Sampling in quantitative research projects is done because it is not feasible to study the whole population, and researchers hope to take what we learn about a small group of people and apply it to a larger population. There are many ways to approach this process, and they can be grouped into two categories— non-probability sampling and probability sampling . Sampling approaches are inextricably linked with recruitment, and researchers should ensure that their proposal’s recruitment strategy matches the sampling approach.

Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. Types of non-probability sampling techniques include: (1) convenience, or availability, sampling; (2) quota sampling; (3) expert sampling; and (4) snowball sampling.

In this section we will discuss how nonprobability sampling is used when conducting research. We will go into detail about the different methods that are commonly used with nonprobability sampling.

Convenience/Availability sampling

Also called accidental or opportunity sampling, convenience sampling is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping center and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centers. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping center such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping center close to a university will attract primarily university students with unique purchase habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalizability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalizable inferences.

There are a number of benefits to the availability sampling approach. First and foremost, it is less costly and time-consuming for the researcher. Availability samples can also be helpful when random sampling isn’t practical. If you are planning to survey students in an LGBTQ+ support group on campus but attendance varies from meeting to meeting, you may show up at a meeting and ask anyone present to participate in your study. A support group with varied membership makes it impossible to have a real list—or written sampling frame—from which to randomly select individuals. Availability sampling would help you reach that population.

Availability sampling is appropriate for student and smaller-scale projects, but it comes with significant limitations. The purpose of sampling in quantitative research is to generalize from a small sample to a larger population. Because availability sampling does not use a random process to select participants, the researcher cannot be sure their sample is representative of the population they hope to generalize to. Instead, the recruitment processes is likely to be structured by factors that may bias the sample to be different in some way than the overall population.

So, for instance, if we asked social work students about their level of satisfaction with the services at the student health center, and we sampled in the evenings, we would most likely get a biased perspective of the issue. Students taking only night classes are much more likely to commute to school, spend less time on campus, and use fewer campus services. Our results would not represent what all social work students feel about the topic. We might get the impression that no social work student had ever visited the health center, when that is not actually true at all. Sampling bias will be discussed in detail in Section 11.3.

Quota sampling

Quota sampling is similar to convenience/availability sampling except it involves making sure a certain number of elements from various subgroups end up in the final sample. The researchers need to identify which subgroups of the target population are important for their research questions. For example, they may want to make sure their sample has representation from different demographic characteristics such as age, race, ethnicity, or gender or other attributes such as political affiliation, health status, or whether they own pets. In quota sampling, the researchers create predefined categories and determine the number of elements (e.g., people or documents) they want in their sample for each category. Then they use convenience/availability sampling to find the elements and recruit them into the sample until the quota for that subgroup is reached.

In proportional quota sampling, the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70% Caucasians, 15% Hispanic-Americans, and 13% African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping center and ask people their voting preferences. But you will have to stop asking people who identify as Hispanic when you have 15 responses from that subgroup and African-Americans when you have 13 responses, even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you don’t have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping center in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population but may be useful in that it allows capturing the opinions of small and underrepresented groups through oversampling.

Purposive sampling

Purposive sampling is a type of sampling where the researchers select a sample based on their knowledge of their study and population, in a way to fulfill the purpose of the sample. Sometimes purposive sampling is used to select not typical cases but atypical ones. This is commonly done when we seek to compare opposite extremes of a phenomenon in order to generate hypotheses about it. Below are 7 types of purposive sampling:

Expert sampling or judgment sampling is where you draw your sample from experts in the area you want to study. If defined this way, it can be called a sub-type of purposive sampling. For example, a study of expert research engineers starts with an exploration of who other engineers look up to and who are most valued by their employers. The result determines that ‘expert’ can be defined as only those who have been awarded ten or more patents and who have at least twelve years of experience.

Critical case sampling is where you collect samples that are most likely to give you the information you are looking for; like particularly important cases or highlighting vital information. For example, if a scientist wishes to study whether hemodialysis impacts financial status and previous research shows that marital status during hemodialysis is a major indicator of financial status, scientists will only include patients on hemodialysis whose marital status changed while on hemodialysis.

Extreme case sampling focusses on participants with unique or special characteristics, the outliers in the population. For example, if you were studying inner city violence, you could study a city with high violence and compare it to a city with low violence.Like any sampling technique where a researcher deliberately chooses cases, extreme case sampling could result in selection bias, undermining results (Collier & Mahoney, 1996).

Homogenous sampling is used to collect a very specific set of participants. For example, Continuing your research on mental health services programs in your state, you are now interested in illuminating the experiences of different ethnicities through group interviewing. Using homogeneous sampling, you select Latinx directors of mental health services agencies, interviewing them about the challenges of implementing evidence-based treatments for mental health problems

Maximum variation sampling is when a sample is chosen to include a wide range of participants, or a sample that includes extreme cases. For example, suppose you are researching the challenges of mental health services programs in your state. Using maximum variation sampling, you select programs in urban and rural areas in different parts of the state, in order to capture maximum variation in location. In this way, you can document unique or diverse variations that have emerged in different locations.

Typical case sampling is a sample that is made up of participants who are have average characteristics with respect to the area of study. For example, You are researching the reactions of 9th grade students to a job placement program. To develop a typical case sample, you select participants with similar socioeconomic backgrounds from five different cities. You collect the students’ experiences via surveys or interviews and create a profile of a “typical” 9th grader who followed a job placement program. This can offer useful insights to employers who want to offer job placements to students in the future.

Total population sampling is when an entire study population is being studied. As for example, if the scientists wish to study the impact of a new teaching technique introduced in the 3rd grade of a particular school, they might consider studying all the students in the 3rd grade of that school.

Snowball sampling

In snowball sampling, participants are invited to help researchers find additional potential subjects. First, you may choose a small number of respondents who meet the requirements for inclusion in your study, and then you ask them to suggest more participants that they know who also qualify for the study. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

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The IRB, in some instances, grants permission for the use of currently enrolled research participants to recruit new participants. However, the recruitment part of the protocol must explain why this method was chosen in light of the study’s objectives and target population, and also specify the measures used to reduce the risks of violating individuals’ privacy. Hence, the approach with the lowest potential risk should be chosen. For example, a researcher looking to understand the informal patterns of leadership in a community would ask people to list other significant members in that community. In terms of this, it might be permissible for participants to provide names and contact information of individuals who might be interested in participating, when the research topic is not too sensitive or personal. As another example of acceptable approach, the study team members can provide participants the information to share with other potential participants who might be qualified or interested in the study. In this case, The IRB must approve all literature or information distributed to participants including flyers or letters of explanation.