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Probability and Nonprobability Sampling

Probability and Nonprobability Sampling

Probability and Nonprobability Sampling

The research question I proposed in week 4 was surrounding the impact of assessment tools on how effectively counseling would benefit the parents and youth to avoid youth delinquency crimes. In order to develop a sampling structure, I may refine the question of why youth delinquency face so many challenges with being viewed as a criminal by the courts. A stratified probability sample of more than 500 courts was constructed. Each court was asked to provide annual aggregate counts of the number of delinquency cases it handled. The statistical integrity of the Children’s Bureau sample was difficult to maintain. After a decade, the project adopted a policy of collecting annual case counts from any court that could provide them. National estimates were then generated from this nonprobability sample.

Qualitative research deals with a phenomenon (Yegidis, 2018), and because of that, when selecting a nonprobability sampling, I will go with a group of youth delinquency who determine the challenges that led to dropping out of school. This is the best route to take when going through nonprobability sampling because Yegidis, (2018) tells us that nonprobability sampling does not involve random selections and several nonprobability samplings can be used to do research.

The advantage of simple random sampling reduces bias. Yegidis, Weinbach, & Myers (2018) noted, “Stratified random sampling is another probability sampling method, and can be used to reduce the amount of sampling error when using a simple or systematic random sample or to ensure that there are enough cases within different value or value label categories of a variable compassion purposes” (p.213). You can get a good representation of the population you are trying to study. A disadvantage of this structure is the time it possibly could consume. The advantages of purposive sampling can help or give a researcher justification to make general views on a particular group or sample being studied. It can also help determine if further inquiry is worth it. Some of the disadvantages allow it to be subject bias. It can also be hard to defend.

For this study, purposive sampling should be used because it will provide the researchers with a unique insight, individual perspective, and understanding of the participants and what they are trying to understand. (Yegidis, 2018). The purposive sampling technique is a type of non-probability sampling that is most effective when one needs to study a certain cultural domain with knowledgeable experts within. Probability sampling uses random selection and it is a generalization of findings to a research question. Researchers will find that using cluster sampling under probability sampling will be useful for this study because they will have the opportunity to use large-skill surveys or studies that can come from any part of the world. Yegidis, 2018).

Like every research method, there are limitations, and for using probability sampling, researchers will find that just studying random people makes it hard for them to work together and often will not get the right information they are looking for. The strength of probability sampling is it is convenient and easy to use. Nonprobability sampling strength is its’ effectiveness, and it brings about excellent results because it is done with a controlled population. A limitation of nonprobability sampling is, if there is an error, the researchers might not know.


Plummer, S.-B., Makris, S., Brocksen, S. (Eds.). (2014). Social work case studies: Foundation Year. Baltimore, MD: Laureate International Universities Publishing. [Vital Source e-reader]. Social Work Research: Couples Counseling

Yegidis, B. L., Weinbach, R. W., & Meyers, L. L. (2018). Research methods for social workers (8th ed.). Upper Saddle River, NJ: Allyn


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Probability and nonprobability are the two general categories of sampling.

Probability and Nonprobability Sampling

Probability and Nonprobability Sampling

Probability sampling uses random selection, whereas nonprobability sampling does not. For example, if you wanted to study the effects of divorce on the psychological development of adolescents, you could gather a population of a certain number of adolescents whose parents were divorced.

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