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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.

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

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4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques 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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Chapter 5. Sampling

Introduction.

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

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

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

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

Quick Terms Refresher

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

The “Who” of Your Research Study

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

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

Sampling People

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

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

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

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

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

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

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

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

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

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

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

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

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

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

When Your Population is Not Composed of People

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

Case Studies

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

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

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

Content: Documents, Narrative Accounts, And So On

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

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

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

What is the Correct “Number” to Sample?

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

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

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

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

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

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

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

How did you find/construct a sample?

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

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

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

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

Examples of “Sample” Sections in Journal Articles

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

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

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

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

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

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

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

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

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

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

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

Final Words

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

Table 5.1. Sampling Type and Strategies

Further Readings

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sampling Techniques for Qualitative Research

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

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

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

Introduction.

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

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

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

Sampling in Qualitative Research

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

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Qualitative Sampling Methods

Affiliation.

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

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

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

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

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10.2 Sampling in qualitative research

Learning objectives.

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

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

Nonprobability sampling

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

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

Types of nonprobability samples

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

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

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

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

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

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

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

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

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

Key Takeaways

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

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

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

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7.2 Sampling in Qualitative Research

Learning objectives.

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

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

Nonprobability Sampling

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

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

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

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

Types of Nonprobability Samples

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

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

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

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

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

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

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

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

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

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

Table 7.1 Types of Nonprobability Samples

Key Takeaways

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

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Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 30 April 2024

Explaining the barriers faced by veterinarians against preventing antimicrobial resistance: an innovative interdisciplinary qualitative study

• Razie Toghroli 1 ,
• Laleh Hassani 1 ,
• Teamur Aghamolaei 1 ,
• Manoj Sharma 2 ,
• Hamid Sharifi 3 , 5 &
• Maziar Jajarmi 4  

BMC Infectious Diseases volume  24 , Article number:  455 ( 2024 ) Cite this article

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Metrics details

Considering the significance of increased antimicrobial resistance (AMR) and its adverse effects on individual and social health and the important and effective role that veterinarians play in controlling this growing issue worldwide, it is essential to have effective preventive control programs. To this aim, the first step is to identify the factors behind the prevalence of AMR in Iran and the barriers veterinarians face to controlling this problem. Thus, the present study was conducted to explain the barriers veterinarians faced in the prevention of AMR from an Iranian veterinarian’s perspective.

The present research was done in three cities in Iran in 2021. The data were collected through in-depth interviews with 18 veterinarians selected through purposive and snowball sampling and analyzed using conventional qualitative content analysis.

The data analysis results were classified into 4 main categories and 44 subcategories. The former included: educational factors, administrative/legal factors, client-related factors, and veterinarian-related factors.

Conclusions

The increased AMR can be approached from multiple aspects. Considering the different factors that affect the increased AMR, it is necessary to consider them all through effective planning and policy-making at multi-level and multidisciplinary dimensions. There is special attention needed to scientific and practical interventions at the individual, interpersonal, social, and even political levels. At the same time, measures should be taken to rehabilitate and maintain the health of society to strengthen supervision and attract the full participation of interested organizations.

Peer Review reports

Introduction

Antimicrobial resistance (AMR) refers to the reduced effectiveness of antimicrobial agents, such as antibiotics, antivirals, antifungals, and antiparasitics, against infections caused by bacteria, viruses, fungi, and parasites [ 1 ]. This phenomenon makes infections harder to treat and increases the risk of disease spread, severe illness, and death. Misuse and overuse of these agents in humans, animals, and plants are key contributors to the development of AMR. AMR is a natural process that occurs gradually over time through genetic changes in microorganisms, but human activities, particularly the improper use of antimicrobials, significantly speed up this process. Veterinarians play a vital role in managing AMR, as they frequently prescribe antimicrobials to protect animal health. However, overprescription and misuse of antimicrobials in veterinary practice contribute to the development of AMR in humans. AMR presents a substantial challenge to global public health and economic stability. If left unchecked, AMR will lead to increased healthcare costs, decreased productivity, and potentially millions of avoidable deaths annually. To combat AMR, governments, healthcare providers, and researchers must collaborate to implement policies promoting judicious antimicrobial use, invest in innovative therapies, and foster educational initiatives to empower individuals to understand the importance of responsible antimicrobial stewardship [ 2 ].

AMR is an increasingly global issue that needs to be settled cooperatively. Resistant organisms exist in animals, humans, the environment, the food, and the main cause of this, is antimicrobial usage. AMR will become a leading cause of mortality in the world in the near future. As reported by some studies, by 2050, AMR will be the main cause of death on a global scale, which surpasses cancer deaths [ 3 , 4 ].

The mortality rate caused by microbial resistance is higher than the total number of deaths induced by cancer worldwide. Yet, the former has been largely neglected and, instead, issues such as cancer and how to treat it have been addressed more [ 5 , 6 , 7 ].

Unintentional antibiotic ingestion occurs frequently due to the widespread use of antibiotics in society. According to a report from 2010, approximately 10 pills, capsules, or teaspoons of antibiotics are taken annually by every person on Earth, which suggests a high degree of accidental consumption. Healthy individuals who consume significant amounts of antibiotics unintentionally may experience negative impacts on their health, particularly concerning the disturbance of the normal microbiome. This disturbance can lead to long-term complications, such as an increased risk of developing conditions like type 1 and 2 diabetes, inflammatory bowel diseases, celiac disease, allergies, and asthma [ 8 ]. Additionally, antibiotic exposure can contribute to the emergence of antibiotic-resistant strains, posing a challenge to public health. It is essential to note that antibiotics are necessary and lifesaving medicines when used appropriately under medical supervision. However, excessive or unnecessary use of antibiotics can pose risks to individual and population health [ 9 , 10 ].

According to the report of the World Health Organization, half of the antibiotics produced in the world are used in medicine and the other half in veterinary, agriculture and aquaculture [ 11 ]. In general, there is no difference between antibiotics used in veterinary medicine and antibiotics used in medicine. These drugs are used to prevent and treat diseases and promote growth in animal farms (pigs and poultry), unfortunately, the use of antibiotics in veterinary medicine leads to leaving residues in meat, milk and eggs [ 12 ]. Drug residues in food have adverse effects such as antibiotic resistance in humans, allergies, and inhibition of bacterial starter cultures used in dairy fermentation industries [ 13 ]. Despite the beneficial effects of antibiotics on the treatment of livestock infectious diseases, the presence of their residues in milk and animal meat, as well as their transfer to the human body have adverse effects on health, industry, and economics. As reported by the National Center for Rational Prescription of Antibiotics, consuming antibiotics in Iran is 16 times as high as the global standard. Some researchers believe that the spread of microbial resistance to antibiotics results not only from the unnecessary prescription and use of these compounds in humans but also from the widespread use of antimicrobial drugs in veterinary medicine. It has caused the transfer of such pathogenic bacteria from animals to human pathogens. The main difference between microbial resistance to antimicrobial drugs in humans and animals is that microbial resistance in humans affects the individual, whereas microbial resistance in livestock affects a large population due to the consumption of raw animal products by humans. Exposure to both resistant bacteria and antibiotic compounds prescribed for the treatment of infectious diseases for livestock through transmission causes the accumulation of drugs and drug residues in raw livestock products. It seems that attempts to prevent the occurrence of microbial resistance in livestock and its consequences for humans are effective and can be implemented efficiently by veterinarians and those active in this domain. What veterinarians can do with this respect is wide-ranging.

Most of the studies conducted in Iran in the field of antimicrobial resistance were in the medical and human fields, and the studies conducted in the veterinary field were mostly quantitative. A systematic review and meta-analysis showed a high level of antibiotic resistance in Staphylococcus aureus bovine mastitis in Iran. This pathogen is the common and main cause of bovine bacterial mastitis, which leads to high economic losses and can easily lead to the transmission of these treatment-resistant bacteria to humans [ 14 ]. In a qualitative study, which is one of the few qualitative and phenomenological studies conducted in Iran in the field of AMR, the lived experience of livestock breeders, their role and views in this field has been investigated. The results of this study have confirmed the importance of antibiotic resistance in Iran and the lack of existing research in this field, especially with a qualitative approach [ 15 ]. In another study that was conducted with semi-structured interviews with key stakeholders in Iran, including managers of the Ministry of Health, Iran Veterinary Organization, national professional associations and researchers through thematic analysis, the international enabling and predisposing factors related to It identified the control of AMR in Iran. The enabling factors that have been highlighted in this review were discussed in general, and more attention was paid to political factors such as formulation and implementation processes, and AMR surveillance, and challenges such as the smuggling of infected animals and antimicrobial drugs and livestock from neighboring countries and the impact of imposed sanctions. The review emphasizes the global nature of AMR as a challenge that requires consensus and international cooperation to effectively deal with this issue, but it does not specifically and specifically deal with why and analyze how AMR occurs and examine ways to prevent it, and only generally with the approaches Emphasizes political, including health diplomacy, to strengthen national efforts in the fight against AMR [ 16 ]. However, the present study, in an interdisciplinary manner, has specifically addressed one of the most important fields involved in the occurrence of AMR in human societies, and a field similar to it has received less attention before.

Yet, it is hard to make interventions with veterinarians directly involved because they are not easily available for research; therefore, veterinary students are the best and closest population for interventional studies. If this population adequately understand the principles of prescribing antibiotics, this successful learning will be productive in practice too [ 17 ].

Overcoming this problem will be possible with an One health approach, taking into account humans, animals, and environmental health altogether [ 18 , 19 , 20 ].

Today, AMR occurs in humans, wildlife, domestic animals, plants, and our environment directly by using antibiotics, and there is a risk on a much larger and more significant scale in animal-source foods consumed by humans indirectly. So it is logical to take a multidisciplinary health approach to solve this problem by eliminating the inappropriate use of antibiotics [ 21 , 22 ].

In the medical domain, extensive research has been done to examine physicians’ beliefs about prescribing antibiotics [ 23 ]. Many interventions have been made to reduce physicians’ over-prescription. The various aspects of antibiotic prescription have been extensively investigated so far in medical and clinical sciences. However, these interventions alone have not managed to prevent the occurrence of this important event. Thus, resolving this problem needs a multidimensional approach [ 24 ] .

Moreover, veterinarians prescribing drugs without using paraclinical services and selling over-the-counter (OTC) drugs are very common in several countries including Iran. In many stockbreeding industries, antibiotics are widely used not only for medical purposes but also as growth stimulants. There has been a serious lack of effective monitoring of these patterns of use. Similarly, there has scarcely been any strict preventive rule for this. Thus, it is likely that the AMR incidence rate is high in countries such as Iran [ 25 , 26 ].

A vast majority of research so far on the effect of AMR has only addressed this issue in human health [ 16 , 24 ]. In veterinary medicine, the body of existing literature has been limited to laboratory research and animal health. Veterinarians’ role in integrated health, especially AMR has not been adequately addressed. Therefore, there is no complete and clear understanding of veterinarians’ mental patterns and perceived social barriers to their decisions during diagnosis and treatment [ 27 ].

The over-prescribing of drugs is very common in animal products and animal-source foods (to be consumed by humans) in Iran. Moreover, each of these foods contains antimicrobial residues. Thus, it can be conjectured that people ingest significant amounts of antibiotics every day unintentionally without suffering from any infectious disease. Therefore, veterinarians must pay adequate attention to AMR in human health [ 28 ].

Overall, the world is faced with increasing availability and misuse of antibiotics in veterinary medicine, which threatens public health. There is a significant increase in AMR on a global scale, and there is a threat of increasing infections that do not respond well to treatment. It is essential to take appropriate measures and plan to prevent the over-prescription of antibiotics by veterinarians. There is an increasing need for education and empowerment policies, all deemed impossible unless the barriers facing veterinarians are recognized in appropriate prescribing. In other words, to deal with the AMR issue, the first step is to identify the causes and underlying contributing factors to this event in veterinary medicine and the disastrous conditions veterinarians face in Iran and the world. It is not possible to adequately approach what veterinarians go through and how they perceive the existing context only through quantitative research. A qualitative approach is needed to explore all aspects of this problem. Therefore, the present study employed this approach to explore the Iranian veterinarians’ perceptions of barriers to AMR prevention. We hope that the results generated from this study will help promote programs to curb slow down and the development of AMR in Iran and the world. The present findings can be used to make new social, economic, and even political decisions.

Materials and methods

Research design.

This research was conducted with a qualitative approach and qualitative content analysis method from three cities of Iran: Kerman (with a large population of large and small livestock), Bandar Abbas (a fishing and aquaculture hub), and Tehran (with a large population of pets and industrial poultry). The present qualitative study used semi-structured interviews with veterinarians who had experience in treating and prescribing antibiotics or office work in veterinary medicine, or those with sufficient experience and knowledge of issues in veterinary diagnosis and treatment. The interviews were held face-to-face from May 3, 2021 to August 13, 2021. In this study, theme analysis was used, which is a common type of qualitative content analysis. It seeks a deep understanding of the complexity, details, and embedded context of a given phenomenon. In this type of analysis, interviews with individuals provide a better understanding and richer information about participants’ experiences and perspectives. This research approach allows for an in-depth and rich exploration of participants’ experiences.

The panel of experts offering advice on research questions and reviewing the transcripts for reliability consisted of one epidemiologist, three health education and health promotion specialists, and two veterinary professors collaborating with three organizations: (Hormozgan University of Medical Sciences, Veterinary Department of Kerman University and Iranian Veterinary Organization). The present participants were selected through maximum variation and snowball sampling.

Participants

The research population consisted of veterinarians from three cities (Tehran, Kerman, and Bandar Abbas) in Iran, all dealing with a large population of livestock, poultry, and aquatic animals in 2021. The inclusion criteria were: veterinary work experience at least three years, the experience of therapeutic clinical work or working as a veterinary administrative and supervisory staff, willingness to participate (in the research), and ability to answer the questions. The exclusion criteria were unwillingness to participate and withdrawal from the interview. Purposive sampling was used with maximum variation (in terms of the province of work, age, sex, the field of work in a clinic or pharmacy, and affiliation with the public or private sector) at first steps and snowball sampling methods In the following. That means some veterinarians were concerned about expressing their opinions or reporting any illegal case they had dealt with. Therefore, they had to be selected through snowball sampling. After reaching the first participant and holding the interview, s/he was asked to suggest the next veterinarian who was aware of or was experienced in prescribing antibiotics against paraclinical rules. Therefore, each participant connected and introduced us to the next participant. Interviews were held in a public place at the interviewee’s convenience. In some cases, the interview was held in the clinic, and in others in the interviewee’s office. The data were collected and analyzed simultaneously. The interviews continued until the data were saturated (i.e. when no new information was obtained) and until all the extracted themes were sufficiently supported by the data. After the 17th interview, no new data were collected, but to be on the safe side, another interview was also conducted, and after the interview with the 18th participant, the sampling was stopped.

Data collection

Guided questions and semi-structured interviews were used for data collection. The interviews were held face-to-face and video calls. When required, a trained research assistant conducted a qualitative interview to increase the accuracy and speed of data collection. The interview questions were derived from a review of the existing literature on AMR with a focus on the underlying causes and also the comments made by a panel of experts. At the beginning of the interview, the purpose of the study was revealed to the participants and they were assured of the confidentiality of the information they provided and the anonymity of their responses. The interviewees were ensured they could withdraw from the study upon their will. Then, an informed letter of consent was signed. The required permission was gained to record all the conversations. The main focus of the questions included:

What are the barriers to veterinarians’ prevention of increased AMR? Explain.

What do you know about the causes and precursors of AMR occurrence in Iran?

What are the determinants of prescribing and using antibiotics in veterinary medicine in Iran in your opinion? Explain.

What are the determinants of the increased AMR in your opinion as a veterinarian?

Based on the participants’ previous answers, more exploratory questions were asked and, as a result, we extracted the main reasons why veterinarians over-prescribed drugs and why antibiotics were overconsumed in the animal source food industry. The sample size was determined by the theoretical data saturation criterion. In other words, during the data collection, when we concluded that more interviews and observations could not add any new information and only led to repeated findings, we stopped the data collection. Therefore, 18 active veterinarians in clinical, medical, educational and administrative fields were interviewed in Tehran, Kerman and Hormozgan (provinces). Individual interviews lasted between 42 and 57 min.

Data analysis

The process of data analysis was done using Granheim and Lundman method [ 29 , 30 ] and with the help of MAXQDA-2010 software by the first and second authors of the article. The first and second authors listened to recorded interviews and transcribed them into a written format in Word 2017 software immediately after every interview and on the same day with the help of other research colleagues. In the second step, the text of the interviews was read by the researchers very carefully to get a general view of their text. In the third stage, all the texts of the interviews were read line by line and very carefully, and the initial codes were started.

In the fourth step, the researchers placed the codes that were similar in terms of meaning and concept and were placed in a category in a subcategory and determined the relationship between them. In the fifth step, the codes and categories were placed in the main categories, which were conceptually more comprehensive and abstract [ 31 ]. Finally, in a joint meeting, the entire process of data analysis was shared and conflicting opinions on the content of a topic were discussed by a research team with two qualitative health researchers and two veterinarians.

Guba and Lincoln evaluation criteria [ 32 ] were used to check the trustworthiness of the findings. To substantiate the validity of the findings, the researcher’s self-review technique was used in data collection and analysis as well as a peer check during which the codes were provided to two participants to resolve misunderstandings. To substantiate the reliability of findings, intra- and inter-rater reliability tests were used. To this aim, the recorded and transcribed conversations were given to several experts for review. After analyzing the data, they were re-analyzed by colleagues. The next step was documentation to test the accuracy and comprehensibility of the procedures, and the underlying mechanisms of errors.

Ethical considerations

This research was approved by the Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.207). In the interviews, the researcher, by introducing herself and also explaining the purposes of the study, tried to create an amicable atmosphere for the interview. The participants were also ensured of the confidentiality of the information they provided, the anonymity of recorded conversations, and also why they were selected. They consented to their voice being recorded. The participants were free to withdraw or leave the interview any time they requested.

The present study was conducted as interviews with 18 veterinarian participants in Tehran, Kerman, and Hormozgan provinces. Both sexes were included. There were 11 male and 7 female participants whose ages ranged between 27 and 58, with an average age of 42.5 years. The participants’ work experience ranged between 3 and 27 years, with an average of 15 years. The demographic information is summarized in Table  1 .

The data analysis led to the extraction of 4 main categories and 44 subcategories (see Table  2 ), each examined separately.

Educational factors

The first determinant of the increased AMR deals with academic issues in university. Among the most important issues are those concerning students, clients’ lacking awareness and knowledge of AMR in animals, and its transmission to humans (from animal source foods).

Unsystematic internship

During the summer holidays of the final 2–3 academic years of veterinary students, they are required to take the internship. Yet, some participants complained about the unsystematic and inefficiency of this internship.

“During our student days, we took up the internship, but we did not learn anything special at all”. (Participant #13)

Unadjusted curricula

The majority of participants agreed that during their studies, only in the bacteriology course, they learn about AMR (only superficially) and that in the university curriculum, this subject was not adequately included.

“All faculty members should teach something about AMR, not just the bacteriology professor. Also, do we not prescribe antibiotics once we diagnose viral diseases in clinical sciences and the like? If so, then why are we not taught what AMR actually is”? (Participant # 9)

Outdated education

As the participants described, it was essential to teach new things about AMR and to develop strict, principled, written instructions on this subject. The participants recommended following effective and efficient exemplar instructions (in foreign countries) to strengthen the educational system not also at university but in food and drug administrative organizations. It is essential to update basic and clinical sciences curricula and add AMR to all courses, as most participants agreed.

“We should keep up with the global community in this regard so that we can be fully aware of the new knowledge and instructions, and can create new instructions based on the preexisting ones”. (Participant #2) ”I think one thing that can definitely help is to see how successful projects in developed countries proceed. Let us follow their example”. (Participant #13) ”They still teach the way they did a hundred years ago. The subject matter should be changed. It seems as if discussing AMR does not matter at all”. (Participant #9)

Lack of specialized training courses

A key determinant of AMR prevention was the need for useful and effective training courses for all those somehow affected or affected by the AMR, including vets, the health staff, medics, therapists, as well as the livestock and poultry breeders, and the like.

“It is essential to hold relevant and useful training courses for ranchers, poultry farmers, pharmacists, as well as veterinarians, veterinary staff who perform inspection and monitoring work for others. So, everyone is expected to cooperate”. (Participant #2). “Farmers should know that adhering to the (medical) interval helps decrease antibiotic concentration in the animal being treated. Thus, the farmer or breeder needs to postpone the slaughter time. Or he is advised not to consume animal source foods while they are being medically treated”. (Participant #7).

Low specialized study index

As in many other sciences, gaining up-to-date knowledge requires studying the most recent research findings.

“We should not only encourage those who influence drug resistance to study about this subject, but universities should also encourage professors to study more about the specialized topic. If a professor fails to have updated knowledge, s/he cannot teach students well. Thus, how can we expect the students to act efficiently in near future”? (Participant #5)

Lack of empowering educational system

Some participants expressed concerns that the educational system did not adequately prepare students for accurate diagnosis and prescription in near future.

“At university, nothing matters more than studying and getting good marks. The educational system does not actually prepare students for the work market. In other words, it does not simulate real conditions before students leave academic life and enter the work market”. (Participant #9)

Lacking cooperation of all medical sectors affiliated with the university

In the present study, participants, all veterinary graduates or instructors, raised the question why discussing AMR was limited to the bacteriology course and not included in clinical and practical courses.

“Why is medical resistance only limited to bacteriology? All other basic and clinical sciences sectors at university are talking about diagnosis and treatment, and are prescribing drugs. But, when they come to medical resistance, they only refer to bacteriologists and the bacteriology labs”. (Participant #9)

Administrative and legal factors

There are issues about the rules/regulations and policies on veterinarians’ practice and that of all people somehow concerned with animal source foods, which can add to the existing problems. Here are the categories and the relevant excerpts:

Problems with rules and regulations

As the participants pinpointed, there is a strong need for food safety rules and regulations especially in terms of AMR. Adherence to these rules and regulations should be closely monitored too.

“Though there are rules, you can never be sure they are abided by fully. No one is afraid of not following the rules. Even I myself, who is doing clinical work, am not sure whether there is any prohibitory rule for this or not!” (Participant #8) . “Breeders who administer drugs themselves or those who slaughter animals being medically treated should be fined or prosecuted because they threaten public health. But, in reality there is no way to stop them”. (Participant #5)

Poor monitoring and administration

A number of participants acknowledged that even if there are rules and regulations, they are not fully observed. There has not been any efficient monitoring over how rules and regulations are followed. That is why rules have been ineffective.

“ All these are just instructions. In practice, there is no veterinary body monitoring how things are done. The rules are ineffective“(Participant #7) .

Selling over-the-counter (OTC) drugs or those without laboratory-based approval

Selling all kinds of drugs, including antibiotics without prescription, without laboratory approval and freely in Iran has caused serious trouble.

“ You can get any medicine you want from any pharmacy at any time. Actually, the main customers of pharmacies are those who buy drugs arbitrarily”. (Participant# 11) ” In my opinion, pharmacies should not sell every kind of drug especially antibiotics unless they receive a laboratory approval for the antibiogram test. Likewise, a vet should not prescribe antibiotics unless s/he receives the lab test result first”. (Participant #2) ” In my opinion, the sale of medicine, especially antibiotics, should be subject to laboratory approval. That is, a person should not be allowed to buy medicine until the laboratory has determined the type of disease or at least the effective antibiotic, even if the vet has prescribed it”. (Participant #9)

Inadequate advocacy

As some participants commented, gaining the full support of international, national and regional communities was a great issue.

“ We need the help of international and national organizations to solve this global issue. When a problem is global, the solution will definitely be achieved with the cooperation of international organizations”. (Participant #2)

Lack of interdisciplinary cooperative approach

According to some participants, to achieve an optimal solution to this problem, all administrative, supervisory, diagnostic, and medical sectors should cooperate.

“ Solving this problem is not what only one organization can do. Universities should teach students in the right way; veterinary administrative organizations should do their job efficiently; the private sector (e.g., clinics and pharmacies) should obey the rules. Most importantly, there should be strict rules made and abided by with all sectors cooperating”. (Participant #4) ” Our clients should be aware of the importance of AMR and also aware of how the drugs are cycled among the environment, animals, and humans. The Environment and Veterinary Organization, public health and agriculture, and the like should all take serious actions. If one ring is missing from this chain, the whole chain is broken. All efforts will end up fruitless”. (Participant #6).

Problems with the production and use of electronic health records (EHRs)

Developing systems such as the integrated prescription system and the use of EHRs can significantly help to prevent AMR occurrence.

“ If the EHR system was used, things would be better now. No pharmacy could then sell OTC drugs. Thus, no customer could buy antibiotics arbitrarily”. (Participant #14)

Slaughtering medically treated livestock

According to some participants, a stock not responding to an antibiotic treatment does not need any lab test. Neither does it need any abstinence interval. It can easily gain slaughter permission even in emergency cases.

“Here, an animal that is taking medicine and is not becoming well or is getting worse is sent for emergency slaughter. Is there any organization in charge here? Only if a buyer comes to know that an animal shows symptoms of a disease, he may buy it at a lower price”. “Before the slaughter, the antibiotic residues are controlled in poultry but not in macro-livestock (e.g., cattle, sheep and goat)”. (Participant #12)

Non-compulsory training courses before issuing a license for animal husbandry

Some participants insisted that the government should make it compulsory for applicants (for stockbreeding or husbandry) to complete AMR training courses before issuing a license for stock breeding. Here are some comments.

“ Certainly, people seeking for an establishment and operation license for livestock, poultry, fish ponds, and in short, any kind of livestock, must be obliged by the relevant governmental body to first pass a series of training courses and then get a license”. (Participant# 2)

Lacking coordination between medical and veterinary organizations

Due to the lack of the required infrastructure in veterinary medicine, this organization needs to cooperate with the Ministry of Health (for service provision), and medical and laboratory sectors especially to perform laboratory tests.

“ We can say that great concern is that veterinary medicine and medical sciences are affiliated with two different ministries. The former is deprived of the facilities provided by the ministry of health. Even for simple antibiogram tests, we should visit veterinary labs provincial centers, or big cities”. (Participant #11)

Lacking attention to micro-industries and micro-breeders

With the expansion and development of livestock, poultry, and aquaculture industries, the main attention has been focused on this group (of industries), and domestic and micro-breeders have been neglected.

“ If there are any rules and regulations, they are mostly about industries such as macro-level poultry breeding or husbandries. Yet, in practice, the national livestock is to a great extent bred by domestic and micro-level breeders that are largely neglected”. (Participant #3)

Limited facilities in small towns

The lack of diagnostic facilities such as laboratories equipped with antibiogram testing for cases sent from veterinary clinics have caused serious problems for clients and veterinarians.

“ For a simple antibiogram test, we have to refer to the provincial center, and this is both time-consuming and costly. More importantly, most of our livestock population is in small towns, not in provincial centers”! (Participant #1)

Client-related factors

Another determinant of the increased AMR as perceived by Iranian veterinarians is the factors related to livestock/poultry breeding and animal owners (termed here as “clients”). The clients’ choices, decisions, and behaviors will have significant effects on increasing AMR.

Quick response : Among the reasons for an emergent antibiotic prescription without any diagnostic test are: concerns about high mortality rate if the drug is not used immediately, the breeder’s referral at the onset or peak of a disease spread, or substantial losses in the herd, or the referral rush to improve conditions.

“ Mostly, livestock farmers especially poultry farmers or any other breeder with a significant number of livestock, poultry, or aquatic animals, insist on getting a strong antibiotic immediately so that the mortality rate does not rise any further. They cannot even wait for the antibiogram test result. If we do not prescribe antibiotics for them, they go get it from somewhere else, and even if they go for the antibiogram test, they may not be patient enough to receive the test result and, thus, arbitrarily begin other antibiotics”. (Participant #6)

Customer satisfaction

Some clients have used several specific drugs for years and found them effective. Thus, they have no faith in the lab diagnostic test result. Besides, some clinicians and especially vets are sometimes subject to too many demands, which can be tempting. They might occasionally be tempted to violate the existing rules and, upon a client’s persistence, they may neglect the protocols and easily give in.

“ For our clients, the drug manufacturing company even matters. Sometimes, they carry the former drug vial to show us and insist that the same drug be prescribed”. (Participant #3) ” Even when the required facilities were available, I faced too many suggestions. Some guys came to tell us to take it easy and let them get away with it (by granting or renewing their permit)”. (Participant #16)

Low purchasing power

As there is no drug and treatment insurance for animals in veterinary medicine in Iran, the cost of treatment or the price of drugs was found as another determinant of antibiotic prescription, as mentioned by the present veterinarian participants.

“ Sometimes prescriptions are written out according to the customer’s affordance. Sometimes, customers ask us to prescribe something they can afford to buy. As there is no insurance coverage for veterinary medicine, the price matters, and it significantly affects the act of prescription”. (Participant #7)

Social learning

An effective factor in antibiotic self-medication or arbitrary use of antibiotics is to learn about it. There are often others living in the same place (city or village) where the clients live, who used a certain drug and found it effective. Now the clients tend to follow their steps. Besides, self-medication cuts down on the diagnostic and therapeutic costs too.

“A farmer might come to us and insist on buying the same drug that his neighbor has already bought. He does not consider that the diseases might be different. Overall, clients are more influenced by neighbors than us”!

Pharmaceutical determinants

In some cases, what causes the clients to insist on our prescribing OTC drugs is the price and effectiveness of the drug (as perceived by the clients) and even the drug manufacturing company.

“ Some clients insist on buying a certain antibiotic because either they have already used and found it effective. Thus, they may ignore what the vet’s diagnosis is”. (Participant #11)

Unawareness of antibiotic residues and abstinence interval

While using antibiotics, the livestock, poultry, and aquaculture breeders should be aware of the animal source food abstinence interval. But in reality, they are mostly unaware of that.

“Many clients are not adequately aware of the abstinence interval after taking antibiotics, and this issue makes them send the animal products into the food cycle during the treatment period”. (Participant # 11)

Drug replacement or early cessation

When seemingly the symptoms of the disease are gone, some breeders ignore the medical instructions and cease the drug sooner than they should.

“For example, a drug should be taken for not shorter than a week. But when a client takes the drug for two days and feels the disease is gone, he stops administering the drug. He does not care about the medical resistance and how it occurs. He ignores them all”. (Participant # 5) ”A client may purchase an antibiotic (either prescribed or self-medicated) and begin the treatment. After one or two days, when there is no sign of recovery, he replaces the drug easily”. (Participant #8)

Antibiotics use as a growth stimulant

Antibiotics have long been used as growth stimulants on a large scale by breeders of raw animal products in Iran.

“ In large breeding industries such as livestock and poultry farming, antibiotics are used as a growth stimulant, and this is very common”. (Participant #4)

Self-medication or arbitrary use of drugs

As the participants mentioned, many clients take some therapeutic measures before visiting a veterinary diagnostic and medical center. They have already begun taking several antibiotics or have quit the treatment half in the way.

“ Sometimes a farmer arbitrarily buys and consumes several drugs before going to any veterinary, diagnostic or medical center”. (Participant #5) ” Some ranchers already take many antibiotics. When we ask them why they say they had it refrigerated since the last time they ever purchased and consumed the drug. They intended to use the remains of the drug and visit a clinic only if their self-medication did not prove effective”. (Participant #8)

The unconventionality of the antibiogram test

As perceived by the present participants, antibiogram testing is a new thing that has not been yet received well by many clients. Not many participants welcome or even prioritize this test. They think doing this test is not compulsory and, thus, they do not feel obliged to take it at all.

“Unless the customer is obliged to, he does not go for the test to a laboratory at all. Thus, it needs to be mandatory; yet in reality, it is not”! (Participant #15) ” When we tell a client that he should take a sample for an antibiogram test and wait until then, he is surprised. It seems as if he has never heard of such a thing. He wonders why none of his fellow breeders were already sent for such a test when they faced the same problem”. (Participant #18) ”Nobody cares about AMR in the future. They laugh at us and wonder what the consequences are”. (Participant #2)

Clients’ preference for over-prescribing vets

As perceived by the present participants, any veterinarian or clinician who prescribes more drugs to treat animals is more popular.

“ If you do not prescribe any drug, the client prefers to go to another vet. He will not wait at all for you to tell him about the importance of drug resistance. Now every doctor who prescribes more drugs becomes more popular and he is perceived as a better doctor”. (Participant #18)

Clients’ lacking foresight

Some participants acknowledged that the AMR problem is unthinkable in the future and far-fetched to many clients.

“ People do not really know what will happen in the future and people will suffer from drug resistance. No one can even imagine what will happen in the future. It is not tangible to them”. (Participant #2)

Rejection of paraclinical costs

Some clients, as the participants’ accounts, revealed, do not bear costs higher than those of the visit, including the cost of a laboratory.

“ Our clients are mostly reluctant to pay much, especially when the cost of the treatment is higher than that of, for example, a domestic chicken that they bring here for treatment”. (Participant #18)

Materialistic view

Many ranchers ignore many important things and are just concerned with more production and productivity, and gaining as much money as they can. So, they do things that are sometimes unethical and illegal but just cost-effective.

“I don’t think it matters how you make money. You just need to be smart and know when to do what. For example, I know a guy who drugs his chickens the day before slaughter but keeps some of them apart for his own family’s use. He sends one of the undrugged chickens to the laboratory so that he gets a negative lab test result. This way, if there is any loss, it will happen to the undrugged chickens and not the whole poultry”. (Participant #9) “Sometimes they breed a few chickens apart from others only to send them later on to the laboratory. The lab also cooperates with them and hides things in the actual report”. (Participant #13)

Veterinarian-related factors

In addition to the above-mentioned factors, veterinarians also sometimes cause an increase in AMR. Here we see how their characteristics affect their decisions on AMR development.

Inadequate job security

The current job market for veterinarians in Iran is not very prosperous and any factor that endangers the current position of activists in this field will fail.

“If you cannot keep the customer satisfied with yourself in the job market right now and put extra costs on the customer, he will quickly go to another clinic and another vet”. (Participant #14)

Lacking experience in the correct act of prescription

As perceived by the participants, many veterinarians who have just entered the work market lack any experience in prescribing drugs. Thus, they significantly account for the increased AMR.

“ As novice veterinarians do not have much experience in prescribing medicine, they prescribe several antibiotics together, with the hope that one of them works”. (Participant #13)

Prescription based on prior experience

Prescribing drugs based solely on diagnostic experience is common practice in more experienced veterinarians.

“ As soon as most colleagues see cases similar to what they have already faced and treated, they begin to write out the same prescription. It is generally well-established that certain drugs are always prescribed for respiratory infections, some for gastrointestinal infections, and so on”. (Participant #14)

The unconventionality of diagnostic tests among veterinarians

Many veterinarians have diagnosed diseases and prescribed them mainly based on their own experience. Antibiogram testing is a new therapeutic measure that has not been welcomed warmly by vets.

“There are very few vets who wait for the antibiogram test before writing out any prescription. Actually, antibiogram tests are still very uncommon”. (Participant #9)

Being labeled as inexperienced if dependent on laboratory diagnosis

As our participants described, a veterinarian who does not make a diagnosis or give treatment immediately and independently (from lab tests) and hinders it until the paraclinical test results are labeled as inexperienced.

“We have no problem sending the client to the lab, but unfortunately it seems as if we were unable to make a diagnosis ourselves and we were inexperienced and because of that we got help from the lab”. (Participant #12)

Fear of losing clients

Some veterinarians acknowledged if they delayed the diagnosis to a later time (to receive the lab test result), they could easily lose many customers.

“If you keep the client waiting or send him to a lab to fetch the test results, he will for sure prefer to visit another vet”. (Participant #5)

Diminishing ethical values

Another determining factor raised by the participants was the need to have a working conscience and commitment to livestock/poultry breeders, laboratories, and those having contracts with labs. In other words, the vets should rely on the lab test results.

“When I used to work on a poultry farm, I saw a separate hall for raising chickens with no antibiotics. The sample sent to the lab was taken from this hall. Or the chickens were slaughtered for the farmer’s own family. The other (drugged) chickens were sent to the slaughterhouse for public use”. (Participant #10) “Some colleagues are not committed enough to their job and do not feel it on their conscience. Similarly, the test results coming from some labs are not reliable either. So, the negative antibiotic results we receive from them might be false”. (Participant #5)

Lacking foresight

AMR is not familiar to many people in society. They do not adequately know what AMR is, which can affect their practice.

“Veterinarians cannot even imagine how dangerous AMR can be to human health in the future. When they have no idea what AMR is and can be, how can we expect them to be worried about it”? (Participant #8)

The insignificance of AMR

The AMR issue is not very important for some veterinarians in diagnosis, treatment, and monitoring.

“Rarely does veterinarian care about drug resistance. I do not think it is even their last priority to consider”! (Participant #10)

Lack of self-efficacy in overcoming barriers

A few interviewees admitted that they or some colleagues have a specific drug classification for most diseases according to which they act spontaneously. It means that they do not take different therapeutic measures when faced with different cases.

“Some clinicians do not consider that everyone can have his disease. I mean, they treat all patients the same way and prescribe strong broad-spectrum antibiotics for 90% of cases”. (Participant #5)

Competitive drug market

Many veterinarians are not required to sell drugs on a prescription, and selling without a prescription is a legal and common task. So active veterinarians in the field compete with each other for selling drugs and evidently for more income.

“Everyone likes to open up a pharmacy because he can easily earn money with no trouble with diagnostic and surgical measures. It is much better if you can persuade customers to buy more”. (Participant #8)

Apparent issues with prescriptions

The last subcategory of veterinarian-related factors was the appearance of prescriptions. The present prescriptions encourage vets to prescribe more drugs.

“The size and shape of prescriptions are such that the vet is encouraged to prescribe more drugs. The prescriptions should be refined in shape to allow for one or two drugs only and no more”. (Participant #5)

The present study aimed to explore the barriers faced by Irainian veterinarians against preventing Antimicrobial resistance. A few qualitative studies have been conducted on AMR, which dealt with the causes of progress and the obstacles faced by those involved in this problem, especially in the veterinary profession [ 33 , 34 ]. The results showed that different educational, legal/administrative and veterinarian-related factors account for the increased AMR in Iranian society. The first category included factors related to the educational system, such as the lack of any specialized training course for veterinary students, those in charge of monitoring veterinary practice, veterinary departments, and ranchers struggling with educational problems who may all be implicated in increasing AMR. In Iran, various educational initiatives have been implemented, such as the publication of a book on rational prescription principles, academic papers, and reports from the National Committee on Prescribing and Rational Drug Use. Despite these efforts, there are numerous educational obstacles in veterinary colleges in Iran when it comes to instructing students on prescription fundamentals and the rational utilization of medications.

The required material has been also developed; training and retraining programs have been planned based on eclectic drug use criteria; workshops, conferences, and seminars have been held too. A prescription can simply be representative of a whole nation’s sociocultural values and medical conditions. Many studies have been conducted worldwide to improve rational drug prescription and consumption [ 35 ]. The effects of educational interventions on the improved prescription pattern have been reported in Iranians and other studies too [ 36 , 37 ]. Continued training on rational drug prescription and pharmacy education has been recommended to doctors in the existing literature [ 38 , 39 ].

In Zareh’s study, the most commonly prescribed drugs were injections and antibiotics. The research findings showed that, after the training, there was an increase in the rational prescriptions for most prescribed drugs [ 40 ]. As for teaching strategies, the WHO has published The Guide to Good Prescribing for medical students. This guidebook contains six rational steps that can significantly reduce the irrational prescription of drugs: 1- defining the patient’s problem 2- defining the goals of treatment 3- ensuring that the treatment is appropriate for the patient. 4 – initiating the therapeutic measure 5 - providing information, instructions and warnings (if any) 6 - monitoring and ceasing the treatment [ 41 ]. Outdated education was a sub-category found in this study. Different studies showed that dentists often, due to a lack of knowledge about the side effects of improper prescribing of antibiotics, tend to over-prescribe them [ 42 , 43 ].

Veterinarians also are central to antimicrobial stewardship on farms, with their prescribing decisions significantly impacting AMR.A study on Canadian dairy cattle veterinarians’ revealed factors influencing their antimicrobial prescribing, attitudes towards reducing antimicrobial use, awareness of AMR, and perceived barriers to improving stewardship [ 44 ]. In addition educational resources have been developed to enhance veterinarians’ understanding of AMR and promote rational antimicrobial use. Online courses such as “Antimicrobial stewardship in veterinary practice” and “Farmed Animal Antimicrobial Stewardship Initiative” aim to educate veterinarians on responsible antimicrobial use [ 45 , 46 ].

What we need is a high-quality time management element added to the existing curricula so that students can be well-equipped with whatever they need to act professionally. Excessive imitation of medical sciences in specialized courses can only lower the efficiency of a vet’s profession. Rather, there is a need for incorporating courses on different animal species both at the general practitioner’s level and the specialized doctorate degrees [ 47 ].

There is also the issue of time management in the curriculum. Decreasing the quantity of content and increasing the quality (by adding more useful content) can better reform the veterinary curriculum. Goal-setting in veterinary sciences has already been revolutionized, and veterinary universities cannot ignore it. Thus, it is essential to consider the present and future needs in defining the required specialties to handle the existing national health issues, each of which can impose a loss of millions of dollars nationally. For many years, curricula have been developed in the European Union to achieve the necessary specializations by the existing needs, at least in the cattle breeding industry [ 48 ]. A deficient educational system is one factor that increases the overuse of antibiotics. Therefore, it is necessary to take basic measures based on the macro-planning of students’ knowledge and increase the quality of internships. In a study by Wushouer et al. in China, it was observed that an increasing awareness was followed by a decreasing rate of antibiotic administration [ 49 ]. Therefore, it is necessary to increase knowledge through a different approach in the educational system. Most experts believe that education in medical sciences should follow a different approach than other fields of study because knowledge construction in these fields of study (i.e., medicine, veterinary medicine) affects the content that students receive and the experiences they gain [ 50 ].

Failure to hold training courses for producers of raw animal products and unsystematic student internships can significantly lower the quality of education. Raising the study index in AMR and modeling on successful examples can be considered in curriculum design. Moreover, all departments of the veterinary faculties should cooperate and the heavy burden of teaching AMR should be removed from the bacteriology department only, and be shared by all basic sciences and clinical courses. Only then can we hope to see improved practice in students’ learning experiences and professional life in the near future.

The present findings showed that currently in our country, the educational system needs to be seriously reformed by appropriate training programs and pre-employment awareness-raising programs for veterinarians and ranchers [ 51 ]. People working in this field should be more empowered, better aware, and skilled enough at a correct diagnosis or proper functioning [ 52 ]. Only then we can hope that their self-efficacy is increased and they can learn to act more responsibly. These can help to prevent the occurrence of AMR and to begin to resolve it rather than worsening the issue.

The second category of the determinants of increased AMR was administrative and legal factors. Problems with the law, monitoring, and selling OTC drugs are important issues that can increase the costs of treatment too. This finding is consistent with several studies. For example, it is estimated that about 100,000 people in the United States die every year from the adverse effects of drugs [ 53 ]. In the United Kingdom, problems in 11% of prescriptions cost over € 400 million in loss, and about 16% of these problems harm patients [ 54 ]. Most of these errors are preventable, including drugs prescribed heedless of contraindications, those taken incorrectly, or those not having been properly monitored. The WHO, along with other relevant international organizations, proposed certain criteria to evaluate the quality of prescriptions to prevent the occurrence of problems and lower treatment costs [ 55 , 56 ]. A useful way of evaluating the prescription pattern in a country is to evaluate the doctors’ prescriptions. A simple prescription can represent the current state of medical education in a country, how laws and regulations affect the medical community, socio-cultural beliefs, and the medical condition [ 36 ]. Based on WHO guidelines use of medically important antimicrobials in food-producing animals, any level of restriction in antibiotic prescription should be considered, including a complete cessation of the use of one or more antibiotics. Examples of restrictions that WHO considered are: any prohibition on the use of antibiotics, such as but not limited to the prohibited use for specific indications (e.g., for prophylaxis of disease or growth promotion), the requirement of a prescription by a veterinarian for the use of antibiotics, voluntary restrictions on farms or organic interventions [ 55 ]. Drugs that need confirmation from a specially qualified person or organization should not be sold over the counter. Prescribed drugs are regulated by the US Food and Drug Administration (FDA). Having a federal license with a medical leaflet is a prerequisite for the packing of any drug. A medical leaflet usually consists of four parts: indications, contraindications, warnings, and dosage [ 57 ]. He who writes out a prescription decides who can consume the drug. A pharmacist can buy drugs, but he should sell them only to those authorized by a legally qualified person. Thus, a prescribed drug has 3 parts [ 58 ]: (1) The doctor’s prescription, (2) The pharmacist’s written prescription while delivering the drug, and (3) the drug package with a label on it. That is why officials are expected to always think about formulating new and public policies to implement correct and new strategies for the use of antibiotics [ 59 ]. Educational and political interventions, establishing and implementing laws regarding AMR stewardship may be effective and acceptable either before or during the livestock and poultry breeding programs, even for pet owners [ 60 ]. Success in the coordinated implementation of related laws is not possible without the advocacy of various stakeholders, including policymakers, veterinarians and ranchers, pet owners, public sector employees, farmers, and consumers [ 51 ]. It seems that the use of effective legislation, contractual requirements, professional obligations and the distribution of suitable facilities in more distant areas makes the implementation of this plan possible [ 21 , 22 ].

The third category was the client-related factors. Quick response and arbitrary drug use were among the sub-categories. With the expansion of public access to the internet system, people may want to refer less to vets and, instead, self-medicate or they may expect a quick response and begin to use antibiotics. In their research, Hofmeister et al. investigated veterinary visitors and found the internet connection speed as the third most important source of retrieving pet health information after GPs and specialized vets and before family and friends and other mass media [ 61 ]. Kogan et al. maintained that internet-based sources are considered an extra source of information about pet health for pet owners besides visiting vets for consultation [ 62 ]. Volk et al. reckoned that the internet and online health information could replace veterinarians and lead to fewer pet owners visiting veterinary clinics [ 63 ]. Thus, since some clients do not want to pay the visit and para clinic fees, by searching on the Internet and cyberspace, or based on their previous experience or else, they prescribe and take antibiotics arbitrarily before any visit to vets. If they do not find some proper treatment, they try other antibiotics, which leads to the problem of changing or stopping the antibiotics early before the end of the treatment period.

Some other poultry or aquatic breeders who have farms of several thousand pieces are very worried about the loss of their livestock, poultry and the aquatic population at the beginning of the disease. Since there may be a large population of their herd while waiting for the antibiogram test, they prefer to use a broad-spectrum and preferably cheaper antibiotic (for large-scale use for a large herd) to begin with and prevent their economic loss to a large extent [ 60 ]. Therefore, both in the producers and breeders of animal-origin food and in the owners of pets, the customer’s demand needs quick response and the customer’s demand should be prioritized [ 22 ].

In addition, a person who once used a broad-spectrum antibiotic without a prescription and got a response, suggests that to his/her colleagues or other breeders, and by promoting social learning, this behavior promotes the progress of antimicrobial resistance. In addition, many of these people are unaware of antibiotic residues and abstinence intervals, and currently do not feel threatened about the future of antimicrobial resistance. When they go to the vet, they prefer to go to a vet who prescribes some antibiotics to return home without any drug prescribed [ 33 ].

Another subcategory extracted from the present findings was the use of antibiotics as growth stimulants by poultry farmers. The use of antibiotics, both as a treatment in humans and as a therapeutic measure or growth stimulant in animals, has a great effect on the microbial flora of the intestine and also induces resistant strains in these animals [ 64 ]. When used as a growth stimulant, antibiotics can have adverse effects on humans and animals [ 65 ].

The fourth category was the veterinarian-related factors. The lack of an inter-sectoral approach was one subcategory extracted from the findings. Foreign studies mentioned a multi-sectoral approach and knowledge sharing in educational environments [ 66 ]. There seems to be a need for all institutions to have the required knowledge about the use of antibiotics through shared efforts between universities, the government, and the various professions. One subcategory was the insignificance of antimicrobial resistance to veterinarians. Antibiotic-containing products have harmful effects and there is a significant increase in the resistance of different types of infectious bacteria [ 67 ] besides the important role that antibiotic-containing animal products play in this process. Thus, global efforts are needed to reduce antibiotic use and attempt to control it. More control is needed over veterinary drugs and their use in livestock and poultry farms [ 68 ].

In line with the qualitative study in the UK, this study showed various behavioral and contextual factors involved in the participants’ beliefs about AMR stewardship and their responsibilities in the right direction [ 33 ]. One of these issues is the lack of experience in writing correct prescriptions among novice vets who prescribe several antibiotics at the same time in the hope that at least one works. This finding is in line with some studies that acknowledged that, when uncertain, most new clinicians tend to over-treat with antimicrobial drugs instead of refraining from treatment [ 1 , 69 , 70 , 71 ]. They prescribe several antibiotics in the hope that one works. The other extreme case is also possible when experienced veterinarians prescribe drugs based on their long-held experience. These clinicians have more faith in a series of antibiotics. On the other hand, the diagnosis of the disease and the prescription are dependent n each other. When they are told about the laboratory evidence, they react as if their credibility has been damaged. Therefore, they provide waves of unprincipled recommendations and increase antimicrobial resistance. If a veterinarian intends to prescribe antibiotics based on the principles and guidelines, s/he will face other problems, including the fear of losing clients because, as mentioned earlier, if the prescription is not in accordance with the client’s wishes or the urgency of responding to it, the client will prefer to go to another vet, and this issue will endanger the job security even more.

Another sub-category is lacking self-efficacy in dealing with different visits.In other words, the approach of veterinarians to prescribing antibiotics is to a great extent pre-established and classified. For example, oxytetracycline is the preferred antibiotic for most respiratory diseases. Any cause of disease that requires more attention to the self-efficacy of veterinarians and clinicians can be improved by training methods and participation in appropriate courses. Diminishing moral values ​​becomes important in cases where full-time monitoring of antibiotic residues in animal products and their transfer to society and the environment is not possible, and where the government and regulatory agencies fail due to poor enforcement of laws. The regulatory forces cannot monitor and take care of the veterinary private sector employees and breeders. We can only hope that the vets will feel committed enough in their acts of diagnosis and prescription and the resultant effect on antimicrobial resistance. In the end, it is possible to recommend the modification of the appearance of prescriptions as a solution, because most of the headers of the veterinarians’ prescriptions in Iran are designed in a large way, which encourages the person to fill most of the prescription with writing the unnecessary drugs, so maybe it is recommended to design and implement a single protocol in limiting the written space of the prescriptions, we can take a step in reducing the obstacles facing the control of antimicrobial resistance.

Limitations, strengths and future directions

There were certain limitations in this study. As the interviews were face-to-face, participants might have been tempted to provide socially acceptable answers. Also, some veterinarians showed concerns about the illegal cases they were aware of and reported. So, they were selected through snowball sampling. In addition, selecting interviewees with work experience and an adequate understanding of the relevant problems and interviewing them in a private place were somehow difficult.

As in other qualitative studies, researchers’ beliefs may have influenced the study procedure from conceptualization to interaction with participants and data interpretation [ 72 ].There were chances that the interviewees’ comments did not cover all factors possibly because of the limited sample size. Sampling in qualitative studies continues until the saturation happens. Thus, in this study also the interviews continued until the data were saturated (i.e., when no new information was obtained) and until all the extracted themes were sufficiently supported by the data. No formula was included.

It is possible that besides the factors mentioned by the present participants, other experiences are gained in other parts of the country that cannot be subsumed under the present categories.

Despite the potential limitations, the present study has several strengths. The first is the sampling method with maximum variation (in terms of the province of work, age, sex, and ​​work in the clinic or drug supply or employed in public and private sectors). The next strength is that during the interviews, some participants were dissatisfied with the current conditions, and this study provided an opportunity for them to find solutions. Moreover, there has been extensive research on AMR, but the vast majority of them are quantitative. Few have explored AMR determinants in society. The present study goes beyond the laboratory work, and with the One Health approach, using numerous interviews, it gains a deep understanding of work experience, and comprehensive and valid data to solve the AMR issue. The authors of this study intend to use the data from this study or at least part of the data for future educational interventions. A focus on the categories extracted from these studies helps to plan effective multidimensional interventions. This study can also guide future lines of research.

The results showed that AMR in veterinary medicine induced by veterinarians active in the clinical field occurs under the influence of different factors. To increase AMR stewardship, in the first step, the barriers facing all people involved should be deeply studied and identified. Appropriate plans and policies should be made to deal with the underlying factors. Educational, administrative and legal, client-related factors, and veterinarian-related factors should be considered as the determinants of the increased AMR. It is essential to reform the education system and strengthhen the interdisciplinary relationships, especially among universities and between the university and regulatory organizations. Removing the barriers these people face and reducing the consequent trouble can make the widespread emergence of AMR more evident. Its adverse effects on society will become a crisis which increases the causes of mortality due to the resistance produced to the antibiotics prescribed to patients.

Data availability

The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding authors.

Abbreviations

Antimicrobial Resistance

over-the-counter

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Acknowledgements

The authors would like to acknowledge all participants for their participation who patiently participated in this study.

This study received funding from Hormozgan University of Medical Sciences. The funder was not involved in the research design, collection, interpretation of data, analysis, the writing of the article or the decision to submit it for publication.

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Social Determinants in Health Promotion Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran

Razie Toghroli, Laleh Hassani & Teamur Aghamolaei

Department of Social and Behavioral Health, School of Public Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, 89119, USA

Manoj Sharma

HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

Hamid Sharifi

Department of Pathobiology, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran

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Contributions

RT, LH, TA and M-SH designed the study. MJ and RT conducted the laboratory analyses. RT, MJ collected the specimens. H-SH conducted the data analysis. RT, LH and M-SH wrote the main manuscript text. All authors reviewed and approved the manuscript.

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Correspondence to Laleh Hassani .

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The study was approved by the Research Ethics Committee of Hormozgan University of Medical Sciences (IR.HUMS.REC.1400.207). A written informed consent was obtained from all the study participants. All methods were performed in accordance with the relevant guidelines and regulations by including a statement in the declarations.

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Note: Here, what we mean by “Livestock” is all animals, including cattle, sheep, goats, camels, poultry, and aquatic animals bred and consumed by humans and consumed as animal-origin food.

By “stockbreeder”, “breeder” and “client”, we mean all those who own livestock and pet. AMR represents antimicrobial resistance.

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Toghroli, R., Hassani, L., Aghamolaei, T. et al. Explaining the barriers faced by veterinarians against preventing antimicrobial resistance: an innovative interdisciplinary qualitative study. BMC Infect Dis 24 , 455 (2024). https://doi.org/10.1186/s12879-024-09352-7

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Received : 13 July 2023

Accepted : 25 April 2024

Published : 30 April 2024

DOI : https://doi.org/10.1186/s12879-024-09352-7

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