qualitative research sampling strategies

What We Offer

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

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

Who We Serve

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

• Consumer Packaged Goods
• Financial Services
• Media Technology
• Medical Device Manufacturing
• Marketing Research

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

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

Sago Announces Launch of Sago Health to Elevate Healthcare Research

Sago Launches AI Video Summaries on QualBoard to Streamline Data Synthesis

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

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

• Case Studies
• Knowledge Kit

Breaking the Silence on Women’s Health: From Perception to Truth

The Swing Voter Project Pennsylvania: April 2024

Get in touch

• Account Logins

Different Types of Sampling Techniques in Qualitative Research

• Resources , Blog

Key Takeaways:

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

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

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

In this Article:

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

Factors to Consider When Choosing a Sampling Technique

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

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

Request a consultation

4 Types of Sampling Techniques and Their Applications

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

1. Purposive Sampling

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

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

Purposive Sampling: Strengths and Weaknesses

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

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

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

2. Convenience Sampling  

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

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

Convenience Sampling: Strengths and Weaknesses

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

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

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

3. Snowball Sampling

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

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

Snowball Sampling: Strengths and Weaknesses

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

4. Theoretical Sampling

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

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

Theoretical Sampling: Strengths and Weaknesses

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

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

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

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

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

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

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

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

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

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

Tips for Selecting Participants

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

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

Ensuring Diversity in Samples

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

Maintaining Ethical Considerations

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

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

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

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

Find the Right Sample for Your Qualitative Research

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

A New Generation Is Born: Meet Gen C

Revolutionizing Healthcare Research: A Q&A With Industry Experts

The Deciders April 2024: Michigan Voters in Union Households

OnDemand: Navigating the Dynamic Future of Qualitative Research

How Connecting with Gen C Can Help Your Brand Grow

Quantifying Digital Detox Experiences Across the Globe

Digital Detox: How Different Generations Navigate Social Media Breaks

Building Trust Through Inclusive Healthcare Research Recruitment

The Swing Voter Project Wisconsin: March 2024

Take a deep dive into your favorite market research topics

How can we help support you and your research needs?

BEFORE YOU GO

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

Sampling Techniques for Qualitative Research

• First Online: 27 October 2022

Cite this chapter

• Heather Douglas 4  

2850 Accesses

3 Citations

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

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

Access this chapter

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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

Chapter   Google Scholar  

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

Article   Google Scholar  

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

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

Google Scholar  

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

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

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

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

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

Download references

Author information

Authors and affiliations.

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

Heather Douglas

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Heather Douglas .

Editor information

Editors and affiliations.

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

M. Rezaul Islam

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

Niaz Ahmed Khan

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

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

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

Download citation

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

Published : 27 October 2022

Publisher Name : Springer, Singapore

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

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

eBook Packages : Social Sciences

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

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.

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*

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

How to Subscribe

• Free Trials

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

Introduction.

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

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

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

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

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

Other Subject Areas

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

• English as an International Language for Academic Publishing
• Girls' Education in the Developing World
• History of Education in Europe
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

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.

back to top

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

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

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

Powered by:

• [66.249.64.20|185.147.128.134]
• 185.147.128.134

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.

Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

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.

• Related Documents

Linking Research Questions to Mixed Methods Data Analysis Procedures 1

The purpose of this paper is to discuss the development of research questions in mixed methods studies. First, we discuss the ways that the goal of the study, the research objective(s), and the research purpose shape the formation of research questions. Second, we compare and contrast quantitative research questions and qualitative research questions. Third, we describe how to write mixed methods research questions, which we define as questions that embed quantitative and qualitative research questions. Finally, we provide a framework for linking research questions to mixed methods data analysis techniques. A major goal of our framework is to illustrate that the development of research questions and data analysis procedures in mixed method studies should occur logically and sequentially.

Mixed methods research in pedagogy: Characteristics, advantages and difficulties in application

The mixed methods research, as a new type of research, is discussed in the context of re-examining the relation between the two approaches, i.e. the possibility of not opposing, but connecting, combining and integrating them within research. The possibility to use such grounding in research as well could be quite important for pedagogy since the nature of the examined phenomena in this field is such that the majority has both quantitative and qualitative aspects. In order to explain the essence of a mixed methods research, the paper analyses its characteristics, first of all, what elements are combined, what is the nature of the relation between the combined elements and why they are combined, as well as its advantages and difficulties in application. The essential thing in a mixed methods research is the fact that combining refers to the research process as a whole, including the ontological and epistemological assumptions it is based on, which implies that the elements that are combined are understood rather broadly. The advantages and difficulties in application are considered in the context of the discussion of the possibility to combine all research elements, neutralise the limitations of quantitative and qualitative research methodology, implement the complex combining procedures etc.

How Marketers Conduct Mixed Methods Research

The complimentary nature of qualitative and quantitative research methods are examined with respect to a study assessing the market's view of a training and development institute in the Middle East. The qualitative portion consisted of focus groups conducted with seven distinct market segments served by the institute. The results proved insightful with respect to uncovering and understanding differences of opinion among the seven groups; however, taken alone, the qualitative research would have been very misleading with respect to the institute's standing in the Middle East.

Using Mixed-methods Research in Health & Education in Nepal

In the areas of health promotion and health education, mixed-methods research approach has become widely used. In mixed-methods research, also called multi-methods research, the researchers combine quantitative and qualitative research designs in a single study. This paper introduces the mixed-methods approach for use in research in health education. To illustrate this pragmatic research approach we are including an example of mixed-methods research as applied in Nepalese research.Journal of Health Promotion Vol.6 2008, p.45-48

Research Methods

<p class="MsoNormal" style="text-justify: inter-ideograph; text-align: justify; margin: 0in 34.2pt 0pt 0.5in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">This paper discusses three common research approaches, qualitative, quantitative, and mixed methods, along with the various research designs commonly used when conducting research within the framework of each approach. Creswell (2002) noted that quantitative research is the process of collecting, analyzing, interpreting, and writing the results of a study, while qualitative research is the approach to data collection, analysis, and report writing differing from the traditional, quantitative approaches. This paper provides a further distinction between quantitative and qualitative research methods. This paper also presents a summary of the different research methods to conduct research in quantitative, qualitative, and mixed methods studies.</span></span></p>

Distinguishing Between Quantitative and Qualitative Research: A Response to Morgan

This is a response to Morgan’s article ( Journal of Mixed Methods Research, 12(3), 268-279) on the qualitative/quantitative distinction. I argue that Morgan has mischaracterized my views on this distinction, and on the value of design typologies in mixed methods research, and that the qualitative/quantitative distinction is more productively framed on a different basis than the one he proposed.

Analysis of Community Preparedness Facing Erosion Disaster in Sambas Regency

This research was conducted to analyze the community's preparedness in the face of erosion in Sambas Regency, which is erosion caused by filing by river water. This research is a mixed methods research<em>.</em> This mixed research combines qualitative research and quantitative research, with a sample of people living in riverbank areas. The head of the local household was also asked for information on preparedness to face erosion disasters. The results of the research on community preparedness for erosion in Sambas District were based on the researchers' view that the community was not ready, people who had filled out the new research questionnaire were about 15% out of 100% who were ready. The results of interviews with residents indicated that there was no counselling, training on this preparedness was also one of the causes of the low level of community preparedness.

Kesejahteraan Anak Adopsi Usia Prasekolah (3-5 Tahun)

Child welfare is the responsibility of the family because the child is part of the family. However, in reality there are still many who neglect their children so that the children's welfare is threatened. Abandoned children need protection to ensure their survival. One of the efforts made in dealing with the problem of neglected children is through an institution-based child service program through child social service institutions. However, institution-based child services have not been optimal in realizing children's welfare. Thus, children who are in institution-based care need to be transferred to family-based care so that the child's welfare can be better. One of the permanent efforts to care for children is through adoption. The method used in this research is mixed methods research method. The design chosen in this study is Explanatory Sequential Mixed Methods, the researcher will measure the level of children's welfare with quantitative research first followed by qualitative research. The results of quantitative research regarding the welfare of preschool adopted children show that basically the welfare of adopted children is in the good category. The results of the qualitative research found that the background and reasons or motivation of adoptive parents to adopt an effect on the care of the adopted child so that the child's welfare can be better. Most adoptive parents do not yet have biological children, so the presence of adopted children is a complement to their long-awaited family. The opportunity they get for adoption makes them try to care for, nurture, and treat their adopted child very well. They always pay attention to children's physical development, children's psychological development, children's social development and children's cognitive development so that children's welfare can be achieved.

Change, Challenges, and Mixed Methods

This chapter discusses three ongoing issues related to the evaluation of qualitative research. First, the chapter considers whether a set of evaluation criteria is either determinative or changeable. Due to the evolving nature of qualitative research, it is likely that the way in which qualitative research is evaluated can change—not all at once, but gradually. Second, qualitative research has been criticized by newly resurrected positivists whose definitions of scientific research and evaluation criteria are narrow. “Politics of evidence” and a recent big-tent evaluation strategy are examined. Last, this chapter analyzes how validity criteria of qualitative research are incorporated into the evaluation of mixed methods research. The elements of qualitative research seem to be fairly represented but are largely treated as trivial. A criterion, the fit of research questions to design, is identified as distinctive in the review guide of the Journal of Mixed Methods Research.

Mixed Methods Research in Developing Country Contexts: Lessons From Field Research in Six Countries Across Africa and the Caribbean

Mixed methods research in developing countries has been increasing since the turn of the century. Given this, there is need to consolidate insights for future researchers. This article contributes to the methodological literature by exploring how cultural factors and logistical challenges in developing contexts interplay with mixed methods research design and implementation. Insights are based on the author’s research experience of using mixed methods in six projects across three African and three Caribbean countries. Three lessons are provided to aid researchers using mixed methods working in developing countries. First, cultural factors call for more reflexivity. Second, adopting a pragmatic research paradigm is necessary. And third, the research process should be iterative and adaptive.

Export Citation Format

Share document.

• Open supplemental data
• Reference Manager
• Simple TEXT file

People also looked at

Original research article, assessment of veterinary pharmaceutical warehouse management practices and its associated challenges in four selected zones and bahir dar city of amhara regional state, ethiopia.

• 1 Department of Veterinary Pharmacy, Pharmaceutical Supply Chain Management, University of Gondar, Gondar, Ethiopia
• 2 Department of Logistic and Supply Chain Management, University of Gondar, Gondar, Ethiopia
• 3 Department of Social and Administrative Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethio
• 4 Department of Veterinary Pharmacy, Pharmaceutical Quality Assurance and Regulatory Affairs, University of Gondar, Gondar, Ethiopia

A pharmaceutical warehouse is part of the pharmaceutical supply chain and is essential to maintaining the quality and efficacy of veterinary pharmaceuticals for successful animal health service delivery. However, poor storage conditions, improper handling, and inappropriate use and disposal constitute challenges for veterinary supplies in animal health services. Therefore, this study aimed to assess the existing practices and challenges in warehouse management in government veterinary clinics and private veterinary drug wholesalers in Ethiopia. A cross-sectional study was conducted on 37 veterinary health facilities in four selected zones (south Gondar, west Gondar, central Gondar, and west Gojam zones) and Bahir Dar administrative city. Zones were selected using a simple random sampling technique. Data was collected using a structured questionnaire, pre-defined and tested observational checklists, and semi-structured interview guides. Descriptive statistics were used to analyze the quantitative data, while qualitative data was analyzed using a thematic approach. The study revealed the presence of poor stock management practices, such as the absence of standard operating procedures for warehouse activities in ~59.5% of facilities surveyed. In none of the surveyed facilities, bin cards and system software utilization were satisfactory. The absence of disposal guidelines was detected in 83.8% of the facilities, and the practice of timely disposal of expired drugs was not satisfactory. Compared to the government veterinary clinics, private veterinary drug wholesalers had better storage practices (86.25%) following theoretical recommendations. The storage conditions in government clinics were rated poor at 48.3% (>80%, which is the limit to the acceptable rate for good storage conditions). The challenges of inadequate infrastructure, a lack of qualified staff, problems with the availability and affordability of pharmaceutical products, insufficient regulatory practice, and budget constraints were identified. A holistic approach involving related stakeholders should be followed to improve the existing challenges and the sector's efficiency.

Introduction

In Africa, livestock plays a significant role in economic growth, and Ethiopia has the biggest livestock population on the continent ( 1 ). However, Ethiopia's livestock productivity is lower compared to the country's livestock population ( 2 , 3 ). The improper handling, inappropriate use, and poor disposal practices of veterinary pharmaceuticals are global issues that reduce livestock productivity and affect successful animal health services ( 4 – 7 ).

Veterinary pharmaceuticals are the foundation of animal disease diagnosis, treatment, prevention, and control, while the quality, safety, and efficacy of pharmaceuticals in a supply chain significantly depend on warehouse management ( 8 , 9 ). A warehouse is a functional and organizational structure designed for storing tangible products (stocks) in a dedicated area using proven technology, managed by a group of people, and furnished with the necessary technical tools. Warehouse management (WM) is part of a logistics system that comprises accepting, storing, issuing, recording, and tracking information flow throughout the process. In pharmaceutical supply chain management (SCM), strategic warehouse operations are the foundation of a service's success ( 10 , 11 ).

Good veterinary supplies management practices play a pivotal role in maintaining the quality of drugs and ensuring the delivery of successful animal health services. They also reduce non-conformities, promote efficient labor allocation, and reduce average operation time. Studies indicate that effective WM practices can save 15.7% of space across manufacturing and healthcare organizations and account for 2% to 5% of the cost of sales ( 12 , 13 ). On the other hand, improper handling of veterinary drugs impedes the quality of animal health services; it is also a leading cause of the risk of antimicrobial resistance (AMR) in humans ( 14 ).

According to the European Medicine Association (EMA), to achieve good veterinary drug management practices and reduce the wastage of veterinary supplies (VS) at any veterinary service delivery point (SDP), proper installation of warehouse and storage facilities, trained and qualified professionals, and standardized written manuals, policies, and guidelines are necessary components that must be fulfilled ( 15 ). In high-income nations, systems have been established to evaluate and track the quality of veterinary pharmaceuticals (VPs) available in the market and at SDP. However, most low- and middle-income nations find it difficult to monitor the use of veterinary drugs in livestock ( 4 ).

In most African countries, the poor advancement of animal healthcare is due to the influx of poor-quality drugs and complex drug distribution chains that involve many actors (both formal and informal) ( 4 , 16 , 17 ). According to a study conducted in Sub-Saharan Africa, the weak distribution of infrastructure and services and the multiplication of non-professional actors in the veterinary drug chains are the major constraints for farmers to access good-quality drugs ( 18 ). Another study conducted in Dares Salaam, Tanzania, revealed that poor record-keeping and the lack of guidelines on the appropriate disposal of veterinary medicines are the factors that affect drug handling and management ( 19 ). A study conducted in Nigeria showed that Africa had lost USD 4 billion due to preventable livestock diseases ( 4 ).

Until the French Veterinary Mission started offering modern veterinary services in 1908, Ethiopia's veterinary healthcare services were managed traditionally ( 20 ). However, currently, the government provides considerable veterinary healthcare services through clinics in every district and kebele (Ethiopia's fourth administrative level, following regions, zones, and districts). Veterinary clinics, designated to deliver veterinary healthcare services, veterinary pharmacy importers, wholesalers, and retail outlets, the majority of which are franchise veterinary drug businesses, professionally distribute veterinary pharmaceuticals throughout the country ( 2 ). Due to the interdependence and dependence among the activities in the veterinary pharmaceutical supply chain, a failure in one activity will have a detrimental impact on subsequent actions. For the provision of high-quality, sustainable animal health services, various service delivery institutions, such as veterinary drug importers, wholesalers, retailers, governmental animal health service providers (AHSP), animal health administrators, research and educational institutions, policymakers, legal affairs, and livestock owners, must all work together in the veterinary drug supply chain ( 20 – 22 ).

Literature indicates that the livestock sub-sector in Ethiopia is vulnerable to several veterinary pharmaceutical supply chain issues, including poor drug handling during purchase, irrational use of drugs, illegal marketing, poor-quality medicines, a lack of waste management practices, low rates of adherence to rules and policies, a lack of qualified public and private animal health services, and a lack of qualified and trained staff ( 5 , 6 , 21 , 23 , 24 ). Another study conducted in Ethiopia on the quality of veterinary drugs during post-marketing surveillance, re-registration, consignment checking, and pre-registration indicated that 8.2% of the examined veterinary medication samples were labeled as being of poor quality, and ~12 (1.3%) of the examined products had flaws in their appearance, packaging, or labeling ( 25 ). Veterinary drugs and biological products produced, imported, distributed, and used in the country are not effectively regulated and managed in terms of quality, safety, and efficacy ( 2 , 22 ).

Currently, due to the prevalence of animal diseases, VPs are widely used. However, in Africa, including Ethiopia, the VP sector, especially veterinary supplies warehouse management practices, is a neglected research area, except for some fragmented studies that show the presence of poor handling of veterinary drugs ( 4 , 5 , 24 ). Besides, the researcher had the opportunity to visit a few veterinary drug warehouses and noticed and understood that warehouse management practices lacked attention even though the products were expensive, unique, and dealt with animal life.

Implementing good veterinary supplies, particularly through WM, in the context of veterinary health facilities served as inspiration for addressing such types of issues and maintaining the overall veterinary pharmaceutical supply chain operations, which are crucial to improving veterinary healthcare services through the provision of quality VPs. Hence, the current study was conducted to assess the veterinary supplies warehouse management practices in relation to stock management, storage conditions, warehousing activities (receiving, storing, and issuing), human and material resources, and identifying the challenges encountered linked to the fundamental veterinary supplies warehouse operations at government district veterinary clinics and private veterinary drug wholesalers in four selected zones and Bahir Dar city of the Amhara region, Ethiopia.

Methods and materials

Study area and period.

The study was conducted in four selected zones and Bahir Dar administrative city in the Amhara region of Ethiopia from April 1, 2022, to January 15, 2023. The Amhara region is located in the north-western part of Addis Ababa, the capital city of Ethiopia. The study area covers the northern and western parts of the region. Administratively, there are 12 zones in the Amhara region. For this study, Bahir Dar City (the capital city of the Amhara region) was purposefully selected because most veterinary drug wholesalers are located in Bahir Dar City. The four zones, namely south Gondar, central Gondar, west Gondar, and west Gojam zones were selected based on the inclusion criteria of political stability and security concern at the time of data collection and veterinary health services coverage. In the study area, according to the 2022 report acquired from the Veterinary Drug and Feed Administration Control Authority (VDFACA) of the Amhara regional branch and the Amhara regional state livestock and fisheries resource development office, there are 66 facilities (14 private veterinary drug wholesalers and 52 government district veterinary clinics) serving to care for over 23 million livestock. Most veterinary drug wholesalers are found in the region's capital city, while others are scattered at the zonal level, and government veterinary clinics are at the district/woreda level.

Study design

A facility-based descriptive cross-sectional study design complemented by qualitative research approaches was conducted. The study mostly used a quantitative approach to produce numerical data, whereas a qualitative approach was used to explore the challenges faced in the catchment area and strengthen the quantitative data. The survey was done at veterinary health facilities using self-administered questionnaires, observational checklists, and face-to-face interviews with key informants (KI).

Source and study population

Source population.

The source population included all government veterinary clinics, all private veterinary drug wholesalers, all veterinary health professionals working with veterinary drugs in the warehouse and storage areas, and those who had a position related to pharmaceutical supply chain management in the four specified zones and Bahir Dar administrative city of Amhara regional state, Ethiopia.

Study population

Selected district government veterinary clinics and private veterinary drug wholesalers in the selected four zones and Bahir Dar city of the Amhara region were assessed to collect the necessary data. The government district veterinary clinic's veterinary drug and input supply officer, veterinary drug store personnel and veterinary drug dispenser, veterinary drug wholesale owners, wholesaler technical managers, wholesaler assistant storekeepers, district livestock and fishery resource development heads, and animal health department coordinators were all contacted and invited to participate in this study.

Inclusion and exclusion criteria

District veterinary clinics, veterinary drug wholesalers, and employees working with veterinary drugs and had positions related to veterinary drug handling and management practices for at least the last 6 months during data collection participated in the study. Zones that suffered from political instability and security concerns during data collection were excluded. Government animal health posts located at the kebele level, private veterinary clinics, and private retail outlets were also excluded as they did not have a permit to hold veterinary pharmaceutical stock.

Sample size determination and sampling techniques

Sample size determination.

The formula developed by Cochran in 1963 for calculating sample sizes when the proportion is the parameter of research was applied, and a 90% confidence level with a 10% margin of error was used. Using this formula and assuming a 10% non-respondent rate, 37 health facilities were selected as a study sample from a refined population of 66 facilities (government district veterinary clinics and private veterinary drug wholesalers) in the study area. The quantitative and qualitative data were obtained from 29 government district veterinary clinics and eight private veterinary drug wholesalers ( Supplementary material 1 ).

The general formula was calculated using Equation (1) :

Where, no is the sample size required, Z is the Z value (1.64 for a 90% confidence level), and p is the estimated prevalence of the indicator. The product of [p] and [q] is maximized when p = 0.5. Therefore, when the prevalence is unknown, 0.5 should be used, and e2 = the 10% margin of error used in estimating the prevalence, e2 = 0.1. However, the sample size (n 0 ) was adjusted to n: This adjustment can substantially reduce the necessary sample size for small populations and is also called the population correction factor ( 26 ) ( Equation 2 ):

Where, n = the adjusted new sample size; N = the population size; n 0 = the sample size obtained from the general formula.

n = 33.5–34 facilities, with a 10% non-response rate, 37 facilities.

Accordingly, 37 veterinary health facilities were selected proportionally from the four zones and Bahir Dar city, and 75 participants working in the selected veterinary health facilities participated. The selection was purposefully made based on their direct involvement in pharmaceutical warehouse management activities and their position in the facilities. Practically, during data collection, some facilities did not have veterinary drug and input supply employees, others did not have drug store and control employees, and a few others did not have a drug dispenser. Due to the small size of the target population in the selected facilities, all 75 professionals participated in this study. Key informants for qualitative data collection were determined based on the viewpoints of different researchers and the principle of data saturation ( 27 , 28 ). The KIs were selected from the 37 veterinary health facilities. Their positions, being decision-makers and having information on the issues of veterinary supply warehouse management practices, were considered during the selection of KIs. Accordingly, the districts' livestock and fishery resource development heads, animal health department coordinators, private veterinary drug wholesale owners, and veterinary drug wholesaler technical managers working from the selected facilities were invited.

Sampling techniques

In the Amhara region, there are 12 zones, so it was difficult to address all district veterinary clinics and private veterinary drug wholesalers in these zones. Therefore, for general representation of the study site, the four zones and Bahir Dar administrative city were selected based on the inclusion criteria and because of their high density of veterinary health facilities and service coverage. Veterinary clinics and drug wholesalers in the four zones and Bahir Dar city were completely enumerated and listed. Then, the health facilities were stratified according to the types of facilities, such as government district veterinary clinics or private veterinary drug wholesalers. Finally, the number of veterinary health facilities included in the sample from each stratum was determined using a proportional size allocation technique ( 28 ). The study was conducted at 37 facilities, of which 29 were government district veterinary clinics, and eight were private veterinary drug wholesalers, selected by a simple random sampling technique ( Supplementary material 2 ).

Regarding study participants, district veterinary clinic drug store personnel, district veterinary clinic drug store manager, district veterinary drug input and supply officer (from the selected government district veterinary clinics), wholesaler veterinary drug assistant storekeeper, and wholesaler veterinary drug technical manager (from private veterinary health facilities) were invited to fill out the self-administered structured questionnaire. For key informants, the district livestock and fishery resource development heads, animal health department coordinators, private veterinary drug wholesale owners, and veterinary drug wholesaler technical managers (from the selected private veterinary health facilities) were invited to participate in the interview. The selection of the KI was based on their position as decision-makers and because they are familiar with pharmaceutical supply chain information and related activities. Moreover, they also have information about the challenges and related factors in the veterinary supplies warehouse management practice.

Data collection tools and procedures

To collect the primary data, structured self-administered five-level Likert scale questionnaires [that were rated from strongly disagree to strongly agree where 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree], observational checklists, and a semi-structured interview guide [adopted from standard criteria from the Logistic Indicator Assessment Tool (LIAT) developed by the USAID Deliver Project and data collection tools] from various related articles were referred for adoption and customized to the local context ( 11 , 24 , 29 – 31 ).

Two veterinary pharmacists and the principal investigators were allocated and participated as data collectors for quantitative and qualitative data. Quantitative data used to assess veterinary pharmaceutical stock management-related practices was collected using structured, self-administered questionnaires. The five-level Likert scale questionnaires were used to assess warehousing activities and human and material resource management practices at the selected facilities, and 75 participants filled out the questionnaire. The veterinary supplies storage conditions of the facilities were evaluated using checklists through direct physical observation ( Supplementary material 3 ), and a semi-structured, open-ended interview guide was used to collect the qualitative data through face-to-face interviews.

The district veterinary clinics veterinary drug and input supply officer, veterinary drug store and control personnel, drug dispensers, veterinary drug wholesalers technical managers, and assistant storekeepers from the sampled facilities participated in filling out the self-administered questionnaire. The qualitative data was collected through face-to-face interviews with KIs using the prepared interview guide. Interviews were conducted with district livestock and fishery resource development heads, district animal health department coordinators, private veterinary drug wholesaler owners, and wholesaler technical managers. Interview guides were prepared in English and then translated into Amharic, the working and local language in the study area. The principal investigator interviewed the KIs in depth for an average of 30 min. Notes were taken, and KI responses were also audio-taped.

Data quality assurance

The study questionnaire and interview guide were derived from a standard tool and developed after reviewing previously studied related research ( 11 , 24 , 30 ). To maintain the quality of the data and to encourage the meaningful participation of the respondents, the layout of the questionnaires was kept clear and very simple. Prior to data collection, the principal investigator provided training to data collectors on data collection procedures and the significance of the study. Before being entered into the Statistical Packages for Social Science (SPSS) and MS Excel, the collected data was carefully checked for accuracy, cleaned for completeness, consistency, omissions, and irregularities., Every day, during data collection, the misunderstood questions were elaborated accordingly. To ensure the reliability of self-administered questionnaires and the respondent's understanding of the questions, the questionnaire was pretested with 5% of the total sample size of the study, which is not included in the study area.

A scale reliability test was conducted for Likert scale items and reliability analysis; Cronbach's alpha was calculated using SPSS version 26. If the Cronbach's alpha coefficient is close to 1.0, then there is greater internal consistency in the items, and a value >0.700 is considered very acceptable for SCM activities (72). For this study, the value of Cronbach's alpha ( Table 1 ) for the Likert scale questionnaire is >0.70.

Table 1 . Summary of the reliability analysis test.

For qualitative data, the probing and flexible questions and interview guide were initially prepared in English and then translated into Amharic by the principal investigator after consulting with people with good command of the two languages. Data collection was undertaken by the researcher, and the interviews were transcribed each day after the interview. Missing ideas and any need for clarification were addressed throughout the process. For data consistency and completeness, all Amharic transcripts were cross-checked with the oral discourse. After repeated reading of the filled-out notes and careful listening to the audio records, coding, and recoding of the contents were done with peer review. The principal investigator also used the reflexivity method to improve the quality of data collection, which enabled better probing, fewer assumptions, the avoidance of premature interpretation, and an accentuated sense of curiosity during the interview ( 32 ).

Data analysis and interpretation

The collected primary data was used to show the magnitude of stock management, storage conditions of the facilities, warehousing activities of the facilities using the target variables (receiving, storing, and issuing), and human and material resource management practices at a facility level. The quantitative data was coded and entered into SPSS version 26 and Microsoft Excel 2010 for analysis. Descriptive statistics (frequency, percentage, mean, and standard deviations) were computed, and summary results were presented using tables, graphs, and charts. The qualitative data obtained from the in-depth interview was analyzed and summarized using a thematic approach ( 33 ). The grand mean and standard deviation (SD) were used to interpret the Likert scale data gathered to assess the warehousing activities of the target variables (receiving, storing, and issuing) and the human and material resource management practices at a facility level. Each warehousing activity (receiving, storing, and issuing) was assessed using four items, and human and material resource management practices were assessed using seven items. The grand mean and SD of the target variables were computed from the respective items ( Table 7 ). The interval range of the 5-likert scale ( Table 2 ) was calculated according to the principle of the grouped data frequency distribution formula ( 34 ). The mean of their response scores for each variable represented their level of satisfaction with pharmaceutical warehousing activities and human and material resource management practices, whereas the SD represented their deviation from the central value ( 35 , 36 ).

Table 2 . The interval range of Likert scale questionnaires used for this study.

The results of the computed mean were then leveled as “strongly disagreeing” if a variable with a mean score fell in the interval of 1–1.8, “disagreeing” if the score fell in the interval of 1.81–2.60, “neutral” if the score fell in the interval of 2.61–3.4, “agreeing” if the score fell in the interval of 3.41–4.2, and “strongly agreeing” if the score fell in the interval of 4.21–5. An SD of > 0.9 implies a significant difference in the target variable among respondents ( 37 , 38 ).

To interpret the results of the mean and SD easily and clearly, the scales were reassigned as follows, and the verbal interpretation was made based on the recommendations of previous researchers ( 37 – 39 ) ( Table 3 ).

Table 3 . Verbal interpretation of the scale.

The percentage of storage conditions was calculated as the average number of “yes” responses in the checklist and the number of standard storage conditions in the checklist ( 40 ) using Equation (3) .

The management of storage conditions associated with veterinary pharmaceutical products was also classified as “poor” and “good” store management. The interpretation was made based on the recommendation from previous research and the principles of pharmaceutical warehouse operations management of the Ethiopian Pharmaceutical Fund Supply Agency (PFSA), now known as the Ethiopian Pharmaceutical Supply Services (EPSS), published in 2015 ( 11 ). Based on this, pharmaceutical warehouses or facilities that fulfilled at least 80% of the criteria for good storage conditions were considered acceptable and have good storage conditions, whereas those that fulfilled <80% were considered poor storage conditions ( 40 ).

For the qualitative data, the principal investigator performed face-to-face, in-depth interviews to explore the challenges faced in veterinary pharmaceutical warehouse management practices. The investigator transcribed the audio recordings of in-depth interviews and discussions verbatim. Textual notes and audio-recorded data were repeatedly read and listened to. Audio recordings and notes were translated into English. The thematic analysis technique was used to analyze the data collected from the KIs as per the approach and steps recommended by Braun and Clarke ( 41 ). By doing so, the investigator became familiar with the textual notes and audio recordings and began taking notes accordingly. Then, the data was coded and written up using MS Word. The coded data was organized to search for themes and subthemes. After that, similar subthemes were grouped, named, and described thematically. Thematic contents were formulated, and a master list of themes was developed based on the research questions and conceptual framework. Finally, the report was produced using an exploratory approach and triangulated with the quantitative result.

The background information of veterinary health facilities and respondents' profile

To assess pharmaceutical warehouse management practices and their challenges, 37 veterinary health facilities−29 (78.4%) district veterinary clinics and 8 (21.6%) private veterinary drug wholesalers—were invited. All 37 facilities participated with a 100% response rate. Among the respondents, the majority (27; 36%) were district veterinary clinic drug store personnel, and 6 (8%) were veterinary drug wholesaler technical managers ( Figure 1 ).

Figure 1 . Professional designations of the respondents from district government veterinary clinics and private veterinary drug wholesalers.

The majority of respondents (35; 46.7%) were qualified in advanced animal health, and 4 (5.3%) had veterinary pharmacy professional qualifications ( Figure 2 ). Furthermore, 36 (48%) of the respondents held degrees, 35 (46.7%) held diplomas, and 4 (5.3%) had obtained MSc level of education. In terms of work experience, 42 (56%) had 3–6 years, 18 (24%) had >7 years, and 15 (20%) had 0–2 years of experience.

Figure 2 . Professional qualifications of the respondents. AH, Animal health; BVSC, Bachelor of Veterinary Science; Vet, Veterinary; DVM, Doctor of Veterinary Medicine.

Advanced animal health: animal health professionals who took advanced animal health qualification courses at the college level for 3 years and were certified.

Bachelor of Veterinary Science: Veterinary professionals who took animal health qualification courses at the university level for 3 years and were certified.

Veterinary Pharmacy: Professionals who have been certified by taking full veterinary pharmacy qualification courses for 4 years at the university level.

Doctor of Veterinary Medicine: a veterinary professional who took animal health qualification courses at the university level for 6 years and was certified.

Assessment of veterinary drug warehouse management practices of the facilities

Veterinary pharmaceutical stock management-related practices of the facility.

All the facilities managed both veterinary drugs (medicines) and other equipment and supplies used in veterinary healthcare services. The majority of the facilities (26; 70.3%) used mixed-type drug arrangement methods, while 8 (21.6%) used pharmacological drug arrangement methods. Bin cards and system software/electronic data interchange technology were not used in any of the surveyed facilities. Of the surveyed facilities, in 23 (62.2%), pharmaceutical information was handled manually or on paper, and 4 (10.8%) utilized mixed-based information handling methods. More than half of the facilities (59.5%) also reported that they did not have a written manual or standard operating procedure (SOP) to manage warehouse practices. The majority of facilities did not dispose of expired products on time (32; 86.5%) and did not have documented policies and guidelines (31; 83.8%) for the management of veterinary drug waste ( Table 4 ).

Table 4 . Summary of stock management practices at the facility.

Assessment of storage conditions by facility type

Storage conditions of district government veterinary clinics.

The storage areas of 29 governmental veterinary clinics were assessed through physical observation using 20 criteria. The study found that the majority (27; 93.1%) of the facilities were protected from direct sunlight−19 (65.5%) stores had separate storage and dispensing areas. However, none of the stores had fire safety equipment or wall thermometers. Only 14 (48.3%) facilities had separate storage areas for expired and damaged products and a very limited number of stores (20.7%) had pallets and shelves. In only a few stores were products stacked at least 20 cm away from the walls (17.2%), 10 cm off the floor (10.3%), and on racks over 2.5 m in length (17.2%). Overall, the average performance of district government veterinary clinics that met the criteria for acceptable storage conditions was 48.3% ( Supplementary material 4 ).

Of the 29 surveyed government district veterinary health clinics, no facility met the criteria for good storage conditions, and seven had a storage condition performance of 25% or below ( Figure 3 ).

Figure 3 . Performance of storage conditions at government district veterinary clinics.

Storage conditions in private veterinary drug wholesalers

The stores of 8 private veterinary drug wholesalers were assessed through physical observation using 20 criteria. The study found that products were protected from direct sunlight in all stores. In all the stores visited, palettes and shelves were accessible, and dispensing and storage areas were separated. The majority (75%) of the facilities had separate storage rooms for damaged and expired goods, and during the physical inspection, all stores looked free from harmful insects and rodents. Only four facilities (50%) had separate and specialized storage areas for flammable products and chemicals. Products were stacked at least 20 cm away from walls, 10 cm from the floor, and on racks that were 2.5 m in length in the majority of stores (87.5%) inspected. Overall, the average performance of private veterinary drug wholesalers that complied with the acceptable storage criteria was 86.25% ( Supplementary material 5 ). Of the eight private drug wholesalers surveyed, six met the criteria for good storage conditions with an average percentage >80% ( Figure 4 ).

Figure 4 . Performance of storage conditions at private veterinary drug wholesalers.

The overall adherence to storage conditions in the district government veterinary clinics and private veterinary drug wholesalers was, on average, 48.3 and 86.25%, respectively ( Figure 5 ).

Figure 5 . Average adherence to acceptable storage conditions by facility type.

Assessment of veterinary pharmaceutical warehousing activities

The warehousing activities (receiving, storing, and issuing) of the surveyed facilities were analyzed using descriptive statistics, and the grand mean and standard deviation of the target variables were computed ( Table 5 ).

Table 5 . The grand mean and standard deviation of the target variables.

Receiving activities

The majority of respondents had a “neutral” response to their facility's pharmaceutical receiving activities, with a mean value of 3.31 and an SD of 0.64. The individual response for each item in the receiving activities is shown in Table 6 .

Table 6 . Respondent's perception of each item in the receiving activities of the facility.

Storing activities

The majority of respondents were found to “disagree” with their facility's pharmaceutical storing activities, with a mean value of 2.53 and an SD of 0.89. The individual response to each item in the storing activities is shown in Table 7 .

Table 7 . Respondent's perception of each item in the storing activities of the facility.

Issuing activity

The study found that the majority of respondents agreed on their facility's pharmaceutical issuing activities, with a mean value of 4.07 and an SD of 0.44. The individual response to each item in the issuing activities is shown in Table 8 .

Table 8 . Respondent's perception of each item in the issuing activities of the facility.

Assessment of human and materials resource management practices of the facilities

The current study found that the majority of respondents “disagreed” with the facilities' human and material resource management practices, with a mean value of 2.40 and an SD of 0.61 ( Supplementary material 6 ). The socio-demographic data collected for this study indicated that the majority (71; 94.7%) of employees had non-veterinary pharmacy professional qualifications, and only 4 (5.3%) were veterinary pharmacy professionals ( Figure 2 ). The study also found that 43 (57.3%) participants had not received or participated in any on-the-job training sessions ( Table 9 ).

Table 9 . Summary of training history of respondents.

When the respondents were asked about training, all of them indicated their desire to take training in the future, and they pointed out the types of training they required ( Figure 6 ).

Figure 6 . Training desirability among respondents.

Results of qualitative data

In this study, qualitative data was collected using a face-to-face interview to identify and explore the challenges faced by government district veterinary clinics and private veterinary drug wholesalers in managing their veterinary supplies warehouses.

Socio-demographic characteristics of key informants

A total of 14 KIs were interviewed for this study. The majority of them (5; 35.7%) were District Livestock and Fishery Resource Development heads, 4 (28.6%) were Animal Health Department coordinators, and the remaining were from private veterinary drug wholesalers ( Figure 7 ). Concerning their educational qualifications, 5 (35.7%) had a doctorate in Veterinary Medicine, 5 (35.7%) had a bachelor's degree in Veterinary Science, 2 (21.4%) had a diploma in Advanced Animal Health, and 2 (21.4%) had a degree in Veterinary Pharmacy.

Figure 7 . Socio-demographic characteristics of key informants. DLFRD, District Livestock and Fishery Resource Development; AH dept, Animal Health Department; Vet, veterinary.

The assessment of the challenges linked to the veterinary pharmaceutical warehouse management practices

As perceived by the KIs, the surveyed facilities face enormous challenges related to veterinary pharmaceutical warehouse management practices. Based on the characteristics of the data, the challenges were divided into three major thematic areas. These were infrastructure challenges, human and material resource challenges, and challenges related to the sale and purchase of veterinary drugs.

Theme one: challenges associated with infrastructure

This theme focuses on challenges raised regarding the warehouse and storage areas of veterinary pharmaceutical products. Key informants reported that the lack of adequate storage space is a challenge in almost all facilities. Most KIs mentioned that “the layout of our pharmaceutical warehouse is not designed based on the standard of drug storage and lacks storage space to accommodate all the stock appropriately” (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

This statement was supported by one other KI who said:

The main challenge in our district is the inadequacy of drug storerooms. As you have seen, our drugstore is very narrow and old, and we do not have enough pallets and shelves. Our drug store is not free from leakage of water, dust, or direct sunlight. We have no warehouse built for this purpose. To store drugs, we assign empty buildings or offices. It is not as per the standards (Coordinator, District Animal Health, BVSc, 3 years of experience).

Another KI stated:

Due to a shortage of adequate storage space, we are forced to store different products, like flammable chemicals, laboratory reagents, expired products, and even non-functional equipment, together with unexpired pharmaceutical products, in the same storage area. This makes it impossible to track the product accurately. But we have no option (Head, District Livestock And Fishery Resource Development, MSc, 5 years of experience).

One more KI emphasized:

The drug storage area is our major challenge. During a meeting at the district and regional level, I wrote a letter and forwarded questions to district leaders and to other higher governmental bodies. But still, this problem is not solved. This is due to budget constraints and the lack of awareness and attention of district leaders and other higher governmental bodies in this sector. Some even think that veterinary drugs do not need special warehouses and storage areas because they consider veterinary drugs to be like other materials (Head, District Livestock And Fishery Resource Development, DVM, 3 years of experience).

On infrastructure, almost all respondents from the private drug wholesalers emphasized that issues related to building rent are their major challenges. “There is a time-to-time rent cost increment, and it isn't easy to search for standard buildings that fit with the directives of VDFACA. So, it creates a big challenge for our business” (Owners and Technical Managers, Private Wholesalers).

Theme two: challenges associated with human and material resources

Human resource.

Most key informants mentioned that the shortage of qualified staff to perform activities like drug storage, issuing, and dispensing is a challenge. A KI remarked, “In our woreda, most of the district clinic drug stores and dispensers are run by non-pharmacy professionals.” One KI stated:

This is the only district clinic in this woreda, and we have 37 clinics at the kebele level. Conversely, we have only one district drug store control person. He performs many activities. He works as an accountant by giving receipts to customers. At the same time, he dispenses drugs. He also works as a drug store employee, issuing drugs to professionals coming from each kebele. There is a high workload among the available professionals. There was no compensation for work overload, and there were no educational opportunities. So, how can we be effective in drug handling and management? (District livestock and fishery resource development heads and animal health department coordinators, Coordinator, Animal Health Department, BVSc, 7 years of experience).

This KI also confirmed that “there is no job description used for district drug dispensary and drug store personnel.”

Training related challenges

All KI interviewed from district clinics mentioned that their main challenge in pharmaceutical warehouse management was the lack of equipment and materials such as fire extinguishers, ventilation, wall thermometers, computers, cold chain materials/ice boxes, and vehicles used for drug transportation (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

This statement was emphasized by another KI:

Our district is grouped under the desert area, so the drug store needs ventilation and a wall thermometer to control the temperature daily, but as you see, our wall thermometer and the ventilation have not been functional for the last 2 months. Non-functional equipment is not timely renewed (Coordinator, District Animal Health Department, DVM, 5 years of experience).

Yet another KI added:

We have no vehicle for transporting drugs from our supplier and distributing drugs to our kebele clinics. Drugs are transported in public vehicles like Bajaj, motorcycles, or even by human carriage. This exposes drugs to external factors like direct sunlight, which reduces the quality of the drug (Head, District Livestock and Fishery Resource Development, BVSc, 9 years of experience).

All the KI mentioned that they need system software and standardized manuals. One KI admitted that these manuals “facilitate our warehousing activities, but still no one uses them.” Another KI added, “our activities are not electronic. This is due to budget constraints and a lack of computers. Our staff is not also trained in this regard.” (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

Theme three: challenges associated with the sale or purchase of veterinary drugs

KIs were interviewed about the challenges faced in the sale and purchase of pharmaceuticals, and all highlighted that availability, affordability, financial resources, and regulatory-related challenges were their major issues.

Availability and affordability of veterinary drugs

According to the KIs, the availability and ability to obtain essential veterinary drugs at an affordable cost are their biggest challenges. In particular, the KIs from drug wholesalers indicated that there was an inadequate supply of veterinary drugs. For instance, one KI said, “Importers are not able to supply the drugs needed by our customers. Even now, it is difficult to afford the drugs available on the market. The lack of availability of essential veterinary drugs is our major challenge in our commercial endeavors.” Another KI added: “As an example, when we see pen-strep, it has not been available on the market for the last 2 months, and its cost has increased from 170 to 650 ETB per vial” (Technical Manager and Owner, Veterinary pharmaceutical warehouse, 2 years of experience).

All the KIs from the district clinics also emphasized this statement by saying that:

Drug availability is our major challenge. We have had no pen-strep for the last 4 months. This is very essential for treating the majority of animal diseases. The Amhara region veterinary drug and input supply agency is the main supplier for all districts in the region, but the agency cannot supply as per our requisition.

One KI strongly highlighted,

We frequently face a shortage of animal drugs. We do not get some items on the market because of the current shortage of hard currency in our country, and due to this, it is difficult to deliver full services to the community (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

Regulatory-related challenges

Most KIs from district clinics mentioned that “government regulatory bodies like VDFACA and other regional and zonal agricultural and livestock offices do not support us in the fulfillment of pharmaceutical warehouses except for some irregular training they deliver.” Another KI added by saying, “We expect more from VDFACA in addition to facilitating training to realize the quality of veterinary drugs. But still, their effort in this regard is very low” (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

All the KIs from the drug wholesalers emphasized that “The lack of a regulatory and service chain between regional and federal regulatory bodies of VDFACA is a challenge. Making contracts with our employees (technical managers and assistant storekeepers) and even renewing our license requires a trip to the central FDFACA.” On this issue, all the respondents appeared quite emotional and exasperated and asked, “Why is the Amhara regional branch of the VDFACA unable to perform these tasks? This creates a big challenge for our services.”

Most of the KIs also highlighted that “Disposing of expired and unusable pharmaceutical products is our challenge. There are teams or committee members organized from different sectors, but the team is not working actively. We don't have policies and guidelines to manage these waste products” (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department). One KI from the government district clinic emphasized, “I have worked in this district for over 15 years, including the last 5 years as the district livestock resource coordinator. In our drug store, there are many expired vaccines, drugs, and chemicals that were in our clinics and collected from kebele clinics starting 10 years ago and are still lying in our store. We always write a letter to the district managers, but they are still not committed to implementing this” (Coordinator, Animal Health Department, BVSc, 16 years of experience).

Budget-related challenges

Most KIs from the district clinics mentioned that: “Inadequate allocations of budgets are the major challenge for us to fulfill regulations relating to premises, including infrastructure, buildings, and human and material resources, which are essential for achieving good practices in pharmaceutical warehouse management and providing basic service at the facility level.” A majority of them support the argument that “even within the sector, budget allocation is not fair since the livestock sector has merged with agriculture. Most of the budget is allocated for the agricultural and livestock production wings rather than the maintenance of livestock health” (heads, District Livestock and Fishery Resource Development and coordinators, Animal Health Department).

To maintain the quality and efficacy of pharmaceutical products, good warehouse management is central and requires attention among other pharmaceutical supply chain activities. To provide effective health services, whether in animal or human health aspects, a pharmaceutical warehouse and store should be properly installed, and drugs should be properly managed and handled ( 11 , 14 ). Without proper pharmaceutical storage management, the entire healthcare system will fail. In the livestock sector, high-income countries have established systems for assessing and monitoring the quality of veterinary products available in the market and at service delivery sites, whereas most low- and middle-income countries still struggle to monitor the proper use of veterinary medications ( 4 ).

Literature suggests that pharmaceuticals should be clearly organized and arranged with each zone of the store to make it much easier for store personnel to control stock, take periodic stock inventories, pick orders, and time will not be wasted ( 42 , 43 ). The current study found that in the majority (26; 70.3%) of surveyed facilities, mixed-type veterinary drug arrangement methods were used. This finding is higher than the finding of the study conducted on the pharmaceutical storage of public health centers at North Shoa Zone, which showed that in 29.3% of facilities, products were arranged in mixed types. The observed difference could be due to the availability of adequate storage space and sufficient shelves and pallets in the medical health sector. In that study, shelves were sufficiently available in 27 (65.9%) facilities out of 41 health facilities ( 44 ), whereas the availability of pallets and shelves in the current study was only about 60.35% on average from the surveyed 37 facilities.

Implementing automated systems is essential for managing warehouse operations and enables warehouse managers to complete their responsibilities more quickly, precisely, affordably, and flexibly (3, 41, 42). The present study found that none of the facilities had system software or electronic data interchange technology, 23 (62.2%) facilities handled pharmaceutical-related information, and everything was paper-based. This contradicted the findings of a study conducted at private medical drug wholesalers in Gondar, Ethiopia, which showed that 80% of the surveyed facilities' pharmaceutical warehouse management practices used the Professional Electronic Data System (PEDS) ( 11 ).

This difference might be due to the type of health facilities studied and the fact that the amount of stock managed in the medical pharmacy store may be huge, making it difficult to manage that huge stock manually. The non-use of system software in veterinary health facilities may be due to financial constraints on computer access and a lack of trained professionals. The quantitative result of this study indicated that the activities being carried out in the surveyed facilities were manual and paper-based. This was also supported by the qualitative result, in which budget-related constraints, computer access, and a lack of trained staff were the major challenges to automating their warehousing practices. Therefore, these findings establish that veterinary pharmaceutical stock arrangements and pharmaceutical-related information are not handled in an organized way.

Even if using a bin card is a time-consuming and laborious task, implementing this professional tool enables store managers to accomplish their activities in a rapid, effective, and cost-effective manner ( 11 ). However, this study revealed that bin cards were not utilized in any of the surveyed facilities. This finding contradicts the results of a study conducted on inventory management of laboratory commodities in Gambela regional state and Jimma zone, Southwest Ethiopia, which found that utilization of bin cards was 58.8 and 69.9%, respectively ( 45 , 46 ). Additionally, the result is also contrary to the findings of the study conducted in public health centers on pharmaceutical store management practices in Addis Ababa and the North Shoa Zone, which indicated that bin card utilization was 48.9 and 54%, respectively ( 30 , 44 ).

The quantitative result of the current study showed that the performance of bin card utilization in the surveyed veterinary health facilities was very low. The qualitative result also supported this finding with KIs noting the presence of a professional awareness gap on the use of bin cards; low ownership and attention given by higher managerial units; less commitment of the district veterinary drug and input supply officer; a lack of trained and qualified store personnel; and a lack of supportive supervision by regulatory bodies and district leaders. This indicated poor implementation of veterinary supplies stock-keeping practices in the surveyed veterinary health facilities.

The standard operational procedure simplifies the warehouse's operations by providing specific step-by-step instructions for each activity and ensuring the quality of the activities performed in the warehouse uses the same measurable standards every time ( 47 ). However, the present study found that 22 (59.5%) facilities had no standardized written manual. It was lower than the results of the study conducted in public health centers and hospitals in Dessie Town, Ethiopia, which showed that 80% of the facilities had standard guidelines for managing commodities in their stores ( 48 ). The observed difference might be due to differences in study facilities. The finding of this study was to deduce if in the majority of veterinary health facilities, warehouse practices are performed randomly or as per standards.

Scientific evidence recommends that expired or damaged stocks should be immediately removed from the usable inventory and sent to a separate place according to the established guidelines. This is because pharmaceutical waste could be dangerous and may pollute the environment habituated by the general public or wildlife, or even be diverted to the marketplace for illegal resale ( 49 , 50 ). However, the present study found that the majority (32; 86.5%) of surveyed veterinary health facilities did not dispose of expired products promptly, and 31 (83.8%) facilities did not have documented policies and guidelines for managing veterinary drug waste. The disposal practices found in the current study were poor compared to the study conducted in the North Shoa Zone, which showed 63.4% of health facilities disposed of pharmaceutical waste. In a study conducted in Addis Ababa, 66.7% of the health centers disposed of pharmaceutical waste within a year, and the availability of waste disposal documents was 100% ( 44 , 51 ). The observed difference could be due to differences in study settings, as currently, the human health sector is implementing an integrated pharmaceutical logistics system throughout health facilities and using the pharmaceutical waste rate as one of the key performance indicators for pharmaceutical logistics. According to the quantitative result of the current study, the expired and waste product disposal practices in the veterinary health sector were poor. This was also strengthened by the results obtained from the face-to-face interview, as the majority of KIs stated that most facilities do not have policies or guidelines to manage waste products. Some government district clinics have not disposed of expired products for the past 10 years.

Storage conditions of the facilities

Storage conditions are regarded as the cornerstone of warehouse management practices. Any defect in the storage area may result in obsolescence, deterioration, spoilage, pilferage, or breakage of stock due to excessive overstocking. Furthermore, the poisonous degradation of products can be hazardous to humans and the environment ( 30 , 52 ). The present study revealed that the average percentage of storage conditions in government district veterinary clinics and private veterinary drug wholesalers is 48.3 and 86.25%, respectively. This observed difference in storage performance between governmental and private entities could be because private veterinary drug wholesalers might be subject to inspections by government regulatory bodies. Furthermore, they also face strict control for the layout and fulfillment of the warehouse premises before their license is issued. According to this study's finding, the government district veterinary clinics did not meet the criteria for acceptable storage conditions, which was below the acceptable range (80%).

This finding is consistent with the study results conducted on assessing pharmaceutical store management practices in public hospitals in Addis Ababa, which showed an average adherence to proper storage conditions at 47.1% ( 30 ). The similarity of the findings could be that both were governmental facilities, so regulatory bodies and management units may not pay attention. However, the result was lower than the results of the study conducted on inventory management for laboratory commodities from health facilities in Gambela regional state and Jimma zone, Ethiopia, which indicated that the overall adherence to the criteria for proper storage conditions was 68.2 and 70.6%, respectively ( 45 , 53 ). The current study generally indicated the storage conditions in governmental district veterinary clinics were poor and below the acceptable limit (≥80%). This could be due to a lack of adequate storage space. The qualitative result also supports this because most of the KIs invited for interviews stated that the main challenge in their districts was the inadequacy of drug storerooms and the lack of standardized design and layout.

On the other hand, private veterinary drug wholesalers met the criteria for acceptable storage conditions, with an overall performance of 86.25%, which is considerably good (≥80%) ( 40 ). The percentage of storage conditions in private veterinary pharmaceutical wholesalers found in this study was higher than those of a study conducted on pharmaceutical warehouse management practices among private medical pharmaceutical wholesalers in Gondar, Ethiopia, which found that the facilities' storage performance was 68.75%. The difference could be due to the commitment of the concerned regulatory bodies to inspecting, controlling, providing feedback, and supervising the facilities.

Warehousing activities of the facilities

Receiving, storing, and issuing/shipping goods are the key operational tasks carried out in the warehouse, and proper practice of all the tasks is vital to warehouse management ( 54 ). Scientific studies suggest that warehouse management practices may differ across different sectors. It depends on various variables, such as material turnover and demand specifications, the type of materials used, the organizational unit's operational scope, and its size ( 55 ). Researchers recommend that if the tasks of receiving in a warehouse are not operated properly, they make up roughly 10% of operating expenditures in any distribution center ( 56 ). Regarding the receiving activities, the present study found that the majority of respondents had “neutral” responses to the performance of pharmaceutical receiving activities at the facility level, with a computed mean value of 3.31 that falls within the range of 2.61–3.4 and an SD of 0.64. From the analysis, it can be deduced that the current pharmaceutical receiving activity of the veterinary health facilities, which includes the availability of a pre-notification area for incoming pharmaceutical products, procedures for the cross-checking and identification of the documents and products received, procedures for the notification of discrepancies to the suppliers for the returning and receiving of products, and the safety of the receiving space for the movement of products handling equipment, is moderately satisfied. The SD of 0.64 indicates that there were no extremes in respondents' positive or negative scores. As stated in the literature, receiving activities should get strict attention, as they make up roughly 10% of operating expenditures in any distribution center. However, the qualitative result of this study indicated that governmental bodies did not pay attention to the sector, especially for the fulfillment of infrastructure and storage premises ( 56 ).

Regarding the storing activities, the majority of respondents “disagreed” with their facility's performance of pharmaceutical storing operations, with a computed mean value of 2.53 that falls within the range of 1.81–2.60 and an SD of 0.89. This indicated the current pharmaceutical storage activity of the veterinary health facilities, which includes the availability of adequate storage areas to store the inspected products, the arrangement of drugs in the storage area as clearly identified with their categories, the availability of clearly recorded and traceable locations for storing products, and the fact that products are stored according to the manufacturer's storage specifications at all times, were unsatisfactory. The standard deviation of 0.89 indicates that there are no extremes in respondents' positive or negative scores. Researchers suggest that storing activities cost ~15% of warehouse operating costs ( 56 ). However, the qualitative findings of this study show that the storage activities of the facilities are not based on standards.

Regarding issuing activities, this study found that the majority of respondents were “in agreement” with a computed mean value of 4.07 that falls within the range of 3.4–4.2 and an SD of 0.44 for the performance of the facility's pharmaceutical issuing activities. From the analysis, it can be deduced that the pharmaceutical issuing activity of the veterinary health facilities in terms of products is picked based on the printed order picking format; products are picked in the order of the FEFO principle; records are updated when goods are issued from their storage areas; and the availability of enough areas for product packing, labeling, and dispatching is “satisfactory.” An SD of 0.44 indicates that the majority of respondents had similar reflections.

Human and material resources management practices

In pharmaceutical warehouse management practices, the personnel who work there and handle the materials have a direct role in managing the stock and all other warehouse operations ( 53 ). Researchers also suggested that effective pharmaceutical warehouse management is determined by the professional's qualification level, training and capacity building, and the accessibility of sufficient material and equipment (which are crucial because they guard against future harm to the workers and the warehouse) ( 11 , 54 ). The present study found that the availability of qualified and sufficient numbers of staff to manage warehouse operations and the availability of equipment and materials used for facilitating warehouse activities at the surveyed facilities were unsatisfactory, with a computed mean value of 2.40 falling within the range of 1.81–2.60, and an SD of 0.61. This finding establishes that the human and material resource management practices, which comprise the availability of a sufficient number of staff, awareness of staff on veterinary pharmaceutical warehouse management principles, availability of job descriptions for their respected duties, availability of sufficient materials and equipment like personal protective materials, store safety materials like fire extinguishers, ladders and pallet jacks, hand trucks, etc. to facilitate warehouse activities, and the delivery of timely maintenance support and replacement for the equipment in the warehouses when it is not working satisfactorily. The SD of 0.61 indicates that the majority of respondents had similar reflections.

Evidence also suggests that the level of exposure to pharmaceutical warehouse management practices and other related supply chain activities is different for different professionals. Medicine storage is one of the most important responsibilities that can be best handled by a pharmacist ( 57 ). Accordingly, the veterinary pharmacy professional has direct exposure to the related tasks compared to non-pharmacy animal health professionals. The present study found that most of the veterinary pharmaceutical warehouse management activities were performed by non-pharmacy professionals (71, 94.7%); on the other hand, the involvement of veterinary pharmacy professionals in pharmaceutical warehouse practice was only about 4 (5.3%). This is in line with the result of the study conducted on the assessment of inventory and store management practices of pharmaceuticals in public health centers and hospitals in Dessie Town, Ethiopia, which showed only two institutions (20%) completely controlled and operated their stores by pharmacists ( 48 ). However, this was lower than the result of the study conducted in India, where 60% of the health centers were operated by pharmacists ( 58 ). This observed difference could be due to the difference in the study population.

The observed difference might also be due to the availability of educated human resources and the absence of job descriptions. As stated in the literature, a review conducted on veterinary drug management, handling, utilization, resistance, and side effects confirmed that low educational levels and a lack of graduates in veterinary medicine who are aware of pharmaceutical warehouse management were the major problems in veterinary drug handling and management ( 14 ). The absence of a job description for veterinary pharmacy professionals was supported by a qualitative result, as key informants confirmed that there is no job description used for district drug dispensaries and drug store personnel.

Different scholars suggest that employing qualified warehouse personnel and providing necessary training is crucial in improving the productivity of warehousing operations ( 10 , 53 ). However, the present study revealed that more than half (57.3%) of the study participants had not received on-the-job training in veterinary drug management, handling, and other related activities. The findings are somewhat consistent with a study carried out on the veterinary drug supply chain in Uganda, which found that nearly 90% of drug retailers and veterinary drug practitioners did not receive specialized training in veterinary medicine handling and storage management. The findings are also consistent with another study conducted on the assessment of veterinary drug handling, management, and supply chain in Ethiopia's Afar Pastoral Region, which found that ~63.9% of respondents lacked sufficient knowledge on safe handling and management of veterinary drugs ( 21 , 24 ).

Strengths and limitations of the study

This study was the first in the country to assess the status of veterinary pharmaceutical warehouse management practices and will serve as a baseline for future research. Furthermore, the strength of this study was that it used both quantitative and qualitative approaches in assessing existing practices and the challenges of veterinary supplies warehouse management practices. However, due to insufficient previous studies conducted in veterinary pharmaceutical warehouse management practices in the study area and abroad, it was difficult to compare the results with those conducted in similar settings. Besides, due to time constraints, geographic distance, and financial limitations, this study did not cover all the facilities available in the study area.

Conclusion and recommendations

This study revealed that most of the surveyed facilities in the study area did not prioritize the management practices of veterinary supply warehouses. The study specifically found that the warehouse management practices at government district veterinary clinics and private drug wholesalers were unsatisfactory. This was evident because 23 (59.5%) facilities lacked standard operating procedures for warehouse activities, and no veterinary health facilities utilized bin cards and system software. Furthermore, the majority of facilities (32; 86.5%) did not have guidelines for drug disposal and failed to dispose of expired drugs on time. The storage conditions at government district veterinary clinics were poor, with 48.3% meeting below the minimum requirements for good storage conditions. In contrast, the storage conditions at private veterinary drug wholesalers were good, with 86.25% meeting the necessary standards.

On warehousing activities, the storing activities and human and material resource management practices of the surveyed facilities were not satisfactory. Key informants highlighted several challenges that hindered effective veterinary supplies warehouse management, such as inadequate infrastructure, lack of qualified and trained staff, insufficient storage safety and security equipment, issues with pharmaceutical product availability and affordability, weak regulatory framework, and budget constraints at the facility level. Overall, the study found that warehouse management practices in the surveyed facilities were significantly poor. To enhance the management practices of veterinary pharmaceutical warehouses, various entities, including the District Veterinary Health Service offices, zonal Agricultural and Veterinary Health Services offices, Amhara Region Livestock and Fishery Resource Development office, Veterinary Drug and Feed Administration Control Authority, and veterinary professionals must make concerted efforts.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Ethics statement

The study was conducted after obtaining approval and clearance letters from the University of Gondar, School of Pharmacy with Ref No. S/A/P/67/2014. To collect data from the selected facilities letters of permission were obtained from the Amhara region livestock and fishery resource development office and the Ethiopian veterinary drug and feed control administration authority, Amhara regional branch. The participants were asked and provided their oral informed consent to participate in this study.

Author contributions

AW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing—original draft. TS: Project administration, Supervision, Visualization, Writing—review & editing. AE: Project administration, Supervision, Visualization, Writing—review & editing. YT: Data curation, Validation, Visualizations, Supervision, Writing—review & editing. BW: Project administration, Supervision, Visualization, Writing—review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We would like to acknowledge the contribution made by the University of Gondar, College of Veterinary Medicine and Animal Sciences and the School of Pharmacy for sponsoring study leave and paying student support fees. The authors also express gratitude to the study participants who work in the district veterinary clinics and private veterinary pharmaceutical wholesalers for their appreciated assistance during the period of data collection. My great thanks are also extended to the social and administrative pharmacy staff, the school of pharmacy, the college of medicine, and the college of veterinary medicine and animal science at the University of Gondar for their professional support.

Conflict of interest

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

The reviewer AB declared a shared affiliation with the authors to the handling editor at the time of review.

Publisher's note

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

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2024.1336660/full#supplementary-material

Abbreviations

AHS, Animal health service; AMR, Antimicrobial resistance; EAA, Ethiopian Agricultural Authority; EMA, European medicine agency; FEFO, first expire first out first in principles; WM, warehouse management; PFSA, pharmaceutical fund and supply agency; SCM, supply chain management; SOP, Standard operational procedures; VDFACA, Veterinary drug and feed administration control authority; VP, Veterinary pharmaceuticals.

1. Entity M. Livestock Systems: Overview and Areas of Inquiry. Gainesville, FL: Feed the Future Innovation Lab for Livestock Systems (2021).

Google Scholar

2. Zeryehun T, Ambachew A, Gulte H, Sisay MG. Proceeding of Research for Enhancing Pastoralist's Livelihood Through Resilience and Market Expansion . (2017). p. 1.

3. Herrero M, Grace D, Njuki J, Johnson N, Enahoro D, Silvestri S, et al. The roles of livestock in developing countries. J Cambridge UnivPress. (2013) 7:3–18. doi: 10.1017/S1751731112001954

PubMed Abstract | Crossref Full Text | Google Scholar

4. Elelu N. Assessment of veterinary drug retail outlets in two rural areas of Kwara state, north-central Nigeria. Sokoto J Vet Sci. (2017) 15:54–61. doi: 10.4314/sokjvs.v15i3.8

Crossref Full Text | Google Scholar

5. Megerrsa SA, Ashine TB. Study on veterinary antibiotic drugs handling and utilization in and around Holeta. J Vet Med Anim Health. (2018) 10:55–9. doi: 10.5897/JVMAH2017.0603

6. Duguma B, Abera B, Muktar Y, Adugna S, Kefyalew H, Mengistu S. Knowledge, attitude and practices about quality and management of anthelmintic drugs in Adea Berga District, Central Ethiopia. J Vet Sci Technol. (2018) 9:1–6. doi: 10.4172/2157-7579.1000540

7. Beyene T, Assefa S, Ayana D, Jibat T, Tadesse F, Nigussie D, et al. Assessment of rational veterinary drugs use in livestock at Adama district veterinary clinic, central Ethiopia. J Vet Sci Technol. (2016) 7:2–7. doi: 10.4172/2157-7579.1000319

8. British Veterinary Association. Code of Practice on the Responsible use of Animal Medicines on the Farm . London: British Veterinary Association (2014). p. 1–24.

9. Melaku Achenef SK, Zeru H. Rational Use of Veterinary Drugs and Vaccines . Addis Ababa: Veterinary Drug and Animal Feed Administration Control Authority (VDFACA) in collaboration with University of Gondar, Faculty of Veterinary Medicine (2016). p. 22–6.

10. Gwynne R. Warehouse Management. A Complete Guide to Improving Efficiency and Minimizing Costs in the Modern Warehouse . London; New York, NY: Kogan Page Publishers (2014). p. 5–76.

11. Yimenu DK, Nigussie AM, Workineh TY. Assessment of pharmaceutical warehouse management practice the case of private pharmaceutical wholesalers in Ethiopia. Int J Suppl Operat Manag. (2021) 8:314–27. doi: 10.22034/IJSOM.2021.3.5

12. Dixit A, Routroy S, Dubey SK. An efficient drug warehouse operations and application of 5S. AIP Conf Proc . (2019) 2200:020005. doi: 10.1063/1.5141175

13. Shah B, Khanzode V. A comprehensive review of warehouse operational issues. Int J Logist Syst Manag. (2017) 2017:2597. doi: 10.1504/IJLSM.2017.10002597

14. Mengesha A. A review on veterinary drug management, handling, utilization and its resistance side effects. Aust J Vet Sci Anim Husbandry. (2021) 8:1–21. doi: 10.26420/austinjvetscianimhusb.2021.1079

15. European Medicines Agency. Regulation of EU 2019/6 on veterinary medicinal products: Good Distribution Practices (GDP) for veterinary medicinal products, advice on implementing under Article 99. Sci Med Health . (2020) 2020:1–26.

16. Beyene T, Tesega B. Rational veterinary drug use: its significance in public health. J Vet Med Anim Health . (2014) 6:3028. doi: 10.5897/JVMAH2014.0332

17. Eagar H, Naidoo V. Veterinary antimicrobial stewardship in South Africa. Int Biol Rev . (2017) 1:7. doi: 10.5599/obp.15.7

18. Jaime GLM, Hobeika A, Figuié M. Access to veterinary drugs in sub-Saharan Africa: roadblocks and currents solutions. Front Vet Sci . (2022) 8:558973. doi: 10.3389/fvets.2021.558973

19. Frumence G, Mboera LE, Sindato C, Durrance-Bagale A, Jung A-S, Mshana SE, et al. Practices and challenges of veterinary paraprofessionals in regards to antimicrobial use and resistance in animals in Dar Es Salaam, Tanzania. Antibiotics . (2021) 10:2–9. doi: 10.3390/antibiotics10060733

20. Asresie A, Zemedu L. The contribution of livestock sector in Ethiopian economy. Rev Adv Life Sci Technol . (2015) 29.

21. Dione MM, Amia WC, Ejobi F, Ouma EA, Wieland B. Supply chain and delivery of antimicrobial drugs in smallholder livestock production systems in Uganda. Front Vet Sci. (2021) 8:1–12. doi: 10.3389/fvets.2021.611076

22. Zeru H. Veterinary medicines regulatory alignment between federal and regional regulatory bodies of Ethiopia. Pharmaceut Drug Regula Affair J . (2019) 2:114. doi: 10.23880/pdraj-16000114

23. Desta AH. Major constraints of veterinary services delivery system and its solution in pastoral areas of Ethiopia. Int J Afri Asian Stud. (2015) 12:5–11.

24. Desta AH. Veterinary drugs handling, management and supply chain assessment in Afar pastoral region of North East Ethiopia. Am J Biosci Bioeng. (2015) 3:143–7. doi: 10.11648/j.bio.20150306.11

25. Tefera B, Bacha B, Belew S, Raffaella R, Andualem T, Abegaz Z, et al. Study on identification, assay and organoleptic quality of veterinary medicines in Ethiopia. J Pharmaceut Policy Practice. (2022) 15:1–7. doi: 10.1186/s40545-022-00410-6

26. Nanjundeswaraswamy T, Shilpa D. Determination of sample size and sampling methods in applied research. Economics . (2021) 3:25–30. doi: 10.24874/PES03.01.003

27. Strauss A, Corbin J. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 3rd ed. New York, NY: SAGE Publications, Inc. (2008).

28. Pandey R, Verma MR. Samples allocation in different strata for impact evaluation of developmental programme. Economics . (2008) 26:103–12.

29. PROJECT UD. The Logistics Handbook: a Practical guide for the Supply Chain Management of Health Commodities. United States: USAID/DELIVER Project . 2nd ed. Arlington, VA: Task Order 1 (2011). p. 114–20.

30. Ayele T. Assessing Performances of Pharmaceutical Store Management in the Case of General Public Hospitals Under the Addis Ababa City Administration Health Bureau. Ethiopia. (2019).

31. Semu E. Inventory Management Practices for Pharmaceuticals Items at Health Facilities in Addis Ababa (2018).

32. Ide Y, Beddoe L. Challenging perspectives: reflexivity as a critical approach to qualitative social work research. Qualit Soc Work . (2023) 2023:14733250231173522. doi: 10.1177/14733250231173522

33. Braun V, Clarke V, Weate P. Using thematic analysis in sport and exercise research. In: Routledge Handbook of Qualitative Research in Sport and Exercise . London: Routledge (2016). p. 213–27.

34. Lohaka HO. Making a Grouped-Data Frequency Table: Development and Examination of the Iteration Algorithm . Athens, ON: Ohio University (2007).

35. Chakrabartty SN. Scoring and analysis of Likert scale: few approaches. J Knowl Manag Inform Technol. (2014) 1:31–44. Available online at: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194981215 .

36. Joshi A, Kale S, Chandel S, Pal DK. Likert scale: explored and explained. Br J Appl Sci Technol. (2015) 7:396–403. doi: 10.9734/BJAST/2015/14975

37. Semahegn A. Assessment of Health Commodities Inventory Management Practices and Challenges: The Case of Zewditu Memorial Hospital (Master thesis). Addis Ababa University, Addis Ababa, Ethiopia (2017).

38. Kassahun T. The Effect of Inventory Control on Healthcare Service Delivery at Felege Hiwot Referral Hospital (Master thesis). Bahir Dar University, Bahir Dar, Ethiopia (2020).

39. Hailu K. Effect of warehousing practices on organizational performance: a case study of my wish enterprise PLC, Addis Ababa, Ethiopia (2019).

40. PFSA. Pharmaceutical Warehouse Operation Management Training Manual . Addis ababa: The Ethiopian Pharmaceuticals Fund and Supply Agency (2015). p. 210.

41. Clarke V, Braun V, Hayfield N. Thematic analysis. Qualit Psychol. (2015) 3:222–48.

42. Solomon KHZ, Melaku A, Ayenew A, Tenaw A. Veterinary Pharmaceutical Management Manual . 1st ed. Addis Ababa: Veterinary Drug and Animal Feed Administration and Control Authority (VDFACA) of Ethiopia in collaboration with Food and Agriculture Organization of the United Nations (FAO/UN) (2020). p. 1–25.

43. Van den Berg JP, Zijm WH. Models for warehouse management: classification and examples. Int J Prod Econ. (1999) 59:520–5.

44. Telila Y. Pharmaceutical Store Management Practice in Public Health Facilities . North Shoa: Amhara Regional State (2018). p. 14–37.

45. Befekadu A, Cheneke W, Kebebe D, Gudeta T. Inventory management performance for laboratory commodities in public hospitals of Jimma zone, Southwest Ethiopia. J Pharmaceut Policy Practice . (2020) 1:2–11. doi: 10.1186/s40545-020-00251-1

46. Boche B, Temam S, Kebede O. Inventory management performance for laboratory commodities and their challenges in public health facilities of Gambella Regional State, Ethiopia. Heliyon . (2022) 2022:2–8. doi: 10.1016/j.heliyon.2022.e11357

47. Mogessie TH. Addis Ababa University College of Business and School of Commerce (Master thesis). Addis Ababa University, Addis Ababa, Ethiopia (2018).

PubMed Abstract | Google Scholar

48. Ewunetie MB, Mesfin, HK, Birhanu, DW. Assessment of Inventory and Store Management Practices of Pharmaceuticals in Public Health Centers and Hospitals of Dessie Town, Ethiopia . (2019). p. 2–8.

49. Tasho RP, Cho JY. Veterinary antibiotics in animal waste, its distribution in soil and uptake by plants: a review. Sci Tot Environ . (2016) 2016:366–76. doi: 10.1016/j.scitotenv.2016.04.140

50. Madende S. Knowledge, Attitudes and Practices on Safe Disposal of Pharmaceutical Waste Among Pharmacy, Medical and Dental Interns at Windhoek and Katutura Central Hospitals in Namibia. Johannesburg: University of Johannesburg (2020).

51. Mudzteba M. Assessment of Pharmaceutical Logistics System in Health Centers of Addis Ababa, Ethiopia (Master thesis), Addis Ababa University, Addis Ababa, Ethiopia (2014).

52. Kassie G, Mammo S. Assessment of pharmaceutical store management in Woreda Health Offices of West Hararghe Zone, Ethiopia. Int Res J Pharm. (2014) 5:642–5. doi: 10.7897/2230-8407.0508131

53. Boche B, Mulugeta T, Gudeta T. Assessment of inventory management practices at the Ethiopian pharmaceuticals supply agency, Addis Ababa, Ethiopia. Integr Pharm Res Practice. (2020) 9:175. doi: 10.2147/IPRP.S269421

54. Kidane AT. Assessment of Warehousing Practices: A Case of Finfine Furniture Factory S. Co . (Master thesis), Addis Ababa University, Addis Ababa, Ethiopia (2017).

55. Zawierucha K. Warehouse management and inventory management on the example of EKOPLON S.A. Business . (2018) 2018:518–30. doi: 10.29119/1641-3466.2018.128.38

56. Kembro JH, Norrman A, Eriksson E. Adapting warehouse operations and design to omni-channel logistics. A literature review and research agenda. Int J Phys Distribut Logist Manag. (2018) 48:890–6. doi: 10.1108/IJPDLM-01-2017-0052

57. Ababu A, Endashaw D, Fesseha H, Mathewos M. Antiprotozoal drug handling and management practices in Asella District, Central Oromia, Ethiopia. Vet Med Int. (2021) 2021:6648328. doi: 10.1155/2021/6648328

58. Kokilam MB, Joshi HG, Kamath VG. Assessment of public pharmaceutical supply management system at rural primary health centers in Udupi District, Karnataka. Ind J Pharm Practice. (2015) 8:149.

Keywords: veterinary pharmaceuticals, veterinary clinics, veterinary drug wholesalers, warehouse management practices, Amhara region, Ethiopia

Citation: Wondie Mekonen A, Sintayehu T, Endeshaw Woldeyohanins A, Tefera Mekasha Y and Weldegerima Atsbeha B (2024) Assessment of veterinary pharmaceutical warehouse management practices and its associated challenges in four selected zones and Bahir Dar city of Amhara regional state, Ethiopia. Front. Vet. Sci. 11:1336660. doi: 10.3389/fvets.2024.1336660

Received: 11 November 2023; Accepted: 09 April 2024; Published: 07 May 2024.

Reviewed by:

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

*Correspondence: Abibo Wondie Mekonen, abibowondie@gmail.com ; abibo.wondie@uog.edu.et