sampling techniques essay questions

Sampling Techniques in Education Essay

Random sampling is a sampling technique where all elements in a population have an equal probability of being selected to form the sample. It means, therefore, that elements are chosen arbitrarily without following any formulae (Babbie, 2010). This technique is unbiased and it gives true representative statistics, especially when the sample size is large.

In addition, the technique requires minimal prior knowledge of the population. Similarly, it is simple to use since it does not require one to have complex mathematical knowledge (Babbie, 2010).

Systematic sampling on the other hand requires arrangement of the population in a given order. The first element is chosen randomly while the subsequent elements are chosen after certain regular intervals. It should be note that this type of sampling gives every element an equal chance of selection. This type of sampling is easy to use and check incase need arises.

On the same note, since the technique arranges the population in a systematic order, sampling is quick which saves time and labor (Babbie, 2010). In addition, when the frame used in systematic sampling is modern, the technique is efficient compared to random sampling.

Convenience sampling refers to a sampling technique where researchers are free to choose sample elements using any method they deem fit. There is no laid down procedure as to how the elements should be sampled thus, it neither applies probability nor judgment. The technique is easy to use for investigators because they choose the sample that is useful to their study (Babbie, 2010). It is good when one has no time and money to gather a large population, because it does not require specific rules to be met.

In stratified sampling, researchers group the population into different groups using differentiating characteristics. The researcher will then randomly select elements from each stratum using the size of the stratum in relation to the population to determine the number of elements to be picked from each stratum. The elements are then combined to form the sample (Babbie, 2010). The technique allows study of each specific group which might not be possible in a generalized population.

In cases where different segments of the population need different degrees of accuracy, stratified sampling is more applicable. Moreover, the resulting sample is more representative and gives more efficient statistics. Furthermore, stratified sampling gives room for investigators to use different types of sampling methods for each stratum as and when they deem fit (Babbie, 2010).

On the other hand, cluster sampling involves the grouping of the population into groups called clusters. A few clusters are then selected randomly and all the elements in the selected clusters are used to form the sample (Babbie, 2010). The advantage of clustering is that it greatly reduces costs of travelling as well as administrative costs. On the same note, this type reduces variability of the statistics observed as compared to other methods of sampling (Babbie, 2010).

Multi-stage sampling involves combination of two or more sampling techniques. Initially, the researcher divides the population into large clusters. The researcher then subdivides few selected clusters into sub-clusters. The clusters to be subdivided are selected either randomly, or using information collected from elements in the first clusters.

The process is repeated until the elements in the sub-sets are few enough. Finally, the researcher uses any other sampling technique to select sub-sets whose elements are used as a sample. The method is beneficial in cases where it is difficult to get a complete list of the population. It is an advanced form of cluster sampling (Babbie, 2010).

Multi-stage sampling is accurate compared to cluster sampling when the same sample size is used. Moreover, multi-stage sampling is a more convenient way of finding a sample. On the same note, the method is more cost effective and in many instances the survey can be done quickly compared to other methods (Babbie, 2010).

Babbie, E. R. (2010). The Basics of Social Research . Stanford: Cengage Learning.

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Methodology
Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

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

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

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

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

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

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

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

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

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

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

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

Sampling frame

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

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

Sample size

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

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

There are four main types of probability sample.

1. Simple random sampling

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

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

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

2. Systematic sampling

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

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

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

3. Stratified sampling

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

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

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

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

4. Cluster sampling

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

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

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

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

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

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

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

1. Convenience sampling

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

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

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

2. Voluntary response sampling

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

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

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

3. Purposive sampling

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

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

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

4. Snowball sampling

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

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

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

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

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

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

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

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

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

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

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

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Chapter 5. Sampling

Introduction.

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

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

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

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

Quick Terms Refresher

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

The “Who” of Your Research Study

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

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

Sampling People

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

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

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

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

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

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

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

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

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

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

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

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

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

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

When Your Population is Not Composed of People

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

Case Studies

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

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

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

Content: Documents, Narrative Accounts, And So On

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

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

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

What is the Correct “Number” to Sample?

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

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

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

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

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

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

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

How did you find/construct a sample?

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

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

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

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

Examples of “Sample” Sections in Journal Articles

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

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

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

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

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

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

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

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

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

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

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

Final Words

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

Table 5.1. Sampling Type and Strategies

10.1 Basic concepts of sampling

Learning objectives.

Differentiate between populations, sampling frames, and samples
Describe inclusion and exclusion criteria
Explain recruitment of participants in a research project

In social scientific research, a population is the cluster of people you are most interested in; it is often the “who” that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as “the American people,” but they are typically more specific. For example, a large study interested in the population of the American people will likely specify which American people, such as adults over the age of 18, citizens, or legal permanent residents.

To reiterate, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that social workers typically ask. For example, let’s say we wish to answer the following research question: “How does gender impact success in a batterer intervention program?” Would you expect to be able to collect data from all people in batterer intervention programs across all nations from all historical time periods? Unless you plan to make answering this research question your entire life’s work (and then some), I’m guessing your answer is a resounding no. So, what to do? Does the lack of time or resources to gather data from every single person of interest mean that you must give up your research interest?

Absolutely not. Instead, researchers use what’s called a sampling frame as an intermediate point between the overall population and the sample that is drawn. A sampling frame is a real or hypothetical list of people from which you will draw your sample, and putting together a sampling frame is the first step in conducting human subjects research. Social work researchers must think about locations or groups in which your target population gathers or interacts. For example, a study on quality of care in nursing homes may choose a local nursing home because it’s easy to access. The sampling frame could be all of the patients at the nursing home. You would select your participants for your study from the list of patients at the nursing home. Note that this is a real list. That is, an administrator at the nursing home would give you a list with every resident’s name on it from which you would select your participants. If you decided to include more nursing homes in your study, then your sampling frame could be all of the patients at all of the nursing homes you included.

The nursing home example is perhaps an easy one. Let’s consider some more examples. Unlike nursing home patients, cancer survivors do not live in an enclosed location and may no longer receive treatment at a hospital or clinic. For social work researchers to reach participants, they may consider partnering with a support group that services this population. Perhaps there is a support group at a local church in which survivors may cycle in and out based on need. Without a set list of people, your sampling frame would simply be the people who showed up to the support group on the nights you were there, which is a hypothetical list.

More challenging still is recruiting people who are homeless, those with very low income, or people who belong to stigmatized groups. For example, a research study by Johnson and Johnson (2014) [1] attempted to learn usage patterns of “bath salts,” or synthetic stimulants that are marketed as “legal highs.” Users of “bath salts” don’t often gather for meetings, and reaching out to individual treatment centers is unlikely to produce enough participants for a study as use of bath salts is rare. To reach participants, these researchers ingeniously used online discussion boards in which users of these drugs share information. Their sampling frame included everyone who participated in the online discussion boards during the time they collected data. Regardless of whether a sampling frame is easy or challenging, the first rule of sampling is: go where your participants are .

Once you have an idea of where your participants are, you need to recruit your participants into your study. Recruitment refers to the process by which the researcher informs potential participants about the study and attempts to get them to participate. Recruitment comes in many different forms. If you have ever received a phone call asking for you to participate in a survey, someone has attempted to recruit you for their study. Perhaps you’ve seen print advertisements on buses, in student centers, or in a periodical. I’ve received many emails that were passed around my school asking for participants, usually for a graduate student. (As an aside, researchers sometimes speak of “research karma.” If you participate in others’ research studies, they will participate in yours.) As we learn more about specific types of sampling, make sure your recruitment strategy makes sense with your sampling approach. For example, if you put up a flyer in the student health office to recruit for your study, you would likely be using availability or convenience sampling.

As you think about sampling frame and recruitment, another level of specificity that researchers add at this stage is deciding if there are certain characteristics or attributes that individuals must have if they participate in your study. These are known as inclusion and exclusion criteria. Inclusion criteria are the characteristics a person must possess in order to be included in your sample. If you were conducting a survey on centenarians living in nursing homes, you might want to sample only elderly adults. In that case, your inclusion criteria for your sample would be that individuals have to be age 100 or older and they must be actively living in a nursing home. Comparably, exclusion criteria are characteristics that disqualify a person from being included in your sample. Going back to the previous example, an older adult could be excluded from your sample because they are 99 years or younger, or because they do not actively live in a nursing home. Exclusion criteria are often like the mirror image of inclusion criteria. However, there may be other criteria by which you want to exclude people from your sample. For example, you may exclude centenarians who are in a medically vegetative state or centenarians who have not lived at the nursing home more than 30 days.

Once you find a sampling frame from which you can recruit your participants, you end up with a sample. A sample is the group of people you successfully recruit from your sampling frame to participate in your study. If you are a participant in a research project—answering survey questions, participating in interviews, etc.—you are part of the sample of that research project. Some research projects social workers may engage in don’t use people at all. Instead of people, the elements selected for inclusion into a sample are documents, including client records, blog entries, or television shows. A researcher conducting this kind of analysis, described in detail in Chapter 14, still goes through the stages of sampling—identifying a sampling frame, applying inclusion criteria, and gathering the sample.

Applying sampling terms

Sampling terms can be a bit daunting at first, but with some practice, they will become second nature. Let’s walk through an example from one of my research projects. I am currently collecting data for a research project on how much it costs to become a licensed clinical social worker or LCSW. An LCSW is necessary for private clinical practice and is used by supervisors in human service organizations to sign off on clinical charts from less credentialed employees, as well as provide clinical supervision. If you are interested in providing clinical services as a social worker, you should become familiar with the licensing laws in your state.

concentric circles with population on the outside, sampling frame next, and sample in the middle circle

Using Figure 10.1 as a guide, my population is clearly clinical social workers, as these are the people about whom I want to draw conclusions. The next step inward would be a sampling frame. Unfortunately, there is no list of every licensed clinical social worker in the United States. I could write to each state’s social work licensing board and ask for a list of names and addresses, perhaps even using a Freedom of Information Act request if they were unwilling to share the information. That option sounds time-consuming and has a low likelihood of success. Instead, I tried to figure out where social workers are likely to congregate. I considered setting up a booth at a National Association of Social Workers (NASW) conference and asking people to participate in my survey. Ultimately, this would prove too costly, and I wouldn’t be able to draw a truly random sample. I finally discovered the NASW membership email list, which is available to advertisers, including researchers advertising for research projects. While the NASW list does not contain every social worker, it reaches over one hundred thousand social workers via email regularly through its monthly newsletter.

My sampling frame became the members of the NASW membership list. I decided to recruit 5000 participants because I knew that email advertisements don’t have the best return rates. I sent a recruitment email to the 5000 participants and specified that I only wanted to hear from social workers who either currently or recently received clinical supervision for licensure. This was my inclusion criteria and it was important because many of the people on the NASW membership list may not be licensed. While I would love it if my sample were all 5000 participants I attempted to recruit, my actual sample contained only 150 people. These are the people I successfully recruited using my email advertisement—the people who filled out my survey on licensure.

From this example, you can see that sampling is a process. The process flows sequentially from figuring out your target population, to thinking about where to find people from your target population, to finding a real or imaginary list of people in your population, to recruiting people from that list to be a part of your sample. Through the sampling process, you must consider where people in your target population are likely to be and how best to get their attention for your study. Sampling can be an easy process, like calling every 100th name from the phone book one afternoon. On the other hand, sampling can sometimes be challenging, like standing in an area where people who are homeless gather for shelter for weeks. In either case, your goal is to recruit enough people who will participate in your study so you can learn about your population.

In the next two sections of this chapter, we will discuss sampling approaches, also known as sampling techniques or types of samples. Sampling approach determines how a researcher selects people from the sampling frame to recruit into their sample. The goals of qualitative and quantitative research differ, so the sampling approach for each is distinctly different. Quantitative approaches allow researchers to make claims about populations that are much larger than their actual sample with a fair amount of confidence. Qualitative approaches are designed to allow researchers to make conclusions that are specific to one time, place, context, and group of people. We will review both of these approaches to sampling in the coming sections of this chapter. First, we examine sampling types and techniques used in qualitative research. After that, we’ll look at how sampling typically works in quantitative research.

Key Takeaways

A population is the group who is the main focus of a researcher’s interest; a sample is the group from whom the researcher actually collects data.
Sampling involves selecting the observations that you will analyze.
A researcher starts to conduct the sampling process by going where the participants are likely to be.
Sampling frames can be real or hypothetical.
Recruitment involves informing potential participants about your study and seeking their participation.

Exclusion criteria – characteristics that disqualify a person from being included in a sample

Inclusion criteria – the characteristics a person must possess in order to be included in a sample

Population – the cluster of people that a researcher is most interested in

Recruitment – the process by which the researcher informs potential participants about the study and attempts to get them to participate

Sample – the group of people you successfully recruit from your sampling frame to participate in your study

Sampling frame – a real or hypothetical list of people from which a researcher will draw her sample

Image attributions

crowd by mwewering CC-0

job interview by styles66 CC-0

Johnson, P. S., & Johnson, M. W. (2014). Investigation of “bath salts” use patterns within an online sample of users in the United States. Journal of psychoactive drugs , 46 (5), 369-378. ↵

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

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As previously mentioned, there are many reasons why you would use a sample rather than a census when conducting research. And as mentioned, there are many things that could go wrong. One of the things that could go wrong is the selection of a sample. The primary goal of sampling is to create a representative sample, one in which the smaller group (sample) accurately represents the characteristics of the larger group (population). If the sample is well selected, the sample will be generalizable to the population.

There are many ways to obtain a sample. The techniques used will vary based on the circumstances under which the study is conducted as well as the aims of the research. The way in which samples are drawn will affect the quality of a study.

Prior to choosing a selection method, you should have defined the population and the purpose for the study. Clearly defining the target population is important, meaning you will define both the size of the population and the accessibility of the population. As noted previously, anticipated survey response refusal will affect the size of the sample needed. Accessibility of individuals within the population will also affect the sample selection procedures. There are two general approaches to sampling: random and non-random. However, additional consideration should be made based on whether the study will be a qualitative study.

Definitions

Quantitative sampling. Surveys are typically designed to produce descriptive numerical statistics (e.g., scores, ages, strength of opinions, frequencies) that can be used to describe various characteristics found within the population. Any qualitative data obtained is typical categorized and quantified. Sampling for these studies must produce representative samples because generalizability is important. A distinctive aim of these studies is to gain a general understanding of the characteristics found in the population.

Qualitative sampling . Qualitative studies are not interested as much with generalizability as they are with understanding a phenomenon. The sample must produce good informants for the study. The characteristics of the respondents are more important than the size of the sample. These samples will be smaller and less representative but should provide researchers access to a good representation of key informants. Often the aim of qualitative research is to get a deeper, fuller understanding of the topic or phenomenon.

Random sampling . A selection technique where every unit in the population has an equal chance of being selected. The unit of analysis often involves individuals but may be intact groups.

Non-random sampling . While random sampling may be preferred, there are many ways in which a planned random sample may become less random. In non-random sampling (or non-probability sampling), researchers are unable to select participants at random from the population. This includes situations where circumstances (e.g., survey refusal leading to low response rates or missing contact information) diminish the likelihood that the sample provides a good representation of the population. Follow-up contact or a post-survey examination of demographic characteristics are often needed to verify the degree to which survey results might be considered generalizable.

Random selection and random assignment. These two terms should not be confused. Random selection is used to establish a sample. If done properly, the results of the study are believed to be generalizable. Random assignment is use in experimental studies. Randomly assigning individuals to two different groups is done in an attempt to make the two groups comparable. Random selection affects claims of generalizability. Random assignment is the basis for experimental claims of causality.

Random Sampling Techniques

Simple random.

For this type of sampling, each individual (or unit) in the population has an equal and independent chance of being selected. In probability sampling, another name for random sampling, the researcher can select the level of chance. In order to produce a true random sample, the population must be known. A known, finite population is one where all members of the population can be identified and are accessible. This kind of sampling also assumes that all who are selected to be part of the sample will respond.

A random sample does not guarantee that the sample will properly represent those in the population. Sampling is not a precise science. There is still a chance that a randomly selected sample will be skewed in some way—by this I mean the sample under- or overrepresents some group or characteristic found within the population. The Central Limit Theory tells us that when an infinite number of samples are taken, the distribution of the sample means will be normally distributed, and the average of the sample means will be that of the population. However, our understanding of the normal curve likewise indicates that the mean of any one sample may be extremely different from the population. Still, while the result we obtain will not be perfect, care should be taken to attain the best result possible. Random sampling is used when we don’t have specific information about those in the target population and wish to remove human bias from the selection process. Random sampling is believed to be the best way to avoid selection bias.

Systematic Sampling

Systematic sampling is an adaptation of random sampling which does not give everyone an independent chance of being selected. For example, the selection process may choose every fifth person in a list. This is not completely random because an individual’s position in the list limits the chance they will be selected (the selection is dependent on the individual's position in the list); the randomness of the selection becomes even more problematic if the list is compiled in a way that introduces a systematic bias.

Stratified Sampling

A stratum is an identifiable, mutually exclusive subgroup within a population. Stratified sampling attempts to guarantee representation from each important strata within the population. Membership in a stratum must be homogeneous so the sampling would not allow selection of an individual who has membership in two distinct strata. Stratification is considered to be a random sampling technique because individuals are randomly selected from each stratum. Stratified sampling could be equal or proportional. The researcher could select an equal number of participants from each stratum, or they could select participants proportionally based on the estimated size of each stratum. Proportional sampling is preferred if the sample is to be generalizable. In this case the required sample size selected from each stratum should be determined independently so each stratum is appropriately represented. This may require a much larger number of participants compared to the number needed using simple random sampling.

Cluster Sampling

With cluster sampling the unit of analysis is based on intact groups rather than individuals. For example, all those in a particular school or classroom are selected, not specific individuals within each school or classroom. The intact units are however randomly selected. For this to produce a representative sample, it is assumed that the intact units will include a variety of individuals represented in the population or that an adequate number of heterogeneous intact groups selected will, as a whole, adequately represent the population. This may or may not be the case and may require a combination of stratified and cluster sampling. In practice, not all samples obtained in this manner are random samples. When a research study requires that the unit of analysis includes sampling of intact groups, special care needs to be taken to make sure that adequate representation is obtained.

Non-Random Sampling Techniques

While random sampling is preferred (and considered by some to be the gold standard), it is not always possible to obtain a random sample. And while the basic procedures used with non-random sampling often mirror sampling procedures used to obtain random samples, any method of sampling that does not allow for individuals (or units) to have an equal and independent chance of being selected is referred to as non-random sampling. Non-random sampling is considered inferior to random sampling because there is a greater chance that the sample will not represent the population adequately. However, for a variety of reasons, non-random sampling in the social sciences is quite common.

The most common reason for using non-random sampling is that of necessity. Random samples cannot be selected when the size of the population is unknown, individuals cannot be easily identified, access to the potential respondents is restricted, or contact information is unattainable. In addition, even when random selection is implemented, ethical consideration regarding the protection of human subjects’ rights may prevent the sample from being a true random sample. For example, randomly selected individuals may not be willing to provide information or allow their information to be used. If this happens in large numbers, or in a systematically unbalanced way, a potential random sample will, in practice, become a non-random sample. This could considerably diminish the chances that the sample adequately represents the population.

Regardless of the way a sample is obtained, the goal of any sampling technique is to allow the researcher to access information from those who can provide useful information. Useful in this case means providing information that helps answer the research questions in such a way that researchers can trust the results; this is an issue of validity. There are many ways to obtain a non-random sample.

Convenient Sampling

A convenient sample is comprised of individuals who are available and willing to complete the survey (i.e., volunteers who can be contacted and are willing to participate). Any time you send out a broad invitation to potential respondents asking them to volunteer to take a survey, you are creating a convenient sample. A convenient sample is less likely to adequately represent the population than a random sample, and the results are less likely to be generalizable without having a larger sample size. Even when a high response rate is obtained, if those available and willing to participate systematically do not represent those in the population, the results will not be valid. Unfortunately, we may never know the degree to which any sample is biased, but there is an increased probability that a convenient sample will not adequately represent the population compared to a random sample.

Quota Sampling

Like a stratified sample, quota sampling involves selecting individuals to participate based on identifiable characteristics of individuals within the population. With quota sampling, the researcher identifies major subgroups of interest within the population (strata), determines the number of individuals needed, and then attempts to obtain a sufficient number of willing and available participants from each subgroup. Like stratified sampling, the number of participants needed (i.e., the quota) may be based on equal or proportional requirements.

Purposive Sampling

Selecting participants using purposive sampling procedures requires the researcher to specify criterion for inclusion. As a result, purposive sampling has at times been called criterion-based sampling. Criteria are based on a set of characteristics individuals possess (i.e., things about the potential respondents that make them interesting because they would likely be able to provide useful information). Once the criterion for inclusion have been identified, participant selection will focus on getting a sufficient number of willing participants who meet the criterion. Because participation in a qualitative study often requires participants to willing submit to lengthy, involved data collection procedures, the sampling techniques used in qualitative studies are almost always purposive. There are several ways the inclusion criterion for purposive sampling may be established.

Comprehensive Sampling

Comprehensive sampling attempts to obtain data from individuals experiencing every possible condition or subgroups defined within the population. This usually isn’t possible, but when it is possible, it is not practical. More often researchers will use some form of homogeneous sampling where selection criteria are based on choosing individuals with similar experiences, situations, perspectives, interests, or circumstances. This is a more manageable approach and researchers often will refine inclusion criteria to match a particular research purpose. Following are examples of these inclusion criteria.

Maximum Variation ( Intensity Sampling). Selection criteria are designed to obtain a wide range of participants based on a few specific variables. An example of maximum variation in a sample would be the selection of students with various levels of academic achievement from various years in school.

Extreme Case . In this situation, selection criteria are intended to include participants representing extreme situations. For example, those who participate in a regimented exercise routine every day without fail and those who claim to never exercise at all.

Typical Case. With this strategy, the researcher sets inclusion criteria to include people who typify the normal (most prominent) individuals in the population. To do this the researcher would consult experts or examine theory to determine characteristics of the “typical” person they wish to study, then set out to find a sample of these individuals. For example, the researcher might look for individuals described as being typical based on characteristics like age, experience, education, gender, behavior, or perspective. In cases where the purpose of the research is to define what is typical or normal, the sampling would need to be more comprehensive.

Critical Case. Sampling to include critical case individuals requires identifying individuals or intact groups who are important for some specific reason. For example, a researcher might select schools where conditions would likely result in greater resistance to planned reforms. The critical inclusion requirement being that if there is resistance, it will exist in those schools. The converse may also be a critical case; if there is little resistance to the proposed reforms, it will likely be at other schools.

Negative Case (Discrepancy Sampling). The selection criteria for a negative case are intended to identify respondents who are atypical, go against the norm, or provide examples that might disconfirm expected results. The sample is chosen to include those who appear to wholly disprove or refute a theory. For example, an intervention may be extremely effective for the vast majority of individuals; however, a small group of individuals tend to be negatively impacted by the intervention, meaning those individuals represent a negative case by going against expected outcomes.

Referral or Snowball Sampling. This type of sampling is based on practical purposes rather than research purposes. When those individuals matching a particular set of criteria are not readily identifiable, one way to locate participants is to ask for referrals. Once one individual is found and surveyed, they are asked if they know others who share similar characteristics. Thus, the selection process has a snowball effect (i.e., the sample gets larger as you go). With this technique it can be difficult to know when the number in the sample is sufficient. This is where data saturation decisions need to be made. Data saturation refers to situations where the information you obtain from participants begins to repeat. Saturation refers to the point where you don’t need more participant data because you are getting the same answers. Additional information in this case would not improve your understanding of the phenomenon, just substantiate the strength of the finding.

Need for Replication

It is important to understand the unlikelihood that any sample you obtain will perfectly represent the population from which it was drawn. Even with a random sample, there is a high probability that the sample will not exactly represent the population in some way. Given that sampling is not a precise science, the need to replicate a study should be evident. A carefully selected sample can provide valuable results, which is why we conduct research. However, the sample used to obtain a result may have been flawed in some way, thus you would need to redo the study with a different sample. In this sense, replication of a study is done to verify the results. Still, few studies are replicated in such a way that completely verify the results of previous studies. The outcomes obtained from any carefully constructed sample will likely be of some value, they just won’t be perfect.

Chapter Summary

The way in which a sample is obtained will affect the quality (or value) of the sample.
Quantitative surveys are typically designed to produce descriptive numerical statistics that can be used to describe various general characteristics found within the population.
Qualitative surveys are not interested as much with generalizability as they are with understanding a phenomenon. As a result, in a qualitative study, the sample must produce a good set (sample) of informants for the study.
Random sampling refers to sampling techniques that allow an equal and independent chance for participants to be selected.
Random sampling is believed to be the best way to alleviate sampling error. Sampling error affects the degree to which the sample represents those in the population and thus the generalizability of the results.
Random sampling is not a foolproof method, and any random sample has a high probability of being flawed in some way. Major flaws in the sample obtained have the potential to adversely affect the result. Minor flaws can be acknowledged and accounted for.
The ability to produce a true random sample will be dependent on whether the size of the population is known (finite), individuals can be easily identified, access to the potential respondents is unrestricted, and the contact information for potential participants is available. In addition, ethical consideration regarding the protection of human subjects’ rights and response refusal issues may prevent a true random sample from being obtained.
The most convincing reason for using random sampling is that it helps researchers avoid human bias in the selection process. Random sampling is often used when specific demographic and personal information about individual respondents is unavailable.
Non-random sampling is commonly used in the social sciences due to the difficulties in obtaining a true random sample.
Random sampling and non-random sampling techniques are similar with the exception of random selection.
The most common reason for using non-random sampling is one of necessity—there is no other way to proceed.
Convenient samples are based on participants' willingness and availability (i.e., volunteers).
Other selection parameters can help refine the sampling procedure (e.g., purposive sampling). This is only possible when specific attributes of the potential respondents are known.
The research purpose will often dictate the best sampling techniques to use; however, practical issues will also influence the decision.
The unit of analysis is often individual people in the population; however, sometimes intact groups are selected. When intact groups are used (i.e., cluster sampling) the degree to which adequate representation has been achieved must be carefully considered.
Because no sample is perfect, replication studies are useful to validate the results of any study.

Discussion Questions

What are the benefits of using a random sampling procedure over a non-random sampling procedure?
How likely is it that any sample you select will be perfect? Explain.
What is the purpose of a replication study? When and why are they needed?

Practice Tasks

Pretend you wish to make comparisons between specific groups of individuals within a population. What sampling techniques would best serve your needs? Explain the benefits and limitations of the sampling procedures you chose.

For a specific study you might consider completing, identify the population and chose a sampling technique that would serve your needs. Explain the benefits and limitations of the sampling procedures you chose. What particular challenges will you need to overcome in order to obtain the sample?

References

Davies, R. S., Williams, D. D., & Yanchar, S. (2008). The use of randomisation in educational research and evaluation: A critical analysis of underlying assumptions. Evaluation & Research in Education , 21 (4), 303-317.

This content is provided to you freely by EdTech Books.

Access it online or download it at https://edtechbooks.org/designing_surveys/sampling_techniquesp .

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