-step guide on how to cluster sample and its advantages and disadvantages.

(Academic reference: Simple Psychology (2012). Snowball sampling: definition, method and examples. https://www.simplypsychology.org/snowball-sampling.html)

Accessing hidden and hard-to-reach populations: snowball research strategies by Atkinson and Flint (2001)

This article explains how snowball sampling can be used to sample hidden populations that experience deprivation and social stigmatisation as well as hard-to-reach elites. It also describes some limitations of the technique and how it can be combined with other tools to enhance research success.

(Academic reference: Atkinson, R. & Flint, J. (2001). Accessing hidden and hard-to-reach populations: snowball research strategies. Social Research Update. https://sru.soc.surrey.ac.uk/SRU33.html)

Snowball sampling by Parker et al. (2019)

This article offers a balanced account of the advantages and disadvantages of snowball sampling as well examples of how it can be used and how obstacles can be overcome when using the sampling method. An inventive final section describes new techniques that have emerged in recent years thanks to social media platforms and other online tools.

(Academic reference: Parker, C., Scott, S. and Geddes, A. (2019). Snowball sampling. SAGE Research Methods Foundations . https://eprints.glos.ac.uk/6781/)

How is it done?

Snowball sampling | non-probability sampling (part-4) | NTA-UGC NET/JRF by Dr Sumeet Bakshi (2021)

This YouTube Video begins with an explanation of why the term “snowball” within snowball sampling is used. It offers an explanation of when to use it, examples of it and the main advantages and disadvantages. It will also provide you with a 3-step method of how to carry it out. This video is 5 minutes and 16 seconds in duration.

(Academic reference: Bakshi, S. (2021, August 20). Snowball sampling | Non-probability sampling (part-4) | NTA-UGC NET/JRF. [VIdeo]. YouTube. https://www.youtube.com/watch?v=GF3kBDb4wJs)

Snowball sampling: definition, method, advantages and disadvantages by QuestionPro (2022)

This webpage begins with a description of snowball sampling and the different population groups it could be applied to researching. It also offers a detailed method of how to carry this technique out in practice. It also discusses some of its advantages and disadvantages.

(Academic reference: QuestionPro (2022). Snowball sampling: definition, method, advantages and disadvantages . QuestionPro. https://www.questionpro.com/blog/snowball-sampling/)

Snowball sampling by Laerd Dissertation (2012)

This webpage explains what snowball sampling is and the situations where it would be useful to use it. It will also provide you with a 2-step method of how to conduct snowball sampling.

(Academic reference: Laerd Dissertation (2012) . Snowball sampling .  https://dissertation.laerd.com/snowball-sampling.php#step1)

Method in action

Sampling, snowballs and non-strategies: how to accidentally stumble upon data by Femke Gubbels (2016)

This blog post describes the author’s experiences of using snowball sampling as a novice researcher in Tanzania, discussing some of its unexpected results, advantages and limitations.

(Academic reference: Gubbels, F. (2016) . Sampling, snowballs and non-strategies: how to accidentally stumble upon data. Africa at LSE. https://blogs.lse.ac.uk/africaatlse/2016/09/07/sampling-snowballs-and-non-strategies-how-to-accidentally-stumble-upon-data-%E2%80%8B/ )

Enhancing the sample diversity of snowball samples: recommendations from a research project on anti-dam movements in Southeast Asia by Julian Kirchherr and Katrina Charles (2018)

This article draws upon interview data from a research project on anti-dam movements in South East Asia where the participants were located through snowball sampling. Its analysis will offer you a framework for enhancing the diversity of your snowball sample through a series of fiverecommendations.

(Academic reference:  Kirchherr, J. & Charles, K. (2018). Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia. Plos One, 13 (8) pp 1-17. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104950/pdf/pone.0201710.pdf)

Womanism and snowball sampling: engaging marginalized populations in holistic research by Xeturah Woodley and Megan Lockard (2016)

This article draws on the experiences of Black women in New Mexico’s institute of Higher Education, who are located through snowball sampling. The article begins by explaining some of the issues of using snowball sampling and places this within a wider discussion of researching women’s rights. This article will allow you to see the benefits of using snowball sampling to research marginalised and hard-to-reach communities, whilst also assessing the importance of the advantages over the disadvantages.

(Academic reference:  Woodley, X. & Lockard, M. (2016). Womanism and snowball sampling: engaging marginalized populations in holistic research. The Qualitative Report, 12 (2), pp 1-11. https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=2198&context=tqr )

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

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

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

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques 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.

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Snowball Sampling: A Purposeful Method of Sampling in Qualitative Research

Background and Objectives Snowball sampling is applied when samples with the target characteristics are not easily accessible. This research describes snowball sampling as a purposeful method of data collection in qualitative research. Methods This paper is a descriptive review of previous research papers. Data were gathered using English keywords, including “review,” “declaration,” “snowball,” and “chain referral,” as well as Persian keywords that are equivalents of the following: “purposeful sampling,” “snowball,” “qualitative research,” and “descriptive review.” The databases included Google Scholar, Scopus, Irandoc, ProQuest, Science Direct, SID, MagIran, Medline, and Cochrane. The search was limited to Persian and English articles written between 2005 and 2013. Results The preliminary search yielded 433 articles from PubMed, 88 articles from Scopus, 1 article from SID, and 18 articles from MagIran. Among 125 articles, methodological and non-research articles were omitted. Finally, 11 relevant articles, which met the criteria, were selected for review. Conclusions Different methods of snowball sampling can be applied to facilitate scientific research, provide community-based data, and hold health educational programs. Snowball sampling can be effectively used to analyze vulnerable groups or individuals under special care. In fact, it allows researchers to access susceptible populations. Thus, it is suggested to consider snowball sampling strategies while working with the attendees of educational programs or samples of research studies.

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Abbas Balouche

bjectives: To determining attitudes and practice regarding breast cancer early detection techniques (breast self-examination (BSE), clinical breast examination (CBE) and mammography) among Iranian woman. Methods: International (PubMed, ISI, and Google Scholar) and national (SID and Magiran) databases were reviewed up to September 2017 to identify articles related to the attitudes and practices of Iranian women concerning breast cancer screening behavior with reference to BSE , CBE and mammography. The screening steps, analysis of quality of the studies and extraction of the papers were performed by two reviewers. Results: Of the 532 studies included initially, 21 performed on 10,521 people were considered eligible. Subjects with a positive attitude toward BSE in various studies were 13.5% to 94.0% with an average of 47.6%. Positive attitudes to CBE and mammography were found in 21.0% and 26.4%, respectively. Participant performance of BSE ranged from 2.6% to 84.7%, with an average of 21.9%. The respective figures for CBE and mammography were 15.8% and 16.7%. Conclusion: Considering the poor performance and low rates for positive attitudes, it is suggested that educational programs should be conducted across the country.

Iranian journal of nursing and midwifery research

Parvin Mirmiran , Nasim Bahrami

Research shows that the age at menarche, as an essential element in the reproductive health of women, had been decreasing in the 19(th) and 20(th) centuries, and shows a huge variation across different countries. There are numerous studies performed in Iran reporting a range of age at menarche. Thus, this meta-analysis aimed to determine the overall mean age at menarche of the girls in Iran. All relevant studies were reviewed using sensitive and standard keywords in the databases from 1950 to 2013. Two raters verified a total of 1088 articles based on the inclusion criteria of this study. Forty-seven studies were selected for this meta-analysis. Cochran test was used for samples' homogeneity (Tau-square). The mean age at menarche of the girls in Iran with 95% confidence interval (CI) from the random effects was reported. The homogeneity assumption for the 47 reviewed studies was attained (Tau-square = 0.00). The mean (95% CI) menarche age of Iranian girls from the random effects...

Purpose Quality of life is the most important psychological factor affecting breast cancer patients. This study aimed to examine the health related quality of life of breast cancer patients in Iran. Methods International (PubMed, Web of science, Scopus and Google scholar) and national (SID, Magiran) databases were searched for related studies to September 2017. The quality of the articles was evaluated using the Hoy tool. Results Out of 232 initial studies, 18 studies performed on 2263 people were included in the final stage of the study. Based on the EORTC-QLQ-C30 and random effect method, the pooled mean score of quality of life in 1073 people was 57.88 (95% CI 48.26–67.41, I 2 = 97.90%) and the pooled mean score of quality of life based on WHOQOL-BREF in 357 people was 66.79 (95% CI 45.96–87.62, I 2 = 99.50%). Conclusion According to the results of the study, a moderate level of quality of life in women with breast cancer was indicated. Therefore, the use of multidimensional approaches can improve their quality of life. Keywords Quality of life · Breast Cancer · Iran · Systematic review

Global Journal of Health Science

mahmood moosazadeh

Reza Ghanei , Mohammad Farajzadeh , Kourosh Sayehmiri

Background: Many nurses experience job stress in their workplace. Given the wide range of differences in the statistics about job stress among nurses, the question that arises is what is the general prevalence of job stress among Iranian nurses? Objective: The present study aimed to evaluate the prevalence of job stress among Iranian nurses through meta‑analysis. Persian and English databases including SID, MagIran, IranMedex, Google Scholar, Sciencedirect, and PubMed were searched by using the keywords such as " job stress, occupational stress, work‑related stress, job related stress " and their combinations and 30 articles were finally selected. All the observational research articles that had information regarding the prevalence of job‑related stress, sample size, and job stress instruments were entered into the meta‑analysis. The form used to extract information included variables such as the first author's name, publication year, the place where the study had been carried out, type of the study, sample size, data collection instruments, and the most important findings. Results: The general prevalence of job stress was estimated to be 69% (confidence interval [CI] 95%: 0.58–0.79) based on the report of 30 papers with sample size of 4630. By region, type of hospital and the type of study, the highest prevalence of nurses' job stress was 90% (CI 95%: 0.85–0.96) in region one (Provinces of Alborz, Tehran, Qazvin, Mazandaran, Semnan, Golestan, and Qom), 70% (CI 95%: 0.60–0.80) in public and private hospitals, and 79% (CI 95%: 0.58–1.01) in studies where the type of study had not been mentioned. Conclusion: Given the high prevalence of job stress among nurses, developing programs to reduce nurses' job‑related stress seems to be essential. and limited power to make decisions would create considerable job stress. [9] Although job stress appears in all professions, jobs dealing with people are associated with serious stress. Nursing is one of these jobs and nurses suffer from high

Mahin S H E I K H Ansari

This study evaluates the role of the addict's social relations network on relapse and relapse prevention and presents an idea for addiction prevention and treatment

Kourosh Sayehmiri , Ali Delpisheh

Journal of Educational Evaluation for Health Professions

Eshagh Ildarabadi , Abbas Heydari , Hossein Karimi Moonaghi

Background and purpose: Aromatherapy, a CAM therapy, is a natural way of treating the mind, body and soul of individuals. The purpose of this study was to systematically review the literature to determine the effect of aromatherapy on hemodialysis complications. Methods: In this systematic review, international (PubMed, Google Scholar, Web of Science, CINHAL, EMBASE and Scopus) and national databases (SID and Magiran) were searched from inception of the databases to 30 De-cember 2017. Results: The results showed that aromatherapy reduced some of the complications of hemodialysis, including anxiety , fatigue, pruritus, pain of arteriovenous fistula puncture, sleep quality, depression, stress and headache. In one case, it improved the quality of life of hemodialysis patients. Conclusion: Considering the complications and heavy costs of managing complications in patients undergoing he-modialysis, it appears that aromatherapy can be used as an inexpensive, fast-acting and effective treatment to reduce complications in hemodialysis patients.

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Hamid Safarpour , Meysam Safi Keykaleh , Salman Daliri

Background: Domestic violence during pregnancy is a public health crisis, because it affects both mother ‎and fetus simultaneously, resulting in irreversible consequences for mothers and their ‎newborns. This study was performed to determine the prevalence of sexual violence during ‎pregnancy in the world and Iran as meta-analysis.‎ Methods: This study is a meta-analysis on the prevalence of sexual violence during pregnancy ‎in the world and Iran that was conducted on Persian and English published articles up to ‎‎2015. To this end, through searching the information by key words and their compounds at SID, Medlib, Irandoc, Google scholar, Pubmid, ‎ISI, Iranmedex, Scopus and Magiran, , all related articles ‎were extracted independently by two trained researchers. The results of studies analyzed using ‎the STATA and Spss16 software.‎ Results: In the initial searching of 167 articles, 33 articles related to Iran, 40 articles related to ‎other parts of the world and totally 73 articles met inclusion criteria for study. The prevalence ‎of sexual violence during pregnancy were estimated in the world 17% (CI95%:15% -18%) and ‎in Iran 28% (CI95%: 23% -32%).The prevalence of sexual violence during pregnancy in Iran is ‎‎11 percent more than the world.‎ Conclusion: According to the present meta-analysis results, the prevalence of sexual violence ‎during pregnancy in Iran is high. Given that sexual violence during pregnancy causes damage to ‎the mother and infant, it is recommended that the relevant authorities with the implementation ‎of intervention and educational programs reduce the prevalence of sexual violence during ‎pregnancy.‎

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Comparing two sampling methods to engage hard-to-reach communities in research priority setting

Melissa a. valerio.

1 Department of Health Promotion and Behavioral Science, University of Texas School of Public Health in San Antonio, 7411 John Smith Drive, Suite 1100, San Antonio, TX 78229 USA

Natalia Rodriguez

2 Center for Research to Advance Community Health (ReACH), University of Texas Health Science Center at San Antonio (UTHSCSA), 7411 John Smith Drive, Suite 1050, San Antonio, TX 78229 USA

Paula Winkler

3 South Central Area Health Education Center (AHEC), UTHSCSA, 7411 John Smith Drive, Suite 1050, San Antonio, TX 78229 USA

Jaime Lopez

4 Frio County AgriLife Extension, 400 S. Pecan Street, Pearsall, TX 78061 USA

Meagen Dennison

5 Karnes County AgriLife Extension, 115 N. Market Street, Karnes City, TX 78118 USA

Yuanyuan Liang

6 Department of Epidemiology and Biostatistics, UTHSCSA, 7703 Floyd Curl Drive, San Antonio, TX 78229 USA

Barbara J. Turner

7 Department of Medicine, UTHSCSA, 7703 Floyd Curl Drive, San Antonio, TX 78229 USA

Effective community-partnered and patient-centered outcomes research needs to address community priorities. However, optimal sampling methods to engage stakeholders from hard-to-reach, vulnerable communities to generate research priorities have not been identified.

In two similar rural, largely Hispanic communities, a community advisory board guided recruitment of stakeholders affected by chronic pain using a different method in each community: 1) snowball sampling, a chain- referral method or 2) purposive sampling to recruit diverse stakeholders. In both communities, three groups of stakeholders attended a series of three facilitated meetings to orient, brainstorm, and prioritize ideas (9 meetings/community). Using mixed methods analysis, we compared stakeholder recruitment and retention as well as priorities from both communities’ stakeholders on mean ratings of their ideas based on importance and feasibility for implementation in their community.

Of 65 eligible stakeholders in one community recruited by snowball sampling, 55 (85 %) consented, 52 (95 %) attended the first meeting, and 36 (65 %) attended all 3 meetings. In the second community, the purposive sampling method was supplemented by convenience sampling to increase recruitment. Of 69 stakeholders recruited by this combined strategy, 62 (90 %) consented, 36 (58 %) attended the first meeting, and 26 (42 %) attended all 3 meetings. Snowball sampling recruited more Hispanics and disabled persons (all P  < 0.05). Despite differing recruitment strategies, stakeholders from the two communities identified largely similar ideas for research, focusing on non-pharmacologic interventions for management of chronic pain. Ratings on importance and feasibility for community implementation differed only on the importance of massage services ( P  = 0.045) which was higher for the purposive/convenience sampling group and for city improvements/transportation services ( P  = 0.004) which was higher for the snowball sampling group.

Conclusions

In each of the two similar hard-to-reach communities, a community advisory board partnered with researchers to implement a different sampling method to recruit stakeholders. The snowball sampling method achieved greater participation with more Hispanics but also more individuals with disabilities than a purposive-convenience sampling method. However, priorities for research on chronic pain from both stakeholder groups were similar. Although utilizing a snowball sampling method appears to be superior, further research is needed on implementation costs and resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0242-z) contains supplementary material, which is available to authorized users.

A key feature of community-based and patient-centered outcomes research is partnering with community stakeholders from a project’s inception to ensure that it offers value to the community, is culturally appropriate, and is likely to yield sustainable improvements in prioritized outcomes [ 1 ]. Engaging persons from hard-to-reach or vulnerable communities has high priority, given evidence that lack of engaging racial-ethnic minorities and lower socioeconomic populations in research and decision-making contributes to disparities in enrollment in randomized clinical trials, cancer prevention, and access to evidence-based advances in medicine [ 2 – 5 ]. Effective strategies to proactively engage and learn from communities experiencing greater health disparities need to inform the development of community-partnered research [ 6 ].

Optimal sampling methods to engage community members to elicit ideas and priorities for community-based participatory research continue to be developed [ 7 , 8 ]. The Methodology Committee of the Patient-Centered Outcomes Research Institute (PCORI) highlighted improving patient engagement methods as one of four high priority areas for standards development [ 9 , 10 ]. Although groups experiencing the greatest health disparities likely have the greatest “stake” in ensuring that research meets their personal and community’s needs, well-recognized challenges in establishing partnerships between researchers and members from these communities can undermine efforts to address health disparities [ 5 ].

Commonly used sampling methods for identification of participants in community settings vary from random to purposive [ 11 , 12 ]. It might be expected that researchers should randomly sample from among eligible individuals in a community but this approach is resource-intensive and less demanding sampling methods have performed well in eliciting information that reflects broadly-held community beliefs and ideas [ 10 ]. Thus, non-probability sampling methods are preferred for recruitment of stakeholders. Table  1 presents the four sampling methods used most often and some of the challenges and benefits of each one. In addition, ethical challenges can arise in implementing strategies to recruit community partners such as misunderstandings about inclusion and exclusion criteria and potential loss of confidentiality and privacy. However, when conducted in close collaboration with community advisors, engaging representative samples of community members who are most affected by a specific issue such as obesity, HIV, and other issues can yield rigorous relevant research programs [ 13 – 15 ].

Review of four sampling strategies commonly used in community-engaged research

This study was designed to advance understanding of the impact of sampling in community-engaged research by comparing the effectiveness of two non-probability sampling methods to recruit and engage community stakeholders from hard-to-reach, vulnerable populations in identifying priorities for community-partnered research.

In two similar rural, predominantly Hispanic counties, one of two sampling methods was selected for use in each community: 1) snowball sampling, a chain-referral method where initial participants (seeds) recruit others from their social network or 2) purposive sampling, also known as judgmental, selective or subjective sampling (Fig.  1 ). Both methods have been used to recruit hard-to-reach subjects for research studies [ 16 , 17 ] but have not been compared for the purposes of developing research priorities. For this study, we recruited persons affected by chronic non-cancer pain, either personally or as a caregiver, to generate priorities for research on interventions that could improve outcomes of persons affected by chronic pain in their communities. We conducted a series of three facilitated group meetings, using the nominal group technique, to brainstorm, and prioritize ideas regarding services and support [ 18 , 19 ] for research on chronic pain.

Two Sampling Methods

Our second objective was to compare categories of ideas to improve outcomes of persons with chronic pain generated by participating stakeholders from these two rural, predominantly Hispanic communities. This real-world implementation study not only addresses a priority articulated by PCORI for methods development, it also offers unique insights into priorities for research addressing chronic pain in under-resourced, vulnerable communities.

For this project, we partnered with residents of two similar rural communities (Frio and Karnes Counties, Texas) that have had limited involvement in research to advance health despite significant health disparities. These communities are rural and predominantly low income Hispanic. The first community, Frio County, has an estimated population of 18,793 with 70 % of residents aged 19–64 years, 41 % women, and 78 % Hispanic ethnicity (of whom 13 % are foreign born) [ 20 ]. Frio County has a median household income of $35,681 and 26 % of residents live in poverty while 68 % completed high school and 23 % are medically uninsured. The second community, Karnes County, has 14,975 residents of whom 70 % are aged 19–64, 41 % women and 52 % Hispanic ethnicity (of whom 6 % are foreign born) [ 21 ]. The median household income is $44,650 and 22.3 % of residents live in poverty. Of Karnes residents, 19 % are uninsured, and 72 % have completed high school. Among residents of Frio and Karnes Counties, 12 % and 15 %, respectively, are disabled.

Study participants

The topic of chronic pain was selected as the community priority to guide the research because of its significant negative impact on health in the U.S., as highlighted in a report by the Institute of Medicine [ 22 ]. Residents of the community in both Frio and Karnes were eligible to serve as stakeholders for group meetings about chronic pain if they were 35–75 years of age and experienced chronic pain for at least three months that negatively affected their daily activities and/or sleep. Potential participants were excluded if their pain was due to cancer because of differing management priorities. We also excluded clinicians such as physicians and nurses because as trained experts they may have strong viewpoints and could dominate discussions with patients and caregivers as seen in other studies [ 23 – 26 ]. In addition, caregivers aged 35–75 of persons with chronic non-cancer pain were eligible for participation. In preparation for this project, our research team reviewed several options for non-probability sampling with a standing translational advisory board at our academic institution. Snowball sampling and purposive sampling were selected because the translational advisory board judged them to be likely to yield the desired diverse sample of community members.

The project first identified a lead community partner in each community. We successfully engaged the director and staff of Texas A&M AgriLife Extension Services in both counties because this organization shares our mission to improve the health of the community through evidence-based advances. Additionally, a member of our team had previously worked with this group on pilot projects. In each county, the AgriLife Extension agent recruited a community advisory board (CAB) to help develop and guide the study by: reviewing all recruitment materials and methods; assisting with stakeholder recruitment; addressing recruitment problems; and leading meetings to disseminate and act on community stakeholders’ ideas.

Sample size

The initial recruitment goal for the study was 130 participants, 65 per sampling method. This goal was based on concept mapping studies where 40 to over 100 participants were involved in idea generation [ 27 ].

Snowball sampling approach – community 1

Incentivized snowball sampling uses a modified chain-referral approach where a small number of recruits (seeds) meeting eligibility criteria and after consenting to participate then receive a small incentive for recruiting others from their social network who also meet eligibility criteria [ 28 ] (Table  2 ). Persons recruited by “seeds” identified others with desired characteristics and then those individuals identified others until either the sample size goal was achieved or the timeframe for recruitment ended. This approach has the advantage of efficiency and, when networks are broad, final recruits can be independent of initial recruiters [ 29 , 30 ]. For our project, a convenience sample of “seeds” were first contacted by a CAB member who used talking points developed by the CAB and team members to solicit interest in participation and offered a flyer about the project as additional information. If agreeable, a project coordinator then contacted the individual to assess for eligibility. Eligible “seed” stakeholders ( N  = 12) who consented to participate were informed about the project, provided the talking points developed by the CAB, and asked to recruit others who met study inclusion criteria. The “seeds” received a $5 gift certificate for each eligible recruit.

Characteristics of stakeholders recruited by sampling method

a Chi-Square test

b Fisher’s Exact test

c Two independent sample t test with unequal variances assumption

Purposive to convenience sampling approach – community 2

We initially used purposive sampling that identifies participants from specific constituencies from thorough analysis (or database, if available) of the target community’s characteristics and assets [ 31 ] (Table  2 ). Preparation for purposive sampling involved a review of relevant data about each community by CAB members and researchers [ 20 , 21 , 32 , 33 ] leading to the development of a matrix of categories of diverse constituencies and gender/age groups to guide recruitment. We aimed to recruit participants with chronic non-cancer pain from diverse community, social, and work organizations including: businesses, volunteer organizations (local Rotary and Lions clubs), faith-based groups, school districts, and agricultural groups such as local ranchers. However, several groups, especially businesses based outside of the community, refused to participate, citing company policies. After conferring with the CAB from community 2, we decided to supplement recruitment with convenience sampling (Table  2 ). At the CAB’s suggestion, we hired a part-time local businesswoman to assist with recruitment through her small franchise (selling snow cones) that served diverse community members. She was trained in the talking points and provided flyers about the project. After an initial contact, the recruiter then provided data about potential recruits for the research team to evaluate for participation.

Stakeholder meetings

Within each county, one sampling method was used to recruit three groups of stakeholders to attend a series of three meetings each lasting one to one and a half hours (total of 18 meetings with 9 in each county). All meetings were conducted in Spanish and English and held at convenient times and locations within the same 6 month period. Team members assisted persons with low literacy to understand and participate in activities. The first session provided an orientation about chronic pain and study procedures, including a video of a Hispanic patient describing her experience with chronic pain and a Hispanic primary care physician discussing his approach to managing chronic pain and challenges. The second session was led by an expert facilitator, one per community, and addressed a focused question: “What services or programs are needed to improve the lives of persons with chronic pain?” The participants generated ideas in response to this focused question in a brainstorming meeting structured by the nominal group technique. Developed by social-psychologists, the nominal group technique is the most commonly used structured group method to generate, combine, and prioritize ideas [ 34 ]. Initially, participants separately respond to a focused question and then list their ideas “round robin” style. All ideas are reviewed by the group in a facilitated discussion, categorized, and rated. This approach generates diverse ideas within a short timeframe, allowing each individual to contribute instead of only the most outspoken [ 19 ]. In the last session, participants grouped ideas into categories and rated separate ideas on both importance and feasibility on five point Likert-type scales. Study protocols were reviewed by the University of Texas Health Science Center at San Antonio Institutional Review Board and determined to be non-regulated research or exempt.

Results were analyzed using qualitative and quantitative methods. To examine differences in demographic characteristics of participating stakeholders in the two counties (e.g., demographic differences between two sampling methods), the chi-square test or Fisher’s Exact test was used for categorical variables and the two independent sample t test with unequal variances assumption was used for continuous variables. Because each group of stakeholders sorted their brainstormed ideas into somewhat different groups, three members of the research team independently reviewed and developed categories for the ideas generated by participating stakeholders [ 35 ]. Final categories for coding were developed after a discussion of differences among the categories that were previously generated. A final coding of all ideas was conducted by two coders and differences resolved after review by the research team.

A database was created of all community stakeholders’ rankings of their group’s ideas in regard to importance to improve outcomes of persons with chronic pain and feasibility of implementation on a five-point Likert-type scale (i.e., not at all important, somewhat important, very important, extremely important; feasibility scale constructed similarly). For each participant, the mean of their ratings on importance of all ideas within a unique category was calculated and another mean calculated for ratings on feasibility of all ideas within a category. Then these participant-specific mean ratings for each category on each dimension were averaged for all participants within the same community. The mean rating for each category on each of the two dimensions was compared between the two communities using two-sample t test with unequal variances assumption. Lastly, the CAB in each community reviewed these results and presented them to community leaders in order to develop strategies and research projects addressing the highest priorities.

Characteristics of the two groups of stakeholders recruited using two different sampling methods show many similarities (Table  2 ). The participants recruited by snowball sampling and purposive-convenience sampling were: mean age 58 versus 57 years, 69 versus 65 % women, and 84 versus 89 % preferring English (all p  > .05). The distribution of occupations also did not differ ( p  = 0.47). However, the snowball sampling strategy had a larger proportion of Hispanic participants than purposive-convenience sampling (87 versus 73 %, respectively, p  = 0.049) and a larger proportion of participants with a disability (47 versus 10 %, p  = <0.001).

Using the snowball recruitment strategy, 67 potential participants were contacted, 65 (97 %) were eligible, and 55 (85 %) consented to participate. Of these 55 stakeholders, 52 (95 %) attended the orientation meeting and 36 (65 %) attended all meetings (Fig.  1 ). In the other community, purposive sampling recruited 21 eligible stakeholders but was supplemented by convenience sampling to increase timely recruitment. Using this combined sampling method, 71 stakeholders were contacted of whom 69 (97 %) were eligible, 62 (90 %) consented, and 36 (58 %) attended. Overall, 26 (42 %) individuals participated in all meetings (Fig.  1 ). As shown in Table  3 , within each community, the same recruitment strategy recruited three separate stakeholder groups who attended the series of three meetings but snowball sampling consistently yielded higher attendance rates to all meetings. Comparison of characteristics of stakeholders who attended the orientation session (Table  4 left columns) reveals that in the first meeting, snowball sampling resulted in a higher proportion of Hispanic participants than purposive-convenience sampling (90 vs. 75 %, respectively p  = 0.052) as well as persons with disabilities (48 vs. 6 %, p  < 0.001), respectively. Among participants attending all three sessions (Table  4 right columns), the significant difference in disability status persisted ( p  = 0.006).

Stakeholder participation by sampling method group a

a In each county, three groups of participants met and each group attended a series of three meetings

b Calculated by dividing the number of participants who attended all three meetings by the number of participants consented to participate

Characteristics of stakeholders within each sampling method group attending first orientation session and three sessions

After brainstorming ideas about interventions needed to improve outcomes of community members with chronic pain (Sessions 1–2), stakeholders rated the priority of each idea on a five-point Likert type scale regarding importance and feasibility to implement. The ideas that were generated by each community and their importance rating are included (Additional file 1 ). Overall, importance ratings were higher than feasibility ratings (Table  5 ). For six of the eight categories, ratings on importance did not differ significantly between stakeholders recruited by the two sampling methods. Both stakeholder groups rated professional chronic pain support as very important on average. Notably, specific ideas categorized under professional services and support included a variety of non-pharmacologic sources of care such as a physical therapist, nurse counseling, and pain management support regarding mental health and other complications (Additional file 1 ). Stakeholders recruited by purposive/convenience sampling rated massage therapy significantly higher (diff = −0.35, p = 0.045 ) while stakeholders recruited by snowball sampling rated nutritional programs and city improvements/transportation services for persons with chronic pain more highly but the difference was significant only for the latter (diff = 0.62, p = 0.004 ).

Importance and feasibility of needed pain management services and support from community stakeholders grouped by recruitment method a

a Ordered by priority rating of the Snowball Sampling Group

b Two-sample t test with unequal variances assumption

None of the ratings on feasibility of implementing these interventions differed significantly between the groups of stakeholders, with most categories rated as being feasible or very feasible. The largest difference in feasibility ratings between the groups was observed for nutritional programs, which was rated as being more feasible by the stakeholders recruited by snowball sampling (diff = 0.50, p  = 0.059).

In a seminal report, Unequal Treatment, the Institute of Medicine highlighted limited acceptance and involvement in research by community members as a major barrier to sustainable implementation and adoption of health care advances [ 36 , 37 ]. Engaging community members starting from earliest stages of developing a research project can increase both acceptance and potential sustainability of research results within the community [ 10 , 38 ]. Community partnerships are especially critical in promoting participation from hard-to-reach populations in all phases of research [ 39 – 41 ]. However, methods to elicit community priorities, especially those of vulnerable, hard-to-reach communities, have been subject to limited evaluation. This study offers valuable insights from implementing two common non-probability sampling methods to recruit individuals from similar rural, predominantly Hispanic communities.

A major finding from this project was that purposive sampling, intended to achieve participation from diverse community constituencies, was challenging to implement largely because of limited cooperation from employers, especially those based outside of the community. In their review of sampling methods to engage stakeholders to identify research priorities, O’Haire and colleagues noted that databases of potential participants are often used for purposive recruitment [ 10 ]. For example, purposive sampling has been successfully employed to recruit physicians and leaders within a community [ 42 ]; however, at the population level, it is highly unlikely that a purposive sampling framework would be available and, if not, it would require significant effort to build. Because our research team lacked a database for purposive sampling and recruitment was flagging, the CAB in community 2 recommended that we transition to a convenience sampling method, as in other community engagement studies [ 43 ]. Convenience sampling was accomplished by hiring a part-time small business owner who interacted with a diverse cross-section of community members on a daily basis. She was trained by the CAB and our research team in recruitment methods and, in a short time, was able to identify potentially eligible low income, Hispanic residents with or affected by chronic pain as caregivers. A pragmatic approach to sampling that combines methods to accommodate challenges such as nonparticipation and inability to locate a target population has been adopted or promoted by others to engage hard-to-reach populations [ 44 – 46 ].

Another key finding was that snowball sampling recruited a larger number of eligible stakeholders. Other studies have found that snowball sampling is particularly effective in hard-to-reach or ‘hidden’ populations because it takes advantage of established social networks of persons with characteristics of interest [ 47 – 49 ]. The CAB in community 1 also served a vital role in operationalizing snowball sampling by identifying the “seeds” who were individuals with or affected by chronic non-cancer pain. This initial recruitment of seeds by the CAB can be regarded as a form of convenience sampling – which in this case resulted in a higher proportion of persons with disabilities with pain, possibly because it was evident to CAB members that these community members suffered from chronic pain. Thus, our snowball sampling method actually integrates initial convenience sampling.

In addition, attendance to all meetings was higher for the snowball sampling recruits than for purposive plus convenience sampling. Promotion of our project by the “seed,” who is a known community member, may have encouraged attendance. However, snowball sampling also recruited more participants who were more severely affected by chronic pain, as manifested by being disabled, compared with those recruited using purposive plus convenience sampling. Thus, careful attention to the characteristics of the seeds and their contacts is needed to promote a balanced representation of stakeholders in the community. Gratifyingly, both sampling methods resulted in recruitment of low-income, predominantly Hispanic community members though snowball sampling recruited a higher proportion of Hispanics.

Despite recruitment with different sampling methods, stakeholders affected by chronic pain in both counties generated relatively similar ideas and priorities for services and support needed to improve outcomes of persons with chronic pain. Professional treatment for chronic pain was rated as very important but this category includes multiple types of professionals delivering non-drug therapies. All community members also gave a high rating to massage therapy but the snowball sampling group in community 1 judged this to be less important, possibly reflecting this group being more disabled. Small studies suggest that the impact of massage on pain or functional outcomes may be diminished for disabled persons [ 50 ]. Other priorities included: non-professional support for chronic pain such as group meetings and activities such as arts and crafts; community education about chronic pain; and water therapy. These priorities differ significantly from those of persons with chronic pain who have been treated with long-term opioid analgesics because the latter group focuses primarily on logistics and challenges of obtaining these drugs [ 51 , 52 ]. These data also suggest that residents of these communities may have a better appreciation of the multi-faceted nature of pain management, in alignment with new national guidelines to use non-opioid drugs and complementary therapies as first line approaches to manage chronic pain [ 53 ]. A fertile line of research follows from these community priorities to study implementation and outcomes of evidence-based non-pharmacologic therapy in under-resourced, low income populations.

Limitations of this study include its location – rural Texas counties – that may not be relevant to other low income, predominantly minority communities. However, in support of generalizability to rural Hispanic communities, these two groups independently arrived at relatively similar ideas and priorities regarding services for chronic pain. The study had a relatively small number of participants but our sample size is similar to other stakeholder engagement studies [ 9 ]. The robustness of our results may have been increased because we convened three small groups within each county, fostering generation of more ideas through greater opportunities for each stakeholder to contribute. Another limitation relates to the adaptive design of our stakeholder engagement strategy. We transitioned from purposive to convenience sampling to achieve our recruitment goals. However, the similarity of priorities elicited from the two distinct communities supports the value of this combined approach to engage a hard-to-reach population.

The structure of our community partnership also likely contributed to the success of this engagement activity. We had a highly respected lead community partner in each community who spearheaded the establishment of a CAB to guide our stakeholder engagement activities. These CABs now serve as the foundation for ongoing community initiatives to operationalize and conduct research based on topics identified by the stakeholders.

Implications for Community Engagement- Implementation of sampling methods to recruit participants for community engaged research need to be guided by community partners. For our study, we relied heavily on two CABs to operationalize different recruitment methods in two similar rural, predominantly Hispanic communities. We found that purposive sampling was challenging to implement due to employers’ lack of cooperation, preventing access to diverse community constituencies; therefore, the CAB directed the research team to transition to convenience sampling. The snowball sampling method was more straightforward to implement and resulted in larger numbers of participants both initially and throughout the series of meetings. Furthermore, it yielded a higher proportion of Hispanic participants whose viewpoints were especially important to solicit in these majority Hispanic communities.

This study informs methods to engage stakeholders from vulnerable communities to identify research priorities by finding that snowball sampling conducted in partnership with a community advisory board achieved higher attendance rates and greater representation from low income Hispanics. Purposive sampling was more difficult to implement and required guidance from the community advisory board to augment recruitment with a convenience sampling approach. Nevertheless, stakeholders from both communities developed similar research priorities, focusing on diverse non-pharmacologic approaches that are not available in the community to address chronic pain. Future studies need to build on this novel study by examining associated resources and costs for differing sampling strategies to be utilized in hard-to-reach communities that need to be prioritized for research initiatives.

Acknowledgements

The authors would like to thank Kay Avant PhD, MSN; Jason Hill MS, MS; Jennifer Potter PhD, MPH and the Frio County and Karnes County Community Advisory Boards (CABS) for their contributions to this study.

This study was supported by the Patient-Centered Outcomes Research Institute by grant ME-13035729. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Availability of data and material

Full dataset generated and analyzed during this study from the corresponding author at [email protected].

Authors’ contributions

BJT is the principal investigator on the grant and was responsible for the planning and conduct of all stages of the research study, and drafting and final version this manuscript. MAV, NR, JL, and MD assisted in the planning and conduct of all stages of the study and MAV drafted the manuscript. MAV, NR and YL contributed to the data analyses and interpretation. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All study protocols were reviewed by the University of Texas Health Science Center at San Antonio Institutional Review Board and determined to be non-regulated research or exempt. Due to this project being a non-regulated research project, verbal consent was obtained from each participant using a script that explained the purpose of the project and the expectations for his/her role during recruitment conversations which were conducted over the telephone prior to stakeholder engagement sessions. The Institutional Review Board deemed the study of minimal risk as it did not include a non-routine intervention or interaction with a living individual for the primary purpose of obtaining data regarding the effect of the intervention or interaction, nor did the researchers obtain private, identifiable information about living individuals.

Abbreviations

Additional file.

Importance Rating of Categories of Ideas to Improve Outcomes of Chronic Pain Generated by 2 Sampling Method Groups. Table containing information presented and ranked at the county level of importance and feasibility of interventions for addressing chronic pain within the two communities sampled. (DOCX 24 kb)

Contributor Information

Melissa A. Valerio, Email: [email protected] .

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Paula Winkler, Email: ude.ascshtu@PrelkniW .

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ORIGINAL RESEARCH article

Attitudes of black american christian church leaders toward opioid use disorder, overdoses, and harm reduction: a qualitative study.

• 1 Department of Psychiatry, Recovery Research Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
• 2 Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, United States
• 3 Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI, United States
• 4 Department of Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
• 5 Center for Biomedical Research Excellence (COBRE) on Opioids and Overdose, Rhode Island Hospital, Providence, RI, United States

Introduction: Black American Christian church leaders are trusted community members and can be invaluable leaders and planners, listeners, and counselors for Opioid Use Disorder (OUD) sufferers in the opioid overdose crisis disproportionately affecting the Black community. This qualitative study examines the extent to which the knowledge, attitudes, practices, and beliefs of Black American church leaders support medical and harm reduction interventions for people with OUD.

Methods: A semi-structured interview guide was used to conduct in-depth interviews of 30 Black Rhode Island church leaders recruited by convenience and snowball sampling.

Results: Thematic analysis of the interviews identified four themes: Church leaders are empathetic and knowledgeable, believe that hopelessness and inequity are OUD risk factors, are committed to helping people flourish beyond staying alive, and welcome collaborations between church and state.

Conclusion: Black American Christian church leaders are a critical resource in providing innovative and culturally sensitive strategies in the opioid overdose crisis affecting the Black American communities. As such, their views should be carefully considered in OUD policies, collaborations, and interventions in the Black American community.

Introduction

The United States is experiencing a drug overdose crisis ( 1 ). The February 2022 Standford-Lancet Commission on opioid use in North America reported that opioid overdoses had caused more than 600,000 deaths in the United States and Canada since 1999 ( 2 ). By the end of the decade, the number of recorded deaths is projected to double ( 3 ). Near the mid 2010s, the opioid overdose crisis shifted from occurring predominantly among Whites to being essentially a crisis among Black American communities ( 4 ) due mainly to fentanyl-involved overdose deaths ( 5 ). Furthermore, over the years, while there has been a medicalization of drugs among White American communities, there has been widespread and disproportionate criminalization of drugs among Black Americans, with Black Americans 6-10 times more likely to be incarcerated for drug offenses compared to White Americans ( 6 ).

In Rhode Island, opioid overdose deaths for non-Hispanic Blacks rose from 26.4 to 49.3 per 100,000 from 2019 to 2020, the highest of any racial/ethnic group in the state ( 7 ). Rhode Island’s implementation of the first statewide program in 2016 to provide Medications for Opioid Use Disorder (MOUD) to incarcerated persons ( 8 ) with associated marked reductions in post-incarceration opioid overdose fatalities ( 9 ), was insufficient to reduce the 2019 to 2020 high opioid overdose deaths. The fentanyl contamination of the non-opioid drug supply was a contributing factor. The rate of fentanyl-involved fatal overdoses was highest among non-Hispanic Black Rhode Islanders compared to Hispanics and non-Hispanic Whites, with rates of 47.8, 20.4, and 24.8 per 100,000, respectively, in this period ( 7 ). Thus, almost all non-Hispanic Black deaths in the state associated with opioids involve fentanyl.

In response to fatal overdoses in the state, the Rhode Island government established the Governor’s Overdose and Prevention Taskforce in 2015 ( 10 ), comprising experts and community members. The Taskforce brings together professionals and community members working together to prevent overdoses and save lives ( 11 ). Additionally, the Rhode Island legislature passed, and the Governor signed a law permitting the first state-sanctioned pilot harm reduction centers in the country in July 2021 ( 12 ). To engage Black Americans and other minority populations, the Rhode Island government has adopted Connecticut’s Imani Breakthrough Recovery Intervention ( 13 ), a culturally informed approach to engaging Blacks and Latinos in substance use treatment based in churches of color.

The Black American church has been vital in disseminating other public health interventions ( 14 ), such as HIV prevention and education ( 15 ), influenza immunizations ( 16 ), and COVID-19 vaccinations ( 17 ). In addition to hosting 12-step groups like Alcoholics Anonymous ( 18 ), churches have promoted recovery from addiction efforts with Christian-based support groups, including Adult and Teen Challenge USA (a residential recovery program) and Celebrate Recovery (an evangelical Christian 12-step recovery support program) ( 19 ). While faith-based organizations could partner in expanding harm reduction services for high-risk populations, little is known about their attitudes and practices regarding OUD.

The first author interviewed Black Rhode Island Christian clergy to address the research question: What is the extent to which the knowledge, attitudes, practices, and beliefs of Black American church leaders support medical and harm reduction interventions for people with opioid use disorder (OUD)? The authors hope this study will inform and encourage church-based opioid overdose interventions, such as the Imani Breakthrough Recovery Intervention project underway in Rhode Island.

Research design overview

This qualitative research study used a semi-structured interview guide to conduct 30 interviews of Black Rhode Island church leaders recruited by convenience and snowball sampling.

Participants

Table 1 summarizes the demographic information and other characteristics of the church leaders. There were 19 (63%) male and 11 (37%) female church leader participants. The majority (63%) of participants were between 50 and 65 years. Half of them had no more than a college degree, and the other half had graduate degrees. Many were bi-vocational and had jobs besides their pastoral ministry, including a property manager, a community health worker, a chemist, clinical providers, and those serving in government agencies. Most congregants resided in Providence County of Rhode Island. Others lived in Massachusetts and Connecticut. Most congregations were Pentecostal. Others were Interdenominational, Church of God, African Methodist Episcopal, Nondenominational, American Baptist, Assemblies of God, and Evangelical. Most churches were multiracial, with a Black American predominance. Their congregant ethnicities included Caribbean, Cape Verdean, Liberian, Ghanaian, White American, and Native American. Additionally, congregation sizes ranged from nine to about 500. However, most congregations had less than 100 members.

Table 1 Characteristics of Black Rhode Island clergy interviewed about their attitude toward Opioid Use Disorder and harm reduction October 2021-January 2022.

The church leaders were recruited by convenience and snowball sampling methods. For instance, at an event introducing the Imani Breakthrough Intervention to the Rhode Island public, the first author was introduced to the Ministers Alliance of Rhode Island, a multicultural collaboration of Christian pastors and ministers in the state. Soon afterward, the first author delivered a presentation about the study to the Alliance at their monthly meeting and obtained 15 participants following the presentation. Many of these participants connected the first author to their colleagues to expand the pool of participants. The first author followed up with subsequent participants via emails and phone calls.

The inclusion criteria were Black clergy in predominantly Black churches, ages 18+ years, who were pastors or in other leadership positions in Rhode Island churches. The Harvard Longwood Campus Research Protocol Institutional Review Board granted exempt status for the research protocol.

Since completing the project, the first author has outreached to some clergy participants during several monthly meetings at the Alliance for their feedback on the study outcomes, and they supported the findings.

Data collection

An introductory email to participants included a request for their availability to participate in the research interview and an attachment of the consent document with the project details. A positive email response and verbal consent before the interview sufficed because of the study’s exempt status. The authors developed a semi-structured interview guide ( Supplementary 1 ) about the OUD crisis and harm reduction by drawing from previous related studies and content area experts. The first author ran the interview guide past a qualitative research and content area expert and updated it per their feedback before administering it. The interviews were conducted via the Zoom platform and included questions for the participants on the following topics:

i. Understanding the role of the interviewee within the organization and gathering details about the organization itself.

ii. Knowledge of the OUD crisis and beliefs

    iii. Knowledge of harm reduction techniques and beliefs about their efficacy and appropriateness

    iv. Opinions and experience with faith-based intervention methods to mitigate the opioid use disorder crisis.

    v. Demographic information of the church leaders and congregations

Data analysis

The first author followed a thematic analytic approach ( 20 ). 3Play Media ( 21 ) transcription service was employed to generate interview transcripts to aid in data familiarization and noting initial impressions. The data was managed with Dedoose qualitative analysis software ( 22 ) using an iterative analytic process with deductive codes capturing topics from the semi-structured interview guide and inductive codes derived from concepts relevant to the study, such as culturally informed approaches from the Imani Breakthrough Recovery Intervention literature. Codes and subcodes were refined iteratively by merging similar codes and associated quotes and regrouping codes and subcodes accordingly. The resulting codes were collated to identify underlying concepts and impressions to generate statements articulating themes. Further, compelling quotes were selected (see Results section) to highlight the themes.

Transparency and openness

The authors follow the journal article reporting standards for qualitative research design ( 23 ). Given how small the number of Black American clergy are in Rhode Island, study data, including recordings and interview transcripts, cannot be shared. Moreover, due to the deductive disclosure ( 24 ), it is almost impossible to de-identify the recordings and transcripts generated. As such, data sharing would breach the confidentiality implied in the participation agreement. To make this study transparent, however, the authors have included the semi-structured interview guide ( Supplementary 1 ), resulting codes and subcodes generated ( Supplementary 2 ), and thematic codes and subcodes with selected responses and associated Biblical references ( Supplementary 3 ) as Supplementary Information . This study’s design and its analysis were not pre-registered.

To minimize bias, the semi-structured interview guide questions ( Supplementary 1 ) were phrased in a neutral way to invite a conversation rather than yes-no responses. In addition, the guide used accessible language with terms defined as needed.

30 church leaders were interviewed from October 18, 2021, to January 4, 2022. The Zoom interviews lasted 70 minutes on average. All church leaders referenced Biblical texts. Supplementary 3 summarizes codes generated from the thematic analysis with selected responses and associated Biblical references ( 25 ).

The following four themes and selected related responses were identified from the analysis:

1. Church leaders are empathetic and knowledgeable about the OUD crisis in their communities. Church leaders’ knowledge, empathy, and compassion regarding the OUD crisis stem from their lived experiences. Some church leaders acknowledged the utility of Celebrate Recovery and Adult and Teen Challenge USA interventions for persons with substance use disorders. Other church leaders were involved in local church recovery support ministries and other ministries supporting social determinants of health by providing housing, education, medical care, employment opportunities, food, and clothing to the church and community.

A church leader who suffered a near-death experience with OUD and who had lost a sibling from an opioid overdose recounted how her faith contributed to her survival and subsequent recovery. Now, the majority of her congregants are persons grappling with substance use problems. As she recounted,

“I am constantly trying to remind people He (Jesus) did it for me. He can do it for you. I could not have done anything myself.”

Similarly, another church leader shared her experience of losing family members from drug overdose:

“It has even affected my own family. I’ve had three uncles die from drug overdoses.”

While a few church leaders with clinical backgrounds (including three clinical providers) were medically knowledgeable about OUD, other church leaders had contextual and historical OUD knowledge regarding the opioid overdose crisis in their communities. For many church leaders, the opioid crisis in their communities was not new:

“For the Black community, including the Black church, this is nothing new. We have been dealing with it for decades and decades and decades. Because we’re not talking about people who are out there. We’re talking about our sons and daughters. We’re talking about our fathers and mothers. We’re talking about our cousins. We’re talking about Sister So-and-so, who lives a couple of doors down.”

The church leaders understood culturally competent care. They know their community and understand what they need and are not getting. A leader’s narrative demonstrates the significance of engaging experts who can better relate to their community needs in providing OUD intervention:

“Some of these clinicians can’t relate to that. So they don’t understand. So we need those programs to help our people. But we need our people, we need the people that come from here, who knows this guy’s mother, knows this guy’s grandmother, to convince them to get involved, and help them, and wrap their arms around them, and bring it back to the way it used to be. And where do we find a lot of those people? In our churches.”

2. Church leaders believe that hopelessness and inequity are OUD risk factors . All clergy alluded to hopelessness as OUD drivers, including the trauma individuals and families experienced from the COVID-19 pandemic:

“I always believe that it’s a multifaceted issue. And with impact of the pandemic, particularly unemployment, additional stress, financial strain on families, and sense of hopelessness, it’s almost like what some might describe as the perfect storm for pushing certain people who may not have many healthier support options.”

The interviews also had many undertones of the inequity and injustice experienced in the Black American community when accessing OUD treatment facilities. For example, a church leader’s expressions suggested that some of their community members accessing OUD treatment facilities were ill-treated and not administered care with compassion, indicative of the concept of criminalization rather than the concept of medicalization:

“Black communities’ reception at these clinics and hospitals is among the poorest. When a Black person with addiction goes to the clinic for help, sometimes that is where they tend to see themselves as drug abusers, but not as people who have health problems.”

Moreover, these perceptions of poor reception when accessing OUD treatment facilities, coupled with inequity experienced by the Black community, further discourage them from accessing resources for OUD:

“First of all, as Black people, we do not really believe in a system that was not designed for us. And when you’re already disenfranchised, and you’re already down, you’re already marginalized, it’s not like you want to go and be mistreated again.”

3. Church leaders are committed to helping people flourish beyond staying alive. Generally, the church leaders appreciated the benefits of MOUDs and harm reduction methods, such as naloxone, that would set OUD individuals on a path to remission and thriving, not only keep people alive. All advocated for counseling and preventive harm reduction methods, including MOUDs. For instance, a church leader’s response to his opinion about faith-based intervention methods to mitigate the OUD crisis was:

“I will use faith to believe in God and bring professional counselors and medical experts to help folks using these drugs. I believe in the treatments, and I believe that the same God who changes and touches lives also gives us knowledge and wisdom to treat people so that people will get out of drugs. Because the thing has both physical and spiritual aspects, we need to also deal with the spiritual elements: prayer, counseling, and believing God to touch them.”

However, most of the church leaders were conflicted over those harm reduction interventions that enabled the confident utilization of opioids, such as clean syringe exchanges and fentanyl test strips and the establishment of harm reduction centers. Their teachings and convictions did not favor misusing opioids or any other substance that would jeopardize a person’s health and well-being. A church leader was almost to the point of tears sharing about her conviction:

“Would you encourage your child to use the test strip first, or are you trying to bring them to Christ? We’re supposed to bring people to do what’s godly. We can’t do that while inventing new ways to cheat.”

They were also convinced that the church establishment is critical in addressing the spiritual and deep-rooted issues of meaning, purpose, and value, allowing people to live and flourish beyond MOUDs and harm reduction methods. For instance, a church leader shared about his conviction:

“The church has a responsibility. I believe in addressing it from a holistic perspective. We are the only entity on the planet with the right and authority to deal with the whole person, the spirit, the soul, and the body, right? Because in my understanding, in my position as a spiritual leader, not every problem is spiritual. What is spiritual, you address it spiritually. What is medical, address it medically. Now, if it is a combination of both, you use both to address it.”

Further, beyond MOUDs and harm reduction methods that keep people alive, church leaders would prioritize prevention efforts such as assisting individuals with their social determinants of health needs such as safe housing and communities, better living conditions, and access to resources to enable individuals and families to flourish. A church leader with personal lived experience on the streets, now ministering to homeless persons, many of whom suffer from OUD, shared:

“I think the homeless crisis is a major start to getting some of these people out of the streets into a safe environment where they can pick up the pieces in their lives. I don’t mean put them in the projects that are already infested with drugs, somewhere where they can feel good about living, and look around, and say, OK, now I have somewhere to store my medication. I have somewhere where I can get mail, you know? I have somewhere to put on clean clothes, get up, and go to a job interview. You’re not going to get that in a shelter, you know?”

4. Church leaders welcome collaborations between church and state. While all the church leaders were pleased with the Rhode Island government’s endorsement of the Imani Recovery Breakthrough Intervention, some expressed a disconnect while engaging with the Rhode Island Governor’s Overdose and Prevention Taskforce. A church leader who was invited to a Taskforce meeting felt out of place:

“I remember the first time I went there [Governor’s Overdose and Prevention Taskforce]; I felt like I was out of the league. I wasn’t in. You know what I mean? I think it was more of the medical field, with the social workers, and with others. So I sat there for a few minutes, and I’m going, you know what, this is not what I should be part of. And I tried again, and it was still the same. So I think they would be more involved if they made faith leaders comfortable.”

Nevertheless, the church leaders are open to partnering with state authorities around the OUD crisis. They are also open to collaborating with state authorities around the OUD crisis. According to a church leader:

“Spiritual care providers or clergy and lay leaders are not in competition with professional health care providers. Our work is complementary to the work of professional healthcare providers. Fighting opioid addiction requires an intentional, integrated effort by both spiritual and secular community leaders.”

Church leaders will fully engage with state authorities and leaders in OUD intervention efforts when they are given autonomy and their values are recognized and appreciated, as elaborated by this church leader:

“Empowering the Church with additional tools and resources, including training and specific education, is a step in the right direction. The false objection based upon the concept of the separation of church and state must not be brought into the conversation. If so, the Church-Faith-Based approach will be anemic and impotent. Faith-Based and Church-Faith-Based frameworks should be seen as different approaches. The former includes the discussion of God, while the latter could involve the Head of the Church - Jesus Christ, His Word [the Biblical scriptures] and the Holy Spirit.”

This qualitative study of 30 semi-structured interviews of Black Rhode Island church leaders examined their views toward Opioid Use Disorder and harm reduction. The major finding of the study was that Black American church leaders deeply care about the health and wellbeing of people with Opioid Use Disorder. While the Black American church leaders are concerned about interventions they perceive as perpetuating use, such as fentanyl test strips or sterile syringe exchanges, they are open to many harm reduction interventions, including counseling, Medications for Opioid Use Disorder, and naloxone. The themes emerging from the interviews highlight opportunities to engage church leaders who are highly respected and influential community members to address the ongoing opioid crisis in Black communities.

The theme, Church leaders are empathetic and knowledgeable about the OUD crisis in their communities , resonates with Jerome Adams, MD, MPH, previous US Surgeon General, who appreciated the critical role of the faith community in approaching the Opioid Crisis in their communities with compassion and a sense of a call to duty. In his statement, “Keeping Faith, Bringing Hope and Healing in the Midst of the Opioid Crisis,” the Surgeon General recognized the contributions of faith communities along with social service agencies in encouraging access to MOUDs while promoting recovery services and prevention of substance misuse ( 26 ). The personal, familial, and medical close encounters with opioid overdoses by several church leaders developed empathy toward persons with OUD and provided contextual knowledge about the OUD crisis. Moreover, some church leaders in this study were involved in substance use prevention and recovery efforts locally in their congregation and through widespread Christian-based recovery ministries such as Adult and Teen Challenge USA, a residential recovery program. Adult and Teen Challenge USA employs a Bible-based curriculum to aid individuals in their recovery journey holistically (psychologically, socially, physically, and spiritually) ( 27 ). The economic benefit of faith-based residential recovery programs is significant. An impact evaluation study to assess the financial impact of a faith-based long-term residential addiction recovery intervention, the Mission, in Baltimore, Maryland, revealed that every person who participates in the Mission for a year saves the state and county governments $14, 263. These savings result from less spending on health care, social services, and criminal justice utilization ( 28 ). The study participants comprised 5,122 homeless men recovering from substance use disorder from 2006 to 2019. Surmising from this impact evaluation study, Adult and Teen Challenge USA, would be a cost-effective recovery intervention.

In addition to their lived experiences, several church leaders provided historical accounts of the OUD crisis, emphasizing that the OUD crisis was not a new epidemic in the Black American community. Historically, Black Americans accounted for the majority of the heroin use disorder crisis of the 1960s and 1970s when drug laws against this heroin-related opioid epidemic predominantly affected Black American and minority communities, resulting in a disproportionate prison population legacy ( 29 , 30 ). Relatedly, the profile of heroin users in the 1960s and 1970s were minority males ( 31 ). The disparate incarceration of minority populations persists in more recent times. For example, in 2001, 94% of imprisoned drug offenders in New York were Blacks and Hispanics compared to 5.3% Whites ( 32 ). The criminalization of minority heroin opioid users is in stark contradiction to the medicalization approach to OUD, viewing the opioid crisis as a public health problem when it is also plaguing Whites ( 33 ).

Moreover, financial restraints leading to inadequate health insurance, distrust of the medical community, and healthcare infrastructure from historical mistreatment and stigma are barriers to assessing healthcare services for OUD ( 29 ). These barriers lead to the second theme: Church leaders believe that hopelessness and inequity are OUD risk factors. Furthermore, consistent with the sentiments expressed by some church leaders, research has revealed the ethnic-discordant relationship between Blacks and health professionals, leading to Blacks disliking their experiences with their medical care compared to Whites ( 34 ).

Unfortunately, despite the shift in the focus of the epidemic from criminalizing it to treating it, the black population is still impacted disproportionately compared to their white counterparts ( 29 , 35 ). Researchers have attributed the disparity in OUD treatment outcomes for black populations to omitting them from discussions around the OUD epidemic ( 29 , 35 ) an indication that providing OUD treatments and intervention strategies to Black and other minority populations should take cultural competence into account.

All the church leaders advocated for primary prevention and recovery strategies such as counseling and role modeling to safeguard youth and families from engaging in substance misuse. They also advocated for supporting people in recovery, especially in addressing trauma and life challenges. The clergy would rather commit to providing these support strategies to enable people to flourish beyond merely keeping them alive through medical interventions. The study participants’ prevention and recovery outlook for OUD was consistent with researchers ( 36 ), who demonstrated that spiritual assistance and religious participation can help prevent misuse of substances in young adults and aid persons in addiction on their recovery journey. Their findings mirrored other studies demonstrating associations between spiritual practices and recovery from substance use disorder ( 37 ) and showing relationships between perceived spiritual support and increased self-efficacy and less cravings from substance use disorder ( 38 ). These outcomes are associated with hope. Specifically, a study investigated the association between distress tolerance and general and religious or spiritual hope for ethnic minorities, including Blacks, compared to non-Hispanic Whites, in a nationally representative adult sample (N=2875) ( 39 ). The researchers showed that ethnic minorities generally experienced lower degrees of psychological distress compared to non-Hispanic whites. Moreover, the ethnic minority groups, including Blacks, experiencing lesser degrees of psychological distress indicated higher degrees of religious or spiritual and secular hope compared to non-Hispanic Whites who are intolerant to distress.

The religious or spiritual orientation of hope by ethnic minority groups is critical in coping with challenges beyond their capacity to contain ( 40 ), including the inequities and injustices that Blacks face compared to Whites in the face of the OUD crisis. Against this backdrop is the third theme: Church leaders are committed to helping people flourish beyond staying alive. For the clergy, providing OUD treatment options and interventions alone is inadequate if they do not offer holistic care for the people. They believed that their faith and religious practices could address the deep-seated needs of people suffering from substance use disorders ( 19 ), including OUD.

Stemming from this viewpoint, most of the clergy discouraged the use of harm reduction methods they perceived as perpetuating use, including clean syringes and fentanyl test strips. This finding was congruent with an online survey of (n=133) faith leaders’ views of a needle exchange program in Illinois ( 41 ). Per the survey, the faith leaders supposed that the needle exchange program would increase drug use, though they also appreciated that the needle exchange programs would reduce blood-borne infections. This mixed response to the study findings is also captured in the present study as the “Inner Value Conflict” subcode. (Please see Supplementary 3 for more selected responses and associated Biblical references).

The overwhelming support for the church-based drug overdose intervention, the Imani Breakthrough Recovery Intervention, endorsed by the Rhode Island government, is suggestive of the fourth theme: Church leaders welcome collaborations between church and state. The Imani Breakthrough Recovery Intervention is a community-based participatory research (CBPR) approach to address the rising cases of drug overdose among Blacks and Latinos, began in partnership with the State of Connecticut Department of Mental Health and Addiction Services, funded by the Substance Abuse and Mental Health Services Administration (SAMHSA) ( 42 ). Parishioners and persons with lived experience facilitate the innovative intervention run by Black and Latino churches. The 22-week intervention is unique in addressing the effect of trauma and racism experienced by these racial minority communities relative to substance use disorder, including OUD. The intervention addresses the inequities in social determinants of health (SODH) encountered by these communities by providing wraparound support and life coaching while focusing on SAMHSA’s eight dimensions of wellness (physical, intellectual, environmental, spiritual, social, occupational, emotional, and financial health) ( 43 ) and the Citizenship Enhancement model brought about by determining the factors necessary for community reintegration by persons previously incarcerated or with mental illness. The Citizenship Enhancement model appreciates the need for access to employment opportunities, healthcare, housing, and a sense of belonging ( 44 ). A report showing 42% retention of Imani intervention participants at 12 weeks and data leading to a significant increase in Citizenship Enhancement scores from baseline to week 12, the Imani Breakthrough intervention shows promise in addressing the SODH disparities of people who use substances such as opioids in Black and Latino communities ( 42 ). The Imani Breakthrough is, thus, at the heart of the church leaders’ desire to enhance and rebuild lives to flourish and shows promise in providing culturally informed recovery intervention for Blacks and Hispanics.

These collaborations between faith-based organizations and state agencies are necessary to engage persons in addiction, their family networks, and communities in their recovery journey ( 45 ), who would otherwise be logistically unreachable by federal and state organizations. Furthermore, researchers have challenged the strict application of separation of church and state, allowing churches to use government funding to advance a social service without promoting religious activities ( 19 ). The researchers posit that this delineation between religious practices and government-funded social programs can lower the impact of faith-based interventions when the religious practices and beliefs are segregated from the intervention. Likewise, the Black church leaders in this study asserted the lower effect of faith-based initiatives not fully embracing their core beliefs.

This study adds to a growing body of knowledge showing the critical role of church leaders in addressing the OUD crisis in black and other ethnic minority communities. The church community is an intimate bridge connecting available interventions and resources to persons experiencing OUD, their families, and the community. Given that the success of OUD church-based initiatives is largely descriptive and anecdotal, more rigorous study designs, including independently cross-checking derived codes from different researchers and computing intercoder agreement, and mixed methods approach to triangulate data from different sources. Longitudinal and quantitative research methods, quantitative evaluation methods, cost-effectiveness, and economic impact assessments, should be employed to assess the public health utility of church-based interventions for OUD. Additionally, researchers should continue to include culturally centered, disparities reduction and community-engaged research approaches such as CBPR methods in interventions and study designs to empower Black and racially ethnic minority communities in discussions around the OUD crisis. These design methods can collectively provide innovative and targeted approaches for nontraditional partners to work together for high-risk groups in the fight against OUD.

Black church leaders have an affinity for primary prevention and flourishing in recovery strategies in OUD intervention efforts. Black church leaders are trusted community members and can be invaluable leaders, planners, listeners, and counselors for OUD sufferers. They are a critical resource in providing innovative and culturally sensitive strategies in the opioid overdose crisis affecting the Black American communities. Their views should be carefully considered in OUD policies, collaborations, and interventions in the Black American community.

This study’s limitations include convenience and snowball sampling recruitment methods that may be less representative of Black American clergy. While comparable to other qualitative research studies, it is worth noting that this sample was 30 clergy members and may not represent the perspectives of all Black clergy members. In addition, many of the churches were in Rhode Island’s Greater Providence community, which may have led to participants’ concerns about anonymity and confidentiality.

Data availability statement

Given how small the number of Black American clergy are in Rhode Island, study data, including recordings and interview transcripts, cannot be shared. Moreover, due to the deductive disclosure, it is almost impossible to de-identify the recordings and transcripts generated. As such, data sharing would breach the confidentiality implied in the participation agreement. To make this study transparent, however, the authors have included the semi-structured interview guide ( Supplementary 1 ), resulting codes and subcodes generated ( Supplementary 2 ), and thematic codes and subcodes with selected responses and associated Biblical references ( Supplementary 3 ) as Supplementary Information . Requests to access the datasets should be directed to AD, [email protected].

Ethics statement

The studies involving humans were approved by The Harvard Longwood Campus Research Protocol Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because a positive email response to an informed consent document and obtaining verbal consent before the interview sufficed due to the study’s exempt status.

Author contributions

AD: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. RS: Funding acquisition, Supervision, Writing – review & editing. IW: Supervision, Writing – review & editing. MM: Supervision, Writing – review & editing. JR: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by a fellowship award through the Recovery Research Institute, Department of Psychiatry, Massachusetts General Hospital by the National Institute on Drug Abuse (R24DA051988) of the National Institutes of Health, a grant from the Center for Biomedical Research Excellence (COBRE) on Opioids and Overdose (P20GM125507), and a fellowship from the Harvard FXB Center for Health and Human rights. Dr. IW is partially supported by the Providence/Boston Center for AIDS Research (P30AI042853) and by Institutional Development Award Number U54GM115677 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds Advance Clinical and Translational Research (Advance-CTR) from the Rhode Island IDeA-CTR award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Acknowledgments

The authors thank the clergymen and clergywomen who were interviewed. Many thanks also go to Chyrell Bellamy, MSW, PhD, Professor at Yale University’s Department of Psychiatry and Co-Principal Investigator of the Imani Recovery Breakthrough Intervention, for her assistance in drafting the qualitative interview questions. For their advice in preparing this manuscript, the authors also wish to thank Emma-Louise Aveling, PhD, MPhil, a Research Scientist at the Harvard T. H. Chan School of Public Health in the Department of Health Policy and Management, for her qualitative research method expertise, Howard Koh, MD, MPH, Professor of the Practice of Public Health Leadership at the Harvard T. H. Chan School of Public Health and the Harvard Kennedy School, and Co-Director of the Initiative on Health, Spirituality, and Religion at Harvard and David Heckendorn, a chaplain at Harvard University, affiliated with the InterVarsity Christian Fellowship. While this manuscript is not under review in any other publication and has not been previously published, this work is from the first author’s thesis embargoed until 4-23-2025 at the Digital Access to Scholarship at Harvard ( 46 ).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1359826/full#supplementary-material

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Keywords: Opioid Use Disorder, opioid overdose, Black American, qualitative, Christian, church leaders, harm reduction, clergy

Citation: Dankwah AB, Siegrist RB Jr., Wilson IB, McKenzie M and Rich JD (2024) Attitudes of Black American Christian church leaders toward Opioid Use Disorder, overdoses, and harm reduction: a qualitative study. Front. Psychiatry 15:1359826. doi: 10.3389/fpsyt.2024.1359826

Received: 22 December 2023; Accepted: 11 March 2024; Published: 03 April 2024.

Reviewed by:

Copyright © 2024 Dankwah, Siegrist, Wilson, McKenzie and Rich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Akosua B. Dankwah, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.