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Home » Non-probability Sampling – Types, Methods and Examples

Non-probability Sampling – Types, Methods and Examples

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Non-probability Sampling

Non-probability Sampling

Definition:

Non-probability sampling is a type of sampling method in which the probability of an individual or a group being selected from the population is not known. In other words, non-probability sampling is a method of sampling where the selection of participants is based on non-random criteria, such as convenience, availability, judgment, or quota.

Non-probability Sampling Methods

Non-probability Sampling Methods are as follows:

Convenience Sampling

This method involves selecting individuals or items that are easily accessible or convenient to the researcher. For example, a researcher conducting a study on college students may select participants from their own class or dormitory because they are convenient to access.

Snowball Sampling

This method involves selecting individuals who know other individuals who meet the criteria for the study. The researcher starts with a few participants and then asks them to refer others who may be interested in participating. This method is often used in studies where the population is difficult to access or identify.

Quota Sampling

This method involves selecting a sample that matches the characteristics of the population. The researcher sets quotas for each characteristic (such as age, gender, or occupation) and selects participants who fit into those quotas. This method is often used in market research studies.

Purposive Sampling

This method involves selecting individuals or items that meet specific criteria or have specific characteristics that the researcher is interested in studying. For example, a researcher studying the experiences of cancer survivors may purposively select individuals who have undergone chemotherapy.

Volunteer Sampling

This method involves selecting individuals who volunteer to participate in the study. This method is often used in studies where the population is difficult to access or identify.

How to Conduct Non-probability Sampling

To conduct a non-probability sampling, you should follow these general steps:

  • Identify the target population: Identify the population you want to study. This can be a specific group of people, a geographic location, or any other defined population.
  • Determine the sampling method: Choose the non-probability sampling method that is most appropriate for your study. Consider the advantages and disadvantages of each method and select the one that fits your research question and resources.
  • Determine the sample size: Determine the appropriate sample size based on your research question, the available resources, and the sampling method you choose.
  • Recruit participants : Recruit participants using the selected non-probability sampling method. For example, if you are using convenience sampling, you might approach people in a public place to participate in your study.
  • Collect data: Collect data from the selected participants using the appropriate research methods, such as surveys, interviews, or observations.
  • Analyze the data: Analyze the data collected from the sample to draw conclusions and make generalizations about the population.

Examples of Non-probability Sampling

  • Convenience Sampling: In this type of sampling, participants are chosen because they are easy to reach or are readily available. For example, a researcher may choose to survey the first 100 people who enter a shopping mall.
  • Quota Sampling : Quota sampling is a type of non-probability sampling in which participants are selected to ensure that the sample reflects the characteristics of the population in terms of certain traits. For example, if a researcher wants to conduct a study on the opinions of men and women about a certain product, they may select a sample that has an equal number of men and women.
  • Purposive Sampling: In this type of sampling, participants are selected based on specific criteria such as age, gender, occupation, or experience. For example, a researcher may choose to interview only CEOs of Fortune 500 companies to study their leadership style.
  • Snowball Sampling: Snowball sampling is a type of sampling in which the initial participants in a study are asked to refer others who they know that fit the criteria of the study. For example, a researcher may ask a person who has experienced homelessness to refer others they know who have experienced homelessness for a study on homelessness.
  • Judgmental Sampling : In judgmental sampling, the researcher selects participants based on their own judgment about who would be the most appropriate for the study. For example, a researcher may select participants for a study on the effects of a new cancer drug based on their experience with the disease and their likelihood of benefiting from the treatment.

Applications of Non-probability Sampling

Here are some applications of non-probability sampling:

  • Exploratory Studies: Non-probability sampling is commonly used in exploratory studies where the focus is on generating new ideas and insights rather than testing hypotheses. Exploratory studies often use a small sample size, and non-probability sampling is used to identify potential patterns or trends.
  • Pilot Studies: Non-probability sampling is also used in pilot studies, which are small-scale studies conducted to evaluate the feasibility and potential outcomes of a larger study. Pilot studies often use a convenience sample or purposive sampling to identify potential issues or areas of improvement before conducting the larger study.
  • Qualitative Research : Non-probability sampling is commonly used in qualitative research where the focus is on gaining an in-depth understanding of a particular phenomenon or context. Qualitative research often uses purposive sampling to identify participants who have the knowledge or experience needed to provide rich and detailed insights.
  • Rare Populations : Non-probability sampling is used in studies of rare populations, where it may be difficult to obtain a large enough sample using a random sampling method. Snowball sampling is often used in studies of rare populations to identify potential participants through referrals from existing participants.
  • Convenience Sampling : Non-probability sampling is also used in studies where the sample size is not a critical factor, and the focus is on convenience and efficiency. Convenience sampling is often used in market research, opinion polls, and customer satisfaction surveys.
  • Ethnographic Research: Non-probability sampling is commonly used in ethnographic research, which involves studying the social and cultural practices of a particular group or community. Ethnographic research often uses purposive sampling to identify participants who can provide insights into the cultural practices and beliefs of the group being studied.
  • Case Studies: Non-probability sampling is often used in case studies, which involve in-depth analysis of a single individual, organization, or event. Case studies often use purposive sampling to select the individual or organization that is most relevant to the study.
  • Action Research: Non-probability sampling is also used in action research, which involves developing solutions to practical problems in real-world settings. Action research often uses purposive sampling to identify participants who can provide input and feedback on the proposed solutions.
  • Behavioral Research: Non-probability sampling is used in behavioral research where the focus is on understanding human behavior, attitudes, and beliefs. Behavioral research often uses purposive sampling to identify participants who can provide insights into the behavior being studied.
  • Historical Research: Non-probability sampling is used in historical research, which involves studying events and phenomena that occurred in the past. Historical research often uses purposive sampling to identify participants who have knowledge or experience relevant to the historical event or phenomenon being studied.

Purpose of Non-probability Sampling

The main purpose of non-probability sampling is to obtain a sample that is more convenient and practical than a random sample, particularly in situations where a random sample is not feasible, practical, or affordable. Non-probability sampling methods are often used in exploratory research, qualitative research, or in situations where researchers want to study a specific group or population.

When to use Non-probability Sampling

Here are some situations where non-probability sampling may be appropriate:

  • Small or hard-to-reach populations: When the population of interest is small or difficult to access, non-probability sampling may be the only feasible option.
  • Exploratory research: Non-probability sampling may be used in exploratory research studies where the objective is to generate hypotheses or insights for further investigation.
  • Convenience sampling : This type of non-probability sampling is commonly used when the researcher selects the most convenient participants available, such as those who are nearby or easily accessible.
  • Expert or judgmental sampling: When the researcher is interested in studying a specific group of individuals with specialized knowledge or expertise, expert or judgmental sampling may be used.
  • Quota sampling: In quota sampling, the researcher identifies relevant characteristics of the population of interest and selects participants based on those characteristics in order to ensure a representative sample.

Characteristics of Non-probability Sampling

Here are some characteristics of non-probability sampling:

  • Non-random selection : In non-probability sampling, the selection of participants is non-random and based on subjective criteria, such as convenience, availability, or judgment.
  • Limited generalizability: Since non-probability sampling does not provide a representative sample of the population, the findings obtained from the sample may not be generalizable to the population as a whole.
  • Biased sample: Non-probability sampling can result in a biased sample, which means that the sample is not representative of the population, leading to inaccurate or misleading conclusions.
  • No sampling frame : Non-probability sampling does not require a sampling frame, which is a list of all the individuals or units in the population, making it easier and cheaper to conduct the sampling process.
  • Subjective judgment: Non-probability sampling requires subjective judgment in selecting participants, which can introduce researcher bias and reduce the objectivity of the research findings.
  • Less precision : Non-probability sampling generally provides less precision and accuracy compared to probability sampling methods, which may lead to lower statistical power and weaker inferential conclusions.

Advantages of Non-probability Sampling

Advantages of Non-probability Sampling are as follows:

  • Easy to conduct: Non-probability sampling is relatively easy to conduct as it does not require a sampling frame or complex statistical calculations.
  • Cost-effective: Non-probability sampling is usually less expensive than probability sampling methods as it does not require a large sample size or specialized equipment.
  • Convenient: Non-probability sampling can be convenient as it allows researchers to select participants based on their availability or willingness to participate.
  • More suitable for exploratory research : Non-probability sampling is more suitable for exploratory research where the focus is on generating new insights or hypotheses rather than making statistical inferences.
  • Better for studying rare phenomena: Non-probability sampling can be more effective for studying rare or hard-to-reach populations, such as drug users or people with specific medical conditions, where a probability sample may be difficult to obtain.
  • Allows for more diverse samples: Non-probability sampling can allow for a more diverse sample as it does not require strict randomization, allowing for the inclusion of participants who may not have been included in a probability sample.

Disadvantages of Non-probability Sampling

Disadvantages of Non-probability Sampling are as follows:

  • Limited generalizability : Non-probability sampling does not provide a representative sample of the population, so the findings obtained from the sample may not be generalizable to the population as a whole.
  • Difficulty in estimating sampling error : Non-probability sampling does not allow for the calculation of sampling error, which is the degree to which the sample estimates differ from the true population values.
  • Difficult to replicate: Non-probability sampling can be difficult to replicate as the selection of participants is based on subjective criteria, making it challenging to obtain similar results in subsequent studies.
  • Limited statistical power: Non-probability sampling generally provides less precision and accuracy compared to probability sampling methods, which may lead to lower statistical power and weaker inferential conclusions.

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Different Types of Sampling Techniques in Qualitative Research

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Key Takeaways:

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

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

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

In this Article:

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

Factors to Consider When Choosing a Sampling Technique

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

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

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4 Types of Sampling Techniques and Their Applications

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

1. Purposive Sampling

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

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

Purposive Sampling: Strengths and Weaknesses

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

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

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

2. Convenience Sampling  

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

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

Convenience Sampling: Strengths and Weaknesses

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

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

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

3. Snowball Sampling

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

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

Snowball Sampling: Strengths and Weaknesses

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

4. Theoretical Sampling

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

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

Theoretical Sampling: Strengths and Weaknesses

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

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

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

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

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

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

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

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

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

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

Tips for Selecting Participants

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

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

Ensuring Diversity in Samples

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

Maintaining Ethical Considerations

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

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

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

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

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 .

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Principles of Social Research Methodology pp 415–426 Cite as

Sampling Techniques for Qualitative Research

  • Heather Douglas 4  
  • First Online: 27 October 2022

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

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

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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.

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

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6.2 Nonprobability sampling

Learning objectives.

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

Qualitative researchers typically make sampling choices that enable them to achieve a deep understanding of whatever phenomenon it is that they are studying.  Sometimes quantitative researchers work with targeted or small samples. Qualitative research often employs a theoretical sampling strategy, where study sites, respondents, or cases are selected based on theoretical considerations such as whether they fit the phenomenon being studied (e.g., sustainable practices can only be studied in organizations that have implemented sustainable practices), whether they possess certain characteristics that make them uniquely suited for the study (e.g., a study of the drivers of firm innovations should include some firms that are high innovators and some that are low innovators, in order to draw contrast between these firms), and so forth.  In this section, we’ll examine the techniques that these researchers typically employ when sampling as well as the various types of samples that they are most likely to use in their work.

Nonprobability sampling

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

qualitative research non probability sampling

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

Types of nonprobability samples

There are several types of nonprobability samples that researchers use. These include purposive samples , snowball samples , quota samples , and convenience samples .

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

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

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

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

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

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

qualitative research non probability sampling

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.

Steven Kogan and colleagues (2011) used a type sampling similar to snowball sampling called respondent-driven sampling (Heckathorn, 2012).  They wished to study the sexual behaviors of non-college-bound African American young adults who lived in high-poverty rural areas. The researchers first relied on their own networks to identify study participants, but because members of the study’s target population were not easy to find, access to the networks of initial study participants was very important for identifying additional participants. Initial participants were given coupons to pass on to others they knew who qualified for the study. Participants were given an added incentive for referring eligible study participants; they received $50 for participating in the study and an additional $20 for each person they recruited who also participated in the study. Using this strategy, Kogan and colleagues succeeded in recruiting 292 study participants.

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

Key Takeaways

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

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  • For more information about the 1948 election and other historically significant dates related to measurement, see the PBS timeline of “ The first measured century ” ↵

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Non-Probability Sampling: Types, Examples, & Advantages

non-probability sampling

When we are going to do an investigation, and we need to collect data, we have to know the type of techniques we are going to use to be prepared. For this reason, there are two types of sampling : the random or probabilistic sample and the non-probabilistic one. In this case, we will talk in-depth about non-probability sampling. Keep reading!

What is non-probability sampling?

Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.

Non-probability sampling is a method in which not all population members have an equal chance of participating in the study, unlike probability sampling . Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations.

LEARN ABOUT: Survey Sampling

Types of non-probability sampling

Here are the types of non-probability sampling methods:

Types of non probability sampling

Convenience sampling

Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population. Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample.

Consecutive sampling

This non-probability sampling method is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive sampling technique gives the researcher a chance to work with many topics and fine-tune his/her research by collecting results that have vital insights.

Quota sampling

Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample , not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.

Judgmental or Purposive sampling

In the judgmental sampling method, researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity.

Snowball sampling

Snowball sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample.

LEARN MORE: Simple Random Sampling

Non-probability sampling examples

Here are three simple examples of non-probability sampling to understand the subject better.

  • An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample.
  • In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling.
  • Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study.

When to use non-probability sampling?

  • Use this type of sampling to indicate if a particular trait or characteristic exists in a population.
  • Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.
  • Researchers use it when they have limited time to conduct research or have budget constraints.
  • When the researcher needs to observe whether a particular issue needs in-depth analysis , he applies this method.
  • Use it when you do not intend to generate results that will generalize the entire population.
LEARN MORE: Population vs Sample

Advantages of non-probability sampling

Here are the advantages of using the non-probability technique

  • Non-probability sampling techniques are a more conducive and practical method for researchers deploying surveys in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers, however, if done correctly, it can produce similar if not the same quality of results and avoid sampling errors .
  • Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.

Select your respondents

Difference between non-probability sampling and probability sampling:

LEARN ABOUT:   Sampling Frame

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Non-probability sampling

Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. Collectively, these units form the sample that the researcher studies [see our article, Sampling: The basics , to learn more about terms such as unit , sample and population ]. A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods ), which is the cornerstone of probability sampling techniques . Whilst some researchers may view non-probabilit y sampling techniques as inferior to probability sampling techniques, there are strong theoretical and practical reasons for their use. This article discusses the principles of non-probability sampling and briefly sets out the types of non-probability sampling technique discussed in detail in other articles within this site. The article is divided into two sections: principles of non-probability sampling and types of non-probability sampling :

Principles of non-probability sampling

Types of non-probability sampling.

There are theoretical and practical reasons for using non-probability sampling. In addition, you need to decide whether non-probability sampling is appropriate based on the research strategy you have chosen to guide your dissertation.

Theoretical reasons

Non-probability sampling represents a valuable group of sampling techniques that can be used in research that follows qualitative , mixed methods , and even quantitative research designs .

Despite this, for researchers following a quantitative research design , non-probability sampling techniques can often be viewed as an inferior alternative to probability sampling techniques . Non-probability sampling techniques can often be viewed in such a way because units are not selected for inclusion in a sample based on random selection, unlike probability sampling techniques. As a result, researchers following a quantitative research design often feel that they are forced to use non-probability sampling techniques because of some inability to use probability sampling (e.g., the lack of access to a list of the population being studied). However, this is not the case for researchers following a qualitative research design .

When following a qualitative research design , non-probability sampling techniques, such as purposive sampling , can provide researchers with strong theoretical reasons for their choice of units (or cases) to be included in their sample. Rather than using probabilistic methods (i.e., random selection) to generate a sample, non-probability sampling requires researchers to use their subjective judgements , drawing on theory (i.e., the academic literature) and practice (i.e., the experience of the researcher and the evolutionary nature of the research process). Unlike probability sampling, the goal is not to achieve objectivity in the selection of samples, or necessarily attempt to make generalisations (i.e., statistical inferences) from the sample being studied to the wider population of interest. Instead, researchers following a qualitative research design tend to be interested in the intricacies of the sample being studied. Whilst making generalisations from the sample to the population under study may be desirable, it is more often a secondary consideration. Even whether this is desired, there are additional problems of bias and transferability (or validity ) [see the section on Research Quality for more information on research strategies, sampling techniques, and bias ].

Practical reasons

Non-probability sampling is often used because the procedures used to select units for inclusion in a sample are much easier , quicker and cheaper when compared with probability sampling. This is especially the case for convenience sampling . For students doing dissertations at the undergraduate and master's level, such practicalities often lead to the use of non-probability sampling techniques.

As mentioned, for researchers following a quantitative research design, non-probability sampling techniques can often be viewed as an inferior alternative to probability sampling techniques. However, where it is not possible to use probability sampling, non-probability sampling at least provides a viable alternative that can be used. As such, it ensures that research following a quantitative research design is not simply abandoned because (a) it cannot meet the criteria of probability sampling and/or (b) meeting such criteria is excessively costly or time consuming , such that it would not be sponsored. This could significantly diminish the potential for researchers to study certain types of population, such as those populations that are hidden or hard-to-reach (e.g., drug addicts, prostitutes), where a list of the population simply does not exist. Here, snowball sampling , a type of non-probability sampling technique, provides a solution.

Non-probability sampling can also be particularly useful in exploratory research where the aim is to find out if a problem or issue even exists in a quick and inexpensive way. After all, you may have a theory that such a problem or issue exists, but there is limited or no research that currently supports such a theory. Where your main desire is to find out is if such a problem or issue even exists, the potential sampling bias of certain non-probability sampling techniques can be used as a tool to help you. For example, you may choose to select only those units to be included in your sample that you feel will exhibit the problem or issue you are interested in finding. If this problem or issue does not exist even in your biased sample , it is unlikely to be present if you selected a relatively unbiased sample (whether using another non-probability sampling technique; or even a probability sampling technique). This would help you to avoid a potentially more time consuming and expensive piece of research looking into a potential problem or issue that actually doesn't exist. It may also be considered an ethical approach to finding out whether a problem or issue is worth examining in more depth, since fewer participants are subjected to a research project unnecessarily.

Deciding whether non-probability sampling is appropriate

If you are considering whether to use non-probability sampling, it is important to consider how your choice of research strategy will influence whether this is an appropriate decision. Even if you know that non-probability sampling fits with the research strategy guiding your dissertation, it is important to choose the appropriate type of non-probability sampling techniques. These non-probability sampling techniques are briefly set out in the next section.

There are five types of non-probability sampling technique that you may use when doing a dissertation at the undergraduate and master's level: quota sampling , convenience sampling , purposive sampling , self-selection sampling and snowball sampling .

Quota sampling

Convenience sampling

Purposive sampling

Self-selection sampling

Snowball sampling

To get a sense of what these five types of non-probability sampling technique are, imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10,000 students. These 10,000 students are our population ( N ). Each of the 10,000 students is known as a unit (although sometimes other terms are used to describe a unit; see Sampling: The basics ). In order to select a sample ( n ) of students from this population of 10,000 students, we could choose to use quota sampling , convenience sampling , purposive sampling , self-selection sampling and snowball sampling :

With proportional quota sampling , the aim is to end up with a sample where the strata ( groups ) being studied (e.g., males vs. females students) are proportional to the population being studied. If we were to examine the differences in male and female students, for example, the number of students from each group that we would include in the sample would be based on the proportion of male and female students amongst the 10,000 university students. To understand more about quota sampling, how to create a quota sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Quota sampling .

A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access . In our example of the 10,000 university students, if we were only interested in achieving a sample size of say 100 students, we may simply stand at one of the main entrances to campus, where it would be easy to invite the many students that pass by to take part in the research. To understand more about convenience sampling, how to create a convenience sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Convenience sampling .

Purposive sampling, also known as judgmental , selective or subjective sampling , reflects a group of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units (e.g., people, cases/organisations, events, pieces of data) that are to be studied. These purposive sampling techniques include maximum variation sampling , homogeneous sampling, typical case sampling , extreme (or deviant) case sampling , total population sampling and expert sampling . Each of these purposive sampling techniques has a specific goal, focusing on certain types of units, all for different reasons. The different purposive sampling techniques can either be used on their own or in combination with other purposive sampling techniques. To understand more about purposive sampling, the different types of purposive sampling, and the advantages and disadvantages of this non-probability sampling technique, see the article: Purposive sampling .

Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in research on their own accord . The key component is that research subjects (or organisations) volunteer to take part in the research rather than being approached by the researcher directly. To understand more about self-selection sampling, how to create a self-selection sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Self-selection sampling .

Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach . These include populations such as drug addicts, homeless people, individuals with AIDS/HIV, prostitutes, and so forth. To understand more about snowball sampling, how to create a snowball sample, and the advantages and disadvantages of this non-probability sampling technique, see the article: Snowball sampling .

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Sampling methods in Clinical Research; an Educational Review

Mohamed elfil.

1 Faculty of Medicine, Alexandria University, Egypt.

Ahmed Negida

2 Faculty of Medicine, Zagazig University, Egypt.

Clinical research usually involves patients with a certain disease or a condition. The generalizability of clinical research findings is based on multiple factors related to the internal and external validity of the research methods. The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we are explaining the different sampling methods in clinical research.

Introduction

In clinical research, we define the population as a group of people who share a common character or a condition, usually the disease. If we are conducting a study on patients with ischemic stroke, it will be difficult to include the whole population of ischemic stroke all over the world. It is difficult to locate the whole population everywhere and to have access to all the population. Therefore, the practical approach in clinical research is to include a part of this population, called “sample population”. The whole population is sometimes called “target population” while the sample population is called “study population. When doing a research study, we should consider the sample to be representative to the target population, as much as possible, with the least possible error and without substitution or incompleteness. The process of selecting a sample population from the target population is called the “sampling method”.

Sampling types

There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1 , 2 ] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee equal chances for each subject in the target population [ 2 , 3 ]. Samples which were selected using probability sampling methods are more representatives of the target population.

An external file that holds a picture, illustration, etc.
Object name is emerg-5-e52-g001.jpg

Sampling methods.

Probability sampling method

Simple random sampling

This method is used when the whole population is accessible and the investigators have a list of all subjects in this target population. The list of all subjects in this population is called the “sampling frame”. From this list, we draw a random sample using lottery method or using a computer generated random list [ 4 ].

Stratified random sampling

This method is a modification of the simple random sampling therefore, it requires the condition of sampling frame being available, as well. However, in this method, the whole population is divided into homogeneous strata or subgroups according a demographic factor (e.g. gender, age, religion, socio-economic level, education, or diagnosis etc.). Then, the researchers select draw a random sample from the different strata [ 3 , 4 ]. The advantages of this method are: (1) it allows researchers to obtain an effect size from each strata separately, as if it was a different study. Therefore, the between group differences become apparent, and (2) it allows obtaining samples from minority/under-represented populations. If the researchers used the simple random sampling, the minority population will remain underrepresented in the sample, as well. Simply, because the simple random method usually represents the whole target population. In such case, investigators can better use the stratified random sample to obtain adequate samples from all strata in the population.

Systematic random sampling (Interval sampling)

In this method, the investigators select subjects to be included in the sample based on a systematic rule, using a fixed interval. For example: If the rule is to include the last patient from every 5 patients. We will include patients with these numbers (5, 10, 15, 20, 25, ...etc.). In some situations, it is not necessary to have the sampling frame if there is a specific hospital or center which the patients are visiting regularly. In this case, the researcher can start randomly and then systemically chooses next patients using a fixed interval [ 4 ].

Cluster sampling (Multistage sampling)

It is used when creating a sampling frame is nearly impossible due to the large size of the population. In this method, the population is divided by geographic location into clusters. A list of all clusters is made and investigators draw a random number of clusters to be included. Then, they list all individuals within these clusters, and run another turn of random selection to get a final random sample exactly as simple random sampling. This method is called multistage because the selection passed with two stages: firstly, the selection of eligible clusters, then, the selection of sample from individuals of these clusters. An example for this, if we are conducting a research project on primary school students from Iran. It will be very difficult to get a list of all primary school students all over the country. In this case, a list of primary schools is made and the researcher randomly picks up a number of schools, then pick a random sample from the eligible schools [ 3 ].

Non-probability sampling method

Convenience sampling

Although it is a non-probability sampling method, it is the most applicable and widely used method in clinical research. In this method, the investigators enroll subjects according to their availability and accessibility. Therefore, this method is quick, inexpensive, and convenient. It is called convenient sampling as the researcher selects the sample elements according to their convenient accessibility and proximity [ 3 , 6 ]. For example: assume that we will perform a cohort study on Egyptian patients with Hepatitis C (HCV) virus. The convenience sample here will be confined to the accessible population for the research team. Accessible population are HCV patients attending in Zagazig University Hospital and Cairo University Hospitals. Therefore, within the study period, all patients attending these two hospitals and meet the eligibility criteria will be included in this study.

Judgmental sampling

In this method, the subjects are selected by the choice of the investigators. The researcher assumes specific characteristics for the sample (e.g. male/female ratio = 2/1) and therefore, they judge the sample to be suitable for representing the population. This method is widely criticized due to the likelihood of bias by investigator judgement [ 5 ].

Snow-ball sampling

This method is used when the population cannot be located in a specific place and therefore, it is different to access this population. In this method, the investigator asks each subject to give him access to his colleagues from the same population. This situation is common in social science research, for example, if we running a survey on street children, there will be no list with the homeless children and it will be difficult to locate this population in one place e.g. a school/hospital. Here, the investigators will deliver the survey to one child then, ask him to take them to his colleagues or deliver the surveys to them.

Conflict of interest:

qualitative research non probability sampling

7.2 Sampling in Qualitative Research

Learning objectives.

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

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

Nonprobability Sampling

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

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

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

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

Types of Nonprobability Samples

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

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

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

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

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

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

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

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

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

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

Table 7.1 Types of Nonprobability Samples

Key Takeaways

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

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What constitutes an appropriate sample depends upon the research question(s), the research objectives, the researcher’s understanding of the phenomenon under study (developed through the literature review), and practical constraints (Palys & Atchison, 2014). These considerations will influence whether the researcher chooses to employ probabilistic or non-probabilistic sampling techniques. Probabilistic sampling techniques are employed to generate a formal or statistically representative sample. This technique is utilized when the researcher has a well-defined population to draw a sample from, as is often the case in quantitative research. This fact enables the researcher to generalize back to the broader population (Palys & Atchison, 2014). On the other hand, a non-probabilistic sampling technique is the method of choice when the population is not created equal and some participants are more desirable in advancing the research project´s objectives. Non-probability sampling techniques are the best approach for qualitative research. Because the researcher seeks a strategically chosen sample, generalizability is more of a theoretical or conceptual issue, and it is not possible to generalize back to the population (Palys & Atchison, 2014).

Probabilistic sampling techniques

As previously mentioned, probability sampling refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is known. You might ask yourself why we should care about a study element’s likelihood of being selected for membership in a researcher’s sample. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample from which to collect data. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you must make sure that your sample doesn’t contain only women. That is a bit of an oversimplification, but the point with representativeness is that if your population varies in some way that is important to your study, your sample should contain the same sort of variation. While there is a formula to help you determine the sample size you will need to ensure representativeness, one of the easiest ways to do this is through an online sample size calculator.  The calculator will do the work for you and tell you the minimum number of samples you will need in order to meet the desired statistical limitations (see  https://www.calculator.net/sample-size-calculator.html )

Obtaining a representative sample is important in probability sampling because a key goal of studies that rely on probability samples is generalizability. In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s target population have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection. Random selection is a mathematical process that must meet two criteria. The first criterion is that chance governs the selection process. The second is that every sampling element has an equal probability of being selected (Palys & Atchison, 2014).

The core principal of probability sampling is random selection. If a researcher uses random selection techniques to draw a sample, he or she will be able to estimate how closely the sample represents the larger population from which it was drawn by estimating the sampling error.

Sampling error is the degree to which your sample deviates from the population’s characteristics. It is a statistical calculation of the difference between results from a sample and the actual parameters of a population. It is important to ensure that there is a minimum of sampling error (your sample needs to match the diversity of the population as closely as possible.) Sampling error comes from two main sources – systemic error and random error. Random error is due to chance, while systemic error means that there is some bias in the selection of the sample that makes particular individuals more likely to be selected than others. Here is an example to more fully explain the difference between a random and systemic error.

Example: Random and systemic errors

Consider the study of playground conditions for elementary school children. You would need a sampling frame (or list from which you sample) and select from that. Random sampling error would occur by chance and could not be controlled, but systemic error would be possible. Let us say that the list is designed in such a way that every 5th school is a private school. If you were to randomly sample every 5th school on the list, you would end up with a sample exclusively from private schools! Sampling error just means that an element of the population is more likely to be selected for the sample than another (in this case, the private schools are more likely to be sampled than the public schools).

Why is this discussion of error important?  The use of the right techniques for sampling gives researchers the best chances at minimizing sampling error, and thus the strongest ability to say their results are reflective of the population. Research is done to benefit society in some way, so it is important that research results reflect what we might expect to see in society. Sample size also impacts sampling error. Generally, the bigger the sample, the smaller the error. However, there is a point of diminishing returns where only small reductions in error occur for increases in size. Cost and resources usually also prohibit very large samples, so ultimately the sample size is dependent upon a variety of factors, of which sampling error is only one Probability sampling techniques.

There are a variety of probability samples that researchers may use. For our purposes, we will focus on four: simple random samples, systematic samples, stratified samples, and cluster samples (see Table 6.1 for a summary of these four techniques). Simple random samples are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample. To draw a simple random sample, a researcher starts with a list of every single member, or element, of his or her population of interest. This list is sometimes referred to as a sampling frame . Once that list has been created, the researcher numbers each element sequentially and then randomly selects the elements from which he or she will collect data. To randomly select elements, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks offer such tables as appendices to the text. Perhaps a more accessible source is one of the many free random number generators available on the Internet. A good online source is the website Stat Trek ( https://stattrek.com/ ), which contains a random number generator that you can use to create a random number table of whatever size you might need.

As you might have guessed, drawing a simple random sample can be quite tedious. S ystematic sampling techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must be able to produce a list of every one of your population elements. Once you have done that, to draw a systematic sample you would simply select every kth element on your list. But what is “k”, and where on the list of population elements does one begin the selection process? The symbol “k” is your selection interval or the distance between the elements you select for inclusion in your study. To begin the selection process, you would need to figure out how many elements you wish to include in your sample.

Let us say you want to interview 25 students from the Law program at your college or university. You do some research and discover that there are 150 students currently registered in the program. In this case, your selection interval, or k, is 6. To arrive at 6, simply divide the total number of population elements by your desired sample size. To determine where on your list of population elements to begin selecting the names of the 25 students you will interview, select a random number between 1 and k, and begin there. If we randomly select 3 as our starting point, we would begin by selecting the third student on the list and then select every sixth student from there.

There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. This is sometimes referred to as the problem of periodicity. Periodicity refers to the tendency for a pattern to occur at regular intervals. For example, suppose you want to observe how people use the outdoor public spaces in your city or town and you need to complete your observations within 28 days. During this time, you wish to conduct four observations on randomly chosen days. To determine which days you will conduct your observations, you will need to determine a selection interval. As you will recall from the preceding paragraphs, to do so you must divide your population size – in this case 28 days – by your desired sample size, in this case 4 days. This formula leads you to a selection interval of 7. If you randomly select 2 as your starting point and select every seventh day after that, you will wind up with a total of 4 days on which to conduct your observations. But what happens is that you are now observing on the second day of the week, being Tuesdays. As you have probably figured out, that is not such a good plan if you really wish to understand how public spaces in your city or town are used. Weekend use probably differs from weekday use, and that use may even vary during the week.

In cases such as this, where the sampling frame is cyclical, it would be better to use a stratified sampling technique . In stratified sampling, a researcher will divide the study population into relevant subgroups and then draw a sample from each subgroup. In this example, you might wish to first divide your sampling frame into two lists: weekend days and weekdays. Once you have your two lists, you can then apply either simple random or systematic sampling techniques to each subgroup.

Stratified sampling is a good technique to use when, as in the example, a subgroup of interest makes up a relatively small proportion of the overall sample. In the example of a study of use of public space in your city or town, you want to be sure to include weekdays and weekends in your sample. However, because weekends make up less than a third of an entire week, there is a chance that a simple random or systematic strategy would not yield sufficient weekend observation days. As you might imagine, stratified sampling is even more useful in cases where a subgroup makes up an even smaller proportion of the study population, say, for example, if you want to be sure to include both male and female perspectives in a study, but males make up only a small percentage of the population. There is a chance that simple random or systematic sampling strategy might not yield any male participants, but by using stratified sampling, you could ensure that your sample contained the proportion of males that is reflective of the larger population.  Let us look at another example to help clarify things.

Example #1 Choosing a sampling technique

Suppose a researcher wanted to talk to police officers in Canada about their views on illegal drug use in the general population. A researcher could find a list of all Canadian police officers (a sampling frame) and do a simple random sample or a systematic sample with random start from that list. But what if the researcher wanted to ensure that female and male officers were included in the same proportions they are in the population of officers? Or if they wanted to ensure that urban and rural officers are represented as they are in the population of police? In these cases, stratified random sampling might be more appropriate. If the goal is to have the subgroups reflect the proportions in the population then proportional stratification should be used. With proportional stratification, the sample size of each subgroup is proportionate to the population size of the group. In other words, each subgroup has the same sampling fraction. The sampling fraction is the proportion of the population that the researcher wants included in the sample. It is equal to the sample size, divided by the population size (n/N) (see Palys & Atchison, 2014).

However, if the researcher wants to be able to compare male and female officers or rural and urban officers (or a more complicated concept: male and female officers within the rural and urban areas), a disproportional stratification may be used instead to ensure that the researcher has enough members of the subgroups to allow between group comparisons. With a disproportional sample, the size of the each sample subgroup does not need to be proportionate to the population size of the group. In other words, two or more strata will have different sampling fractions (see Palys & Atchison, 2014).

Up to this point in our discussion of probability samples, we have assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let us say, for example, that you wish to conduct a study of bullying in high schools across Canada. Just imagine trying to create a list of every single high school student in the country. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.  Here is an example of when a cluster sampling technique would be suitable:

Example #2 – Cluster sampling

Perhaps you are interested in the workplace experiences of college instructors. Chances are good that obtaining a list of all instructors that work for Canadian colleges would be rather difficult. You would be more likely, without too much hassle, to come up with a list of all colleges in Canada. Consequently, you could draw a random sample of Canadian colleges (your cluster) and then draw another random sample of elements (in this case, instructors) from within the colleges you initially selected. Cluster sampling works in stages. In this example we sampled in two stages. As you might have guessed, sampling in multiple stages does introduce the possibility of greater error (each stage is subjected to its own sampling error), but it is nevertheless a highly efficient method.

Now suppose colleges across the country were not willing to share their instructor lists? How might you sample then? Is it important that the instructors in your study are representative of all instructors? What happens if you need a representative sample, but you do not have a sampling frame? In these cases, multi-stage cluster sampling may be appropriate. This complex form of cluster sampling involves dividing the population into groups (or clusters ). The researcher chooses one or more clusters at random and samples everyone within the chosen cluster (see Palys & Atchison, 2014).

Table 7.1 Four Types of Probability Samples

Nonprobability Sampling Techniques.

Nonprobability sampling refers to sampling techniques for which a person’s (or event’s or researcher’s focus) likelihood of being selected for membership in the sample is unknown. Because we do not know the likelihood of selection, we do not know whether or not a nonprobability sample represents a larger population. Representing the population is not the goal with nonprobability samples, however 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. The following subsection, “Types of Nonprobability Samples,” examines more closely the process of selecting research elements when drawing a nonprobability sample. But first, let us consider why a researcher might choose to use a nonprobability sample.

One instance might be at the design stage of a research project. For example, if you are conducting survey research, you may want to administer the survey to a few people who seem to resemble the people you are interested in studying in order to help work out kinks in the survey. You might also use a nonprobability sample at the early stages of a research project if you are conducting a pilot study or exploratory research. 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, nomothetic1 understanding. Evaluation researchers whose aim is to describe some very specific small group might use nonprobability sampling techniques. Researchers interested in contributing to our theoretical understanding of a phenomenon might also collect data from nonprobability samples. Researchers interested in contributing to social theories, by either expanding on them, modifying them, or poking holes in their propositions, might use nonprobability sampling techniques to seek out cases that seem anomalous in order to understand how theories can be improved.

In sum, there are many instances in which the use of nonprobability samples makes sense. The next subsection will examine several specific types of nonprobability samples.

Nonprobability sampling techniques

Researchers use several types of nonprobability samples, including: 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; because they are both nonprobability methods, we include them in this section of the chapter.

To draw a purposive sample, researchers begin with specific perspectives that they wish to examine in mind, and then seek out research participants who cover that full range of perspectives. For example, if you are studying students’ level of satisfaction with their college or university program of study, you must include students from all programs, males and females, students of different ages, students who are working and those who are not, students who are studying online and those who are taking classes face-to-face, as well as past and present. 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.

Qualitative researchers sometimes rely on snowball sampling techniques to identify study participants. In this case, a researcher might know of one or two people he or she would like to include in the 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 behaviour. 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.

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. While quota sampling offers the strength of helping the researcher account for potentially relevant variation across study elements, we must remember that such a strategy does not yield statistically representative findings. And while this is important to note, it is also often the case that we do not really care about a statistically representative sample, because we are only interested in a specific case.

Let us go back to a previous example of student satisfaction with their college or university course of study, to look at an example of how a quota sampling approach would work in such a study.

Imagine you want to understand how student satisfaction varies across two types programs: the Emergency Services Management (ESM) degree program and the ESM diploma program. Perhaps you have the time and resources to interview 40 ESM students. Since you are interested in comparing the degree and the diploma program, you decide to interview 20 students from each program.  In your review of literature on the topic before you began the study, you learned that degree and diploma experiences can vary by age of the students. Consequently, you decide on four important subgroup: males who are 29 years of age or younger, females who are 29 years of age or younger, males who are 30 years of age or older, and females who are thirty years of age or older. Your findings would not be representative of all students who enroll in degree or diploma programs at the college, or at other institutions; however, this is irrelevant to your purposes since you are solely interested in finding out about the satisfaction level of ESM students who are enrolled in either the ESM degree or diploma program.

Finally, convenience sampling is another nonprobability sampling strategy that is employed by both qualitative and quantitative researchers. To draw a convenience sample, a researcher simply collects data from those people or other relevant elements to which 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 have ever seen brief interviews of people on the street on the news, you have 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 sampling.

The following table provides a summary of the main differences between probability and non-probability sampling.

You will recall in Section 6.2 we discussed random assignment, which is different than random sampling.  The following matrix will help differentiate the two.

qualitative research non probability sampling

Adapted from Cetinkaya-Rundel, M. (n.d.).  Random sampling vs. assignment. Retrieved ffrom https://www2.stat.duke.edu/courses/Fall12/sta101.001/resources/lecturettes/random_sample_assignment.pdf

A Word of Caution about Sampling: Questions to Ask about Samples

We read and hear about research results so often that we might overlook the need to ask important questions about where research participants come from and how they are identified for inclusion in a research project. It is easy to focus only on findings when we are busy and when the really interesting stuff is in a study’s conclusion, not its procedures. Now that you have some familiarity with the variety of procedures for selecting study participants, you are equipped to ask some very important questions about the findings you read, and to be a more responsible consumer of research.

Research Methods, Data Collection and Ethics Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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  • What Is Non-Probability Sampling? | Types & Examples

What Is Non-Probability Sampling? | Types & Examples

Published on 21 July 2022 by Kassiani Nikolopoulou . Revised on 10 October 2022.

Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question.

Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify. For example, visitors to a website that doesn’t require users to create an account could form part of a non-probability sample.

  • In non-probability sampling , each unit in your target population does not have an equal chance of being included. Here, you can form your sample using other considerations, such as convenience or a particular characteristic.
  • In probability sampling , each unit in your target population must have an equal chance of selection. This minimises the risk of selection bias .

Table of contents

Types of non-probability sampling, non-probability sampling examples, probability vs. non-probability sampling, advantages and disadvantages of non-probability sampling, frequently asked questions about non-probability sampling.

There are five common types of non-probability sampling:

Convenience sampling

Quota sampling, self-selection (volunteer) sampling, snowball sampling, purposive (judgmental) sampling.

Convenience sampling is primarily determined by convenience to the researcher.

This can include factors like:

  • Ease of access
  • Geographical proximity
  • Existing contact within the population of interest

Convenience samples are sometimes called “accidental samples,” because participants can be selected for the sample simply because they happen to be nearby when the researcher is conducting the data collection .

In quota sampling , you select a predetermined number or proportion of units, called a quota. Your quota should comprise subgroups with specific characteristics (e.g., individuals, cases, or organisations) and should be selected in a non-random manner.

Your subgroups, called strata , should be mutually exclusive. Your estimation can be based on previous studies or on other existing data, if there are any. This helps you determine how many units should be chosen from each subgroup. In the data collection phase, you continue to recruit units until you reach your quota.

There are two types of quota sampling:

  • Proportional quota sampling is used when the size of the population is known. This allows you to determine the quota of individuals that you need to include in your sample in order to be representative of your population.
  • Non-proportional quota sampling is used when the size of the population is unknown. Here, it’s up to you to determine the quota of individuals that you are going to include in your sample in advance.

Note that quota sampling may sound similar to stratified sampling , a probability sampling method where you divide your population into subgroups that share a common characteristic.

The key difference here is that in stratified sampling, you take a random sample from each subgroup, while in quota sampling, the sample selection is non-random, usually via convenience sampling. In other words, who is included in the sample is left up to the subjective judgment of the researcher.

You stand at a convenient location, such as a busy shopping street, and randomly select people to talk to who appear to satisfy the age criterion. Once you stop them, you must first determine whether they do indeed fit the criteria of belonging to the predetermined age range and owning or renting a property in the suburb.

Self-selection sampling (also called volunteer sampling) relies on participants who voluntarily agree to be part of your research. This is common for samples that need people who meet specific criteria, as is often the case for medical or psychological research.

In self-selection sampling, volunteers are usually invited to participate through advertisements asking those who meet the requirements to sign up. Volunteers are recruited until a predetermined sample size is reached.

Self-selection or volunteer sampling involves two steps:

  • Publicizing your need for subjects
  • Checking the suitability of each subject and either inviting or rejecting them

Keep in mind that not all people who apply will be eligible for your research. There is a high chance that many applicants will not fully read or understand what your study is about, or may possess disqualifying factors. It’s important to double-check eligibility carefully before inviting any volunteers to form part of your sample.

Snowball sampling is used when the population you want to research is hard to reach, or there is no existing database or other sampling frame to help you find them. Research about socially marginalised groups such as drug addicts, homeless people, or sex workers often uses snowball sampling.

To conduct a snowball sample, you start by finding one person who is willing to participate in your research. You then ask them to introduce you to others.

Alternatively, your research may involve finding people who use a certain product or have experience in the area you are interested in. In these cases, you can also use networks of people to gain access to your population of interest.

In this way, the process of snowball sampling begins. You started by attending the meeting, where you met someone who could then put you in touch with others in the group.

Purposive sampling is a blanket term for several sampling techniques that choose participants deliberately due to qualities they possess. It is also called judgmental sampling, because it relies on the judgment of the researcher to select the units (e.g., people, cases, or organizations studied).

Purposive sampling is common in qualitative and mixed methods research designs, especially when considering specific issues with unique cases.

Common purposive sampling techniques include:

  • Maximum variation (heterogeneous) sampling

Homogeneous sampling

Typical case sampling.

  • Extreme (or deviant) case sampling

Critical case sampling

Expert sampling.

These can either be used on their own or in combination with other purposive sampling techniques.

Maximum variation sampling

The idea behind maximum variation sampling is to look at a subject from all possible angles in order to achieve greater understanding. Also known as heterogeneous sampling, it involves selecting candidates across a broad spectrum relating to the topic of study. This helps you capture a wide range of perspectives and identifies common themes evident across the sample.

Homogeneous sampling , unlike maximum variation sampling, aims to achieve a sample whose units share characteristics, such as a group of people that are similar in terms of age, culture, or job. The idea here is to focus on this similarity, investigating how it relates to the topic you are researching.

A typical case sample is composed of people who can be regarded as “typical” for a community or phenomenon. A typical case sample allows you to develop a profile of what would generally be agreed as being “average” or “normal.”

Typical case samples are often used when large communities or complex problems are investigated. In this way, you can gain an understanding in a relatively short time, even if you are not familiar with what’s going on yourself.

Note that the purpose of typical case sampling is to describe and illustrate what is typical to those unfamiliar with the setting or situation. The purpose is not to make generalised statements about the experiences of all participants. In other words, typical case sampling allows you to compare samples, not generalise samples to populations.

Extreme (deviant) case sampling

Extreme (or deviant) case sampling uses extreme cases of a particular phenomenon ( outliers ). This can mean remarkable failures, successes, or crises, as well as any event, organisation, or individual that appears to be the “exception to the rule.” Extreme case sampling is most often used when researchers are developing best-practice guidelines.

Note that extreme case sampling usually occurs in combination with other sampling strategies. The process of identifying extreme or deviant cases usually occurs after some portion of data collection and analysis has already been completed.

Critical case sampling is used where a single case (or a small number of cases) can be critical or decisive in explaining the phenomenon of interest. It is often used in exploratory research , or in research with limited resources.

There are a few cues that can help show you whether or not a case is critical, such as:

  • “If it happens here, it will happen anywhere”
  • “If that group is having problems, then all groups are having problems”

It is critical to ensure that your cases fit these criteria prior to proceeding with this sampling method.

Expert sampling involves selecting a sample based on demonstrable experience, knowledge, or expertise of participants. This expertise may be a good way to compensate for a lack of observational evidence or to gather information during the exploratory phase of your research.

Alternatively, your research may be focused on individuals who possess exactly this expertise, similar to ethnographic research .

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There are a few methods you can use to draw a non-probability sample, such as:

Social media

River sampling, street research.

Suppose you are researching the motivations of digital nomads (young professionals working solely in an online environment). You are curious what led them to adopt this lifestyle.

Since your population of interest is located all over the globe, it clearly isn’t feasible to conduct your study in person. Instead, you decide to use social media, finding your participants through snowball sampling.

You start by identifying social media sites that cater to digital nomads, such as Facebook groups, blogs, or freelance job sites. You ask the administrators for permission to post a call for participants with information about your research, encouraging readers to share the call with peers.

You are part of a research group investigating online behavior and cyberbullying, in particular among users aged 15 to 30 in your state. You are collecting data in two ways, using an online survey.

You first place a link to your survey in an online news article about cyber-hate published by local media. Second, you create an advertising campaign through social media, targeted at users aged 15 to 30 and linking back to your survey. To entice users to participate, a prize draw (movie tickets) is mentioned in all ads. The survey and the campaign are active for the same length of time.

These two data collection methods are river samples. The name refers to the idea of researchers dipping into the traffic flow of a website, catching some of the users floating by.

You are interested in the level of knowledge about myocardial infarction symptoms among the general population.

For a week, you stand in a shopping mall and stop passersby, asking them whether they would be willing to take part in your research. To try to allow as broad a range of respondents as possible to be included, you interview equal numbers of people from Monday to Friday during working hours.

Sampling methods can be broadly divided into two types:

  • Probability sampling : When the sample is drawn in such a way that each unit in the population has an equal chance of selection
  • Non-probability sampling : When you select the units for your sample with other considerations in mind, such as convenience or geographical proximity

Probability sampling

For many types of analysis, it is important that the statistical analysis is conducted from a random probability sample from the population of interest. For the sample to qualify as random, each unit must have an equal chance (i.e., equal probability) of being selected.

When you use a random selection method (e.g., a drawing) and ensure that you have a sufficiently large sample, your sample is more likely to be representative, and the results generalisable.

Non-probability sampling

Non-probability sampling designs are used when the sample needs to be collected based on a specific characteristic of the population (e.g., people with diabetes).

Unlike probability sampling, the goal is not to achieve objectivity in the selection of samples, or to make statistical inferences. Rather, the goal is to apply the results only to a certain subsection or organisation. These are used in both quantitative and qualitative research.

It is important to be aware of the advantages and disadvantages of non-probability sampling and to understand how they can play a role in your study design.

Advantages of non-probability sampling

Depending on your research design, there are advantages to choosing non-probability sampling.

  • Non-probability sampling does not require a sampling frame, so your subjects are often readily available. This can make non-probability sampling quicker and easier to carry out.
  • Non-probability sampling allows you to target particular groups within your population. In certain types of research, it is vital that certain units be included in your sample. For example, many kinds of medical research rely on people with a specific health issue.
  • Although it is not possible to make statistical inferences from the sample to the population, non-probability sampling methods can provide researchers with the data to make other types of generalisations from the sample being studied.

Disadvantages of non-probability sampling

Non-probability sampling has some downsides as well. These include the following:

  • Non-probability samples are extremely unlikely to be representative of the population studied. This undermines the generalisability of your results.
  • As some units in the population have no chance of being included in the sample, undercoverage bias is likely.
  • Furthermore, since the selection of units included in the sample is often based on ease of access, sampling bias is common as well.
  • While the subjective judgment of the researcher in choosing who makes up the sample can be an advantage, it also increases the risk of researcher bias.

You can mitigate the disadvantages of non-probability sampling by describing your choices in the methodology section of your dissertation . Specifically, explain how your sample would differ from one that was randomly selected and mention any subjects who might be excluded or overrepresented in your sample.

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

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

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

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

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

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

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

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  • What Is Probability Sampling? | Types & Examples

What Is Probability Sampling? | Types & Examples

Published on July 5, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling.

To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance of being selected. This is usually done through a random selection process, like a drawing.

Table of contents

Types of probability sampling, examples of probability sampling methods, probability vs. non-probability sampling, advantages and disadvantages of probability sampling, other interesting articles, frequently asked questions about probability sampling.

There are four commonly used types of probability sampling designs:

Simple random sampling

  • Stratified sampling

Systematic sampling

  • Cluster sampling

Simple random sampling gathers a random selection from the entire population, where each unit has an equal chance of selection. This is the most common way to select a random sample.

To compile a list of the units in your research population, consider using a random number generator. There are several free ones available online, such as random.org , calculator.net , and randomnumbergenerator.org .

Writing down the names of all 4,000 inhabitants by hand to randomly draw 100 of them would be impractical and time-consuming, as well as questionable for ethical reasons. Instead, you decide to use a random number generator to draw a simple random sample.

Stratified sampling collects a random selection of a sample from within certain strata, or subgroups within the population. Each subgroup is separated from the others on the basis of a common characteristic, such as gender, race, or religion. This way, you can ensure that all subgroups of a given population are adequately represented within your sample population.

For example, if you are dividing a student population by college majors, Engineering, Linguistics, and Physical Education students are three different strata within that population.

To split your population into different subgroups, first choose which characteristic you would like to divide them by. Then you can select your sample from each subgroup. You can do this in one of two ways:

  • By selecting an equal number of units from each subgroup
  • By selecting units from each subgroup equal to their proportion in the total population

If you take a simple random sample, children from urban areas will have a far greater chance of being selected, so the best way of getting a representative sample is to take a stratified sample.

First, you divide the population into your strata: one for children from urban areas and one for children from rural areas. Then, you take a simple random sample from each subgroup. You can use one of two options:

  • Select 100 urban and 100 rural, i.e., an equal number of units
  • Select 80 urban and 20 rural, which gives you a representative sample of 100 people

Systematic sampling draws a random sample from the target population by selecting units at regular intervals starting from a random point. This method is useful in situations where records of your target population already exist, such as records of an agency’s clients, enrollment lists of university students, or a company’s employment records. Any of these can be used as a sampling frame.

To start your systematic sample, you first need to divide your sampling frame into a number of segments, called intervals. You calculate these by dividing your population size by the desired sample size.

Then, from the first interval, you select one unit using simple random sampling. The selection of the next units from other intervals depends upon the position of the unit selected in the first interval.

Let’s refer back to our example about the political views of the municipality of 4,000 inhabitants. You can also draw a sample of 100 people using systematic sampling. To do so, follow these steps:

  • Determine your interval: 4,000 / 100 = 40. This means that you must select 1 inhabitant from every 40 in the record.
  • Using simple random sampling (e.g., a random number generator), you select 1 inhabitant.
  • Let’s say you select the 11th person on the list. In every subsequent interval, you need to select the 11th person in that interval, until you have a sample of 100 people.

Cluster sampling is the process of dividing the target population into groups, called clusters. A randomly selected subsection of these groups then forms your sample. Cluster sampling is an efficient approach when you want to study large, geographically dispersed populations. It usually involves existing groups that are similar to each other in some way (e.g., classes in a school).

There are two types of cluster sampling:

  • Single (or one-stage) cluster sampling, when you divide the entire population into clusters
  • Multistage cluster sampling, when you divide the cluster further into more clusters, in order to narrow down the sample size

Clusters are pre-existing groups, so each high school is a cluster, and you assign a number to each one of them. Then, you use simple random sampling to further select clusters. How many clusters you select will depend on the sample size that you need.

Multi-stage sampling is a more complex form of cluster sampling, in which smaller groups are successively selected from larger populations to form the sample population used in your study.

First, you take a simple random sample of departments. Then, again using simple random sampling, you select a number of units. Based on the size of the population (i.e., how many employees work at the company) and your desired sample size, you establish that you need to include 3 units in your sample.

In stratified sampling , you divide your population in groups (strata) that share a common characteristic and then select some members from every group for your sample. In cluster sampling , you use pre-existing groups to divide your population into clusters and then include all members from randomly selected clusters for your sample.

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There are a few methods you can use to draw a random sample. Here are a few examples:

  • The fishbowl draw
  • A random number generator
  • The random number function

Fishbowl draw

You are investigating the use of a popular portable e‐reader device among library and information science students and its effects on individual reading practices. You write the names of 25 students on pieces of paper, put them in a jar, and then, without looking, randomly select three students to be interviewed for your research.

All students have equal chances of being selected and no other consideration (such as personal preference) can influence this selection. This method is suitable when your total population is small, so writing the names or numbers of each unit on a piece of paper is feasible.

Random number generator

Suppose you are researching what people think about road safety in a specific residential area. You make a list of all the suburbs and assign a number to each one of them. Then, using an online random number generator, you select four numbers, corresponding to four suburbs, and focus on them.

This works best when you already have a list with the total population and you can easily assign every individual a number.

RAND function in Microsoft Excel

If your data are in a spreadsheet, you can also use the random number function (RAND) in Microsoft Excel to select a random sample.

Suppose you have a list of 4,000 people and you need a sample of 300. By typing in the formula =RAND() and then pressing enter, you can have Excel assign a random number to each name on the list. For this to work, make sure there are no blank rows.

This video explains how to use the RAND function.

Depending on the goals of your research study, there are two sampling methods you can use:

  • Probability sampling : Sampling method that ensures that each unit in the study population has an equal chance of being selected
  • Non-probability sampling : Sampling method that uses a non-random sample from the population you want to research, based on specific criteria, such as convenience

Probability sampling

In quantitative research , it is important that your sample is representative of your target population. This allows you to make strong statistical inferences based on the collected data. Having a sufficiently large random probability sample is the best guarantee that the sample will be representative and the results are generalizable and free from research biases like selection bias and sampling bias .

Non-probability sampling

Non-probability sampling designs are used in both quantitative and qualitative research when the number of units in the population is either unknown or impossible to individually identify. It is also used when you want to apply the results only to a certain subsection or organization rather than the general public. Non-probability sampling is at higher risk than probability sampling for research biases like sampling bias .

You are unlikely to be able to compile a list of every practicing organizational psychologist in the country, but you could compile a list with all the experts in your area and select a few to interview.

It’s important to be aware of the advantages and disadvantages of probability sampling, as it will help you decide if this is the right sampling method for your research design.

Advantages of probability sampling

There are two main advantages to probability sampling.

  • Samples selected with this method are representative of the population at large. Due to this, inferences drawn from such samples can be generalized to the total population you are studying.
  • As some statistical tests, such as multiple linear regression , t test , or ANOVA , can only be applied to a sample size large enough to approximate the true distribution of the population, using probability sampling allows you to establish correlation or cause-and-effect relationship between your variables.

Disadvantages of probability sampling

Choosing probability sampling as your sampling method comes with some challenges, too. These include the following:

  • It may be difficult to access a list of the entire population, due to ethical or privacy concerns, or a full list may not exist. It can be expensive and time-consuming to compile this yourself.
  • Although probability sampling reduces the risk of sampling bias , it can still occur. When your selected sample is not inclusive enough, representation of the full population is skewed .

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qualitative research non probability sampling

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

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

Research bias

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

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

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

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

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COMMENTS

  1. What Is Non-Probability Sampling?

    Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question. Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify.

  2. Non-probability Sampling

    Non-probability sampling is a type of sampling method in which the probability of an individual or a group being selected from the population is not known. In other words, non-probability sampling is a method of sampling where the selection of participants is based on non-random criteria, such as convenience, availability, judgment, or quota.

  3. Different Types of Sampling Techniques in Qualitative Research

    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.

  4. Sampling Methods

    Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data. You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work. Table of contents Population vs. sample

  5. Sampling Techniques for Qualitative Research

    Sampling Techniques for Qualitative Research Heather Douglas Chapter First Online: 27 October 2022 1987 Accesses 2 Citations Abstract This chapter explains how to design suitable sampling strategies for qualitative research.

  6. Sampling Methods

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

  7. 10.2 Sampling in qualitative research

    Nonprobability sampling- sampling techniques for which a person's likelihood of being selected for membership in the sample is unknown; Purposive sample- when a researcher seeks out participants with specific characteristics; ... Basics of social research: Qualitative and quantitative approaches (2nd ed.). Boston, MA: Pearson.

  8. 6.2 Nonprobability sampling

    Qualitative research often employs a theoretical sampling strategy, where study sites, respondents, or cases are selected based on theoretical considerations such as whether they fit the phenomenon being studied (e.g., sustainable practices can only be studied in organizations that have implemented sustainable practices), whether they possess ce...

  9. Nonprobability sampling

    Nonprobability sampling is however widely used in qualitative research. Examples of nonprobability sampling include: Convenience, haphazard or accidental sampling - members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, are all examples of convenience sampling.

  10. Sampling in Qualitative Research

    Despite the growing numbers of textbooks on qualitative research, most offer only a brief discussion of sampling issues, and far less is presented in a critical fashion ( Gubrium and Sankar 1994; Werner and Schoepfle 1987; Spradley 1979, 1980; Strauss and Corbin 1990; Trotter 1991; but cf. Denzin and Lincoln 1994; DePoy and Gitlin 1993; Miles an...

  11. Non-Probability Sampling: Types, Examples, & Advantages

    Non-Probability Sampling: Types, Examples, & Advantages When we are going to do an investigation, and we need to collect data, we have to know the type of techniques we are going to use to be prepared.

  12. Non-probability sampling

    Non-probability sampling represents a valuable group of sampling techniques that can be used in research that follows qualitative, mixed methods, and even quantitative research designs. Despite this, for researchers following a quantitative research design , non-probability sampling techniques can often be viewed as an inferior alternative to ...

  13. 10.2 Sampling in qualitative research

    Nonprobability sampling refers to sampling techniques for which a person's likelihood of being selected for membership in the sample is unknown. Since we don't know the likelihood of selection, we don't know whether a nonprobability sample is truly representative of a larger population. ... Basics of social research: Qualitative and ...

  14. (PDF) Non-probability sampling

    Non-probability sampling Authors: Vasja Vehovar University of Ljubljana Vera Toepoel Utrecht University Stephanie Steinmetz University of Lausanne Abstract A sample is a subset of a population...

  15. Sampling methods in Clinical Research; an Educational Review

    Sampling types. There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1, 2] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee ...

  16. (PDF) Non-Probability and Probability Sampling

    ... In "non-probability" sampling, nonrandomised methods are used to draw the sample and the method mostly involves judgment (Showkat, 2017; Glen, 2015;Doherty, 1994). Examples of...

  17. Sampling in Qualitative Research

    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 ...

  18. (PDF) Non-Probability Sampling

    Paper. Oaks, CA: Sage. ... Six cases were selected through a purposive sampling to explore the processes which shape learning and quality enterprise teaching pedagogy (Marshall and Rossman, 1999;...

  19. Big enough? Sampling in qualitative inquiry

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

  20. 7.3 Probabilistic and Non-Probabilistic Sampling Techniques

    Non-probability sampling techniques are the best approach for qualitative research. Because the researcher seeks a strategically chosen sample, generalizability is more of a theoretical or conceptual issue, and it is not possible to generalize back to the population (Palys & Atchison, 2014). Probabilistic sampling techniques

  21. What Is Non-Probability Sampling?

    Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question. Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify.

  22. What Is Probability Sampling?

    Probability sampling is a sampling method that involves randomly selecting a sample, or a part of the population that you want to research. It is also sometimes called random sampling. To qualify as being random, each research unit (e.g., person, business, or organization in your population) must have an equal chance of being selected.

  23. (PDF) Sampling in Qualitative Research

    Abstract. The chapter discusses different types of sampling methods used in qualitative research to select information-rich cases. Two types of sampling techniques are discussed in the past ...

  24. Types of Purposive Sampling Techniques with Their Examples and

    The aim of the article was to review the purposive sampling types as discussed by Patton (1990) and exemplify them in line with the current trends in the studies being conducted today.