Psychology
Snowball Sampling
Snowball sampling is a non-probability sampling technique used to identify and recruit participants through referrals from existing study subjects. This method is often employed when the target population is difficult to access or identify. It involves the initial selection of participants followed by the request for those participants to refer others who meet the study criteria.
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10 Key excerpts on "Snowball Sampling"
- Allen Rubin, Earl Babbie(Authors)
- 2021(Publication Date)
- Cengage Learning EMEA(Publisher)
Researchers using quota sampling should be aware of potential pro-blems like this and work to prevent them. For example, they should do all they can to obtain an accurate count of the number and characteristics of individuals who make up a particular cell. They should make sure that interviewers are properly trained and supervised to minimize the chances that the interviewers will violate the sampling pro-tocol in order to skip certain undesirable interviews. But there is no guarantee that all potential problems like these will be anticipated or prevented. There-fore, you would be advised to treat quota sampling warily if your purpose is statistical description. Snowball Sampling Another nonprobability sampling technique, one that some researchers consider a form of accidental sampling, is called Snowball Sampling . Snowball Sampling is appropriate when the members of a spe-cial population are difficult to locate. It might be appropriate, for example, to find a sample of home-less individuals, migrant workers, or undocumented immigrants. This procedure is implemented by col-lecting data on the few members of the target popu-lation whom one is able to locate and then asking those individuals to provide the information needed to locate other members of that population they happen to know. The term snowball refers to the process of accumulation as each located subject sug-gests other subjects. This sampling procedure also results in samples that have questionable represen-tativeness, so it is used primarily for exploratory purposes. Nevertheless, Snowball Sampling is an important and commonly used technique in qualita-tive research (as we ’ ll discuss in Chapter 18), and in research on minority and oppressed populations it is often necessary (as we mentioned in Chapter 6). 356 Part 5 Data Collection Methods with Large Sources of Data Copyright 2017 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part.
eBook - PDF- V. Kumar, Robert P. Leone, David A. Aaker, George S. Day(Authors)
- 2018(Publication Date)
- Wiley(Publisher)
The process of randomly selecting two cities could very well generate a highly nonrepresentative set. If a focus-group interview of eight or nine people is needed, again, a judgmental sample might be a highly appro- priate way to proceed. Third, sometimes it is useful to obtain a deliberately biased sample. If, for example, a product or service modification is to be evaluated, it might be possible to identify a group that, by its very nature, should be disposed toward the modification. If it is found that they do not like it, then it can be assumed that the rest of the population will be at least as negative. If they like it, of course, more research probably is required. Snowball Sampling Snowball Sampling is a form of judgmental sampling that is very appropriate when it is nec- essary to reach small, specialized populations. Suppose a long-range planning group wants to sample people who are very knowledgeable about a specialized new technology, such as the use of lasers in construction. Even specialized magazines would have a small percentage of readers in this category. Further, the target group may be employed by diverse organizations, such as the government, universities, research organizations, and industrial firms. Under a snowball design, each respondent, after being interviewed, is asked to identify one or more others in the field. The result can be a very useful sample. This design can be used to reach any small population, such as deep-sea divers, people confined to wheelchairs, owners of dune buggies, families with triplets, and so on. One problem is that those who are socially visible are more likely to be selected. Convenience Sampling To obtain information quickly and inexpensively, a convenience sample may be employed.
eBook - PDFCocaine
Scientific and Social Dimensions
- Gregory R. Bock, Julie Whelan, Gregory R. Bock, Julie Whelan(Authors)
- 2008(Publication Date)
- Wiley(Publisher)
Methodology The majority of the published community surveys of cocaine use have employed, for the most part, a ‘snowball’ sampling methodology for acquiring respondents (van Meter 1990b, Cohen 1989, Erickson & Murray 1989, Chitwood 1985). In this methodology, sample selection begins by making contact with members of a specific population that may be characterized as ‘rare’, ‘elusive’ or ‘hidden’ (see Kaplan et a1 1987, Sudman et a1 1988, Watters & Biernacki 1989, Biernacki & Waldorf 1981). The knowledge accrued in this initial fieldwork (a simple or stratified random sample may also be used) is used to find the ‘starters’, composing the ‘zero stage’ of a snowball sample. These starters, in turn, ‘nominate’ other members of the population. Interviews with the starters and nominees are routinely conducted. The snowball procedure ‘ascends’ through these chains of referrals into more general levels of the population. Snowball Sampling provides a practical technique in research situations where probability samples are not feasible. This practical advantage is usually offset by the disadvantage of the uncertain generalizability of the sample. If it is not known how representative the sample is, valid results may be difficult to obtain. There is, however, promising work being done aimed at overcoming some of these methodological disadvantages (van Meter 1990a, Frank 1979). The data presented here have been collected in Rotterdam using a design employing Snowball Sampling in different settings of cocaine use (Intraval 1990). Fieldwork began at the end of 1990. Key informants were contacted and a social map of the organizations and institutions important for the study was drawn. A large number of locations and events (cafes, bars, discos, parties, and so on) were visited by the research fieldworkers. Informal conversations explained the aim of the study and built trust in the community of cocaine users.
eBook - ePubResearching Health Needs
A Community-Based Approach
- Judy Payne(Author)
- 1999(Publication Date)
- SAGE Publications Ltd(Publisher)
Because of these limitations, research based on quota samples tends to be treated as less reliable. If you are seeking to convince bureaucrats, politicians and health professionals, your case will be more credible if you have been able to use a random sample. On the other hand, constraints of time, volunteers, or budgets may leave you no option. While random sampling is better, quota sampling, if carefully done, is a good second choice.Snowballing
A snowballing or pyramid sampling method can be used when no population list is available and representativeness is not regarded as important for the investigation. Here, researchers first select people known to them who are appropriate to the study. These people are interviewed and asked to nominate people known to them who fit the selection criteria. These are then interviewed, and so on, until a suitably sized sample is obtained. Thus, in the women with children under 5 example, you would first approach women known to you (or if you knew none, known to some of your friends or colleagues). You would then interview these women and ask them to name other women with children under 5, etc., at each stage making sure that you did not approach previous respondents.This is a very simple and cheap method of obtaining detailed information from people who are not easily identified. However, the resulting sample is likely to be extremely biased towards the particular social groups that you or your first set of respondents belong to. Your findings will, therefore, be of limited generalizability.Purposive sampling
In this method of selection, researchers purposely select people whom they think might contribute useful information. These people are often referred to as key informants or expert witnesses. Here, particular groups of people, such as teachers, police, GPs, councillors, chairpersons of community organizations, shopkeepers etc. are selected. The main disadvantage of this method is that, although it is relatively quick, the views of these people are not necessarily representative of the whole community. Rapid Appraisal, discussed in Chapter 8
eBook - ePubPolling America
An Encyclopedia of Public Opinion [2 volumes]
- Richard L. Clark, Kelly N. Foster, Samuel J. Best, Benjamin Radcliff, Richard L. Clark, Kelly N. Foster, Samuel J. Best, Benjamin Radcliff(Authors)
- 2020(Publication Date)
- Greenwood(Publisher)
N th record until a sufficient sample size has been reached. Stratified sampling divides the target population into a number of subpopulations based on a predetermined characteristic and then randomly select a certain number of individuals from each subpopulation. Cluster sampling also divides the target population into subpopulations, but it selects from among subpopulations before sampling within them. Determining the optimal method is typically a function of resource and time constraints and the characteristics of the target population.Nonprobabilistic sampling methods draw samples arbitrarily without a specific probability structure in mind. Individuals are selected because of their availability, geographical proximity, or willingness to participate. Since everyone in the target population does not possess an opportunity of being selected, nonprobabilistic samples cannot be used to compute confidence intervals around the sample estimates that possess some statistical likelihood of containing the true population values. Without such assurances, researchers will be unable to know how well the sample represents the target population. As a result, generalizations beyond the cases studies are no different than educated guesses about the nature of the population.Nonetheless, nonprobabilistic sampling methods can serve other objectives. They can be used to develop hypotheses or refine theories. They can serve to test various instrument designs. When participants are randomly assigned, they can also be used for experimental manipulations to demonstrate the potential effects of particular stimuli.The most commonly used forms of nonprobabilistic sampling are convenience sampling, self-selected sampling, Snowball Sampling, and quota sampling. In convenience sampling, the researcher selects individuals based on their accessibility, such as individuals passing a certain street corner or attending an event. In self-selected samples, individuals volunteer to participate at their convenience. Snowball Sampling uses referrals from individuals who initially agree to participate in a study to generate additional subjects. Quota sampling identifies characteristics germane to the study, such as race, gender, and party identification, and then selects available individuals until these goals are reached. As with probabilistic sampling, the choice of technique is based on resource availability and population characteristics.
eBook - PDFProgram Evaluation
An Introduction to an Evidence-Based Approach
- David Royse, Bruce Thyer, Deborah Padgett, , David Royse, Bruce Thyer, Deborah Padgett(Authors)
- 2015(Publication Date)
- Cengage Learning EMEA(Publisher)
For instance, Chung and Seo (2007) have reported on social adjustment and post-traumatic stress disorder among North Koreans who have defected to South Korea. A Snowball Sampling design works well for that study because it would be difficult for most investigators to locate North Korean defectors. However, people tend to have social networks with people like themselves and defectors may have knowledge of others like themselves. Another example of this would be Hoda, Kerr, Li, Montaner, and Wood ’ s (2008) study involving a snowball sample of injec-tion drug users who used jugular injection. Baltar and Brunet (2012) have described 214 Chapter 8 Copyright 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. the use of Facebook along with traditional Snowball Sampling to identify a specific sample of individuals. Maximum Variation, Deviant, and Extreme Case Sampling Maximum variation sampling is when the evaluator goes looking for extremes at either end of the range of client experiences. For instance, the evaluator may seek out the most successful clients or its most glorious failures. There ’ s no interest in the “ average ” client ’ s experience; the focus is on understanding the range of experi-ences. Going back to the example of patients in an outpatient mental health clinic, the evaluator might want to contact persons who had one treatment episode (and dropped out) as well as those who have had the highest number of counseling ses-sions. The idea is to look for what sets them apart — to see different perspectives from different kinds of individuals.
- Kumar, K Nirmal Ravi(Authors)
- 2021(Publication Date)
- Daya Publishing House(Publisher)
When sample is to be selected not based on that they are representative to the target population, but because they contribute requisite data and information. When the target population to be studied is difficult to locate or if some members are thought to be better (more knowledgeable, more willing to answer the questions framed in the schedule or questionnaire etc) than others to interview. Instead of interacting with a huge representative sample of the target population, it is better for the researcher to rely on few knowledgeable members. When a limited number of total population has the requisite information sought by the researcher. To collect lesser distorted data. d. Snowball Sampling When some of the sample units are difficult to locate, hard-to-reach and when sampling frame is not available. When information is to be elicited from the persons engaged in illicit or illegal activities say, drug users etc. When the data is to be collected from referral sample units guided by the initial sample e. Volunteer sampling When the researcher appeals to people to voluntarily participate in a survey. The sample respondents have a strong interest in the main topic of the survey and This ebook is exclusively for this university only. Cannot be resold/distributed. hence, they themselves become part of the research study. The sample is chosen by the respondents themselves and not by the survey administrator. This sampling is applicable, when the people who contribute to the survey might have different views and are much more interested in that topic than others who do not participate in the survey. No sampling frame is needed. f. Panel sampling When the same sample of respondents were asked for the same information again several times over a period of time. To analyze the trends in the observations made. g. Expert sampling When the respondents to be chosen in a non-random manner based on their expertise on the selected topic for investigation.
- Lisa M. Given(Author)
- 2008(Publication Date)
- SAGE Publications, Inc(Publisher)
But it can be just as difficult to draw probability samples in many non-hidden populations (such as cancer patients, non-governmental organizations, or media stories on crime, and so on). In those cases, the only option is to use a nonprobability sample. Sampling ——— 799 Among the most common ways of selecting non-probability samples are convenience samplin g, which accepts any eligible case that can be found; quota sam-pling, which specifies categories within the sample and states how many people should be included in each cat-egory; and Snowball Sampling, which uses an initial set of data sources as the basis for locating additional data sources. It is important to note that this list does not include purposive sampling because, as stated above, that is properly part of the process of defining the popu-lation rather than the process of selecting a sample from that population. In practice, almost all qualitative research does rely on nonprobability samples, but this has little connection to the use of purposive sampling. Instead, this reliance on nonprobability samples is often due to the difficulty of even locating data sources that meet eligibility criteria, let alone counting the total size of the population from which that sample is drawn. In addition, the need to collect detailed, in-depth data typ-ically leads to small sample sizes where there would be no point to doing statistical analysis. Thus, the common use of nonprobability samples in qualitative research matches an approach to data collection and analysis strategy that typically relies on the careful interpretation of a small number of very rich data sources. David L. Morgan See also Convenience Sample; Nonprobability Sampling; Population; Probability Sampling; Purposive Sampling; Quota Sampling; Random Sampling; Sample; Sample Size; Sampling Frame; Snowball Sampling; Stratified Sampling; Theoretical Sampling Further Readings Kalton, G. (1983). Quantitative applications in the social sciences: Vol.
- Stephen Gorard(Author)
- 2003(Publication Date)
- Continuum(Publisher)
Nineteenth-century psychol-ogy was often based on what researchers found out about themselves (introspection), while later twentieth-century psychology was chiefly based on what psychologists found out about each other. There are some hopeful signs that in the twenty-first century psychology is becoming more concerned with people at large. NON-PROBABILITY SAMPLES An implicit assumption has been made in the chapter so far that our sample will be what is termed a 'probability' sample, where cases will be selected either randomly or systematically. There are two good reasons for this focus. First: this kind of sampling is generally more technical than its alternatives, so requiring more explanation for a new researcher. Second: this kind of sampling is preferable in 72 Quantitative Methods in Social Science almost every way to any of its alternatives in all research situations. Thus, a simple guideline would be that probability samples should be used in all circumstances in which they are possible. A high-quality sample is crucial for safe generalization to take place (for high 'external validity'). Non-probability samples should therefore be reserved only for those projects in which there is no other choice. The most common and over-used form of non-probability sampling is the convenience sample, composed of those cases chosen only because they are easily available. A researcher standing in a railway station or shopping centre or outside a student union and stopping people in an ad hoc manner would thereby create a convenience sample and not a random one. This approach is often justified by the comment that a range of people use such places, so the sample will be mixed in composition. The approach is sometimes strengthened, for example in market 'research', by determining quotas for groups of cases (such as men and women) and then deselecting people (e.g. by not stopping them) once the quota for each group is filled.
No longer available |Learn more- Frederick J Gravetter; Lori-Ann B. Forzano; Tim Rakow, Frederick Gravetter, Frederick Gravetter, Lori-Ann Forzano, Tim Rakow(Authors)
- 2021(Publication Date)
- Cengage Learning EMEA(Publisher)
DEFINITIONS Sampling is the process of selecting individuals to participate in a research study. In probability sampling, the entire population is known, each individual in the population has a specifi- able probability of selection, and sampling occurs by a random process based on the probabilities. A random process is a procedure that produces one outcome from a set of possible outcomes. The outcome must be unpredictable each time, and the process must guarantee that each of the possible outcomes is equally likely to occur. In non-probability sampling, the population is not completely known, individual probabilities cannot be known, and the sampling method is based on factors such as commonsense or ease, with an effort to maintain representativeness and avoid bias. In the following sections, we discuss five probability sampling methods (simple random, systematic, stratified, proportionate stratified and cluster sampling) and two non-probability sampling methods (convenience and quota sampling). For each method, the general goal is to obtain a sample that is Copyright 2021 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. C H A P T E R 5 / Selecting Research Participants 116 representative of the population from which it is taken. For different kinds of research, however, the defi- nition of representative varies; hence, there are several well-defined sampling procedures that attempt to produce a particular kind of representation. L E A R N I N G C H E C K 1. Dr Near conducts an experiment on memory for individuals who are above the age of 65.
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