Respond by Day 5 to two colleague’s post by suggesting an alternative sampling structure for their research question as well as an alternate way of selecting the sample. Please use the Learning Resources to support your answer.

Colleague 1 –
COLLAPSE
            In Week 4, the research question, How does the impact of kinship care on grandparents affect grandchildren in their care? may be structured using probability or nonprobability sampling structures. In a probability sampling structure, a proportionate stratified random sampling of 25 percent of 1000 children in a particular school district and 1000 seniors (250 children and 250 seniors) may suffice (Yegidis, Weinbach, & Myers, 2018). Strengths of a probability structure and a proportionate stratified random sampling are such that there is a likelihood any number of children and grandparents partaking in kinship care would be selected and the ratio of grandchildren to grandparents would be equal. Limitations may exist in terms of lack of diversity, especially if the sampling of participants was not taken in a culturally/ethnically/or racially diverse area. Gender may also present limitations as grandmothers and grandfathers may experience differing health issues, and differing genders in children may also respond to the impact of kinship care on their grandparents in a different manner. Additionally, random sampling would not guarantee grandparent and grandchild sampling.

            A nonprobability sampling structure would not use random selection, and may be more appropriate because, in contrast to the randomly selected participants in a probability sample, they may be excluded if they do not contribute to the understanding of the research problem (Yegidis, Weinbach, & Myers, 2018). Purposive sampling of grandchildren and grandparents could provide the unique perspectives needed to address the research problem, and it may help describe the wide variety of coping methods grandchildren use in kinship care (Yegidis, Weinbach, & Myers, 2018, p. 216). Samples could be selected from clinics and hospitals who treat grandparents with health conditions and from school counseling departments or teachers who encounter children in kinship care. This type of sampling structure appears to pose difficulty in privacy matters and overall sampling objectives. Though it may be more representative of whom the research problem is seeking to understand, it is not without serious issues in gathering participants and data.

References

Yegidis, B. L., Weinbach, R. W., & Myers, L. L. (2018). Research methods for social

            workers (8th ed.). New York, NY: Pearson.

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Colleague 2

In the case of random sampling, stratified random sampling and cluster random sampling are key techniques relevant and applicable in the pre-defined research question. For example, the random cluster sampling will adopt a process of selecting participants, especially in a vast and expansive geographical area. Precisely, it will be ideal in instances where the LGBT clients, as well as the counselors, consist of multiple elements such as cities, families, or learning institutions. On the other hand, simple random sampling will adopt a random number table or a lottery system in selecting LGBT clients and counselors. Conversely, convenience sampling, as well as purposive sampling, is techniques applicable in non-probability sampling (Etikan, Musa, & Alkassim, 2016). In specific, the convenience sampling will entail the collection of data from LGBT clients and counselors from a convenient location. On the other hand, purposive sampling will select participating LGBT clients and counselors based on their knowledge of the specific population.

Section Two

To obtain an ideal sample from the rest of the population, it is imperative to identify the ideal sample that will be representing the entire population. As a result, Both LGBT clients, as well as counselors, will be considered as participants as outlined in the pre-defined research study. In the selection of the LGBT clients and counselors, the proposed research study will adopt a stratified random sampling.

Section Three

            Probability sampling and non-probability sampling are some of the common techniques that are widely applicable in empirical quantitative research studies. On the one hand, a non-probability sampling technique refers to an approach in which the odds of any member of a member selected as a sample cannot be determined through calculation (Yegidis, Weinbach, & Myers, 2017). On the other hand, probability sampling entails the technique of selecting representatives from an expansive population through a theory-based approach of probability. For instance, for a participant to be part of the sample, one has to undergo random selection. However, there are many sub-categories in both the probability as well as non-probability sampling techniques (Bacher, 2019). Some of the key merits of non-probability sampling include the time and cost-effectiveness of the technique.

Furthermore, non-probability sampling is widely applicable, particularly in instances where probability sampling is impractical. Nonetheless, lack of depth, particularly in the representation of a population, is a major demerit of non-probability sampling. As opposed to non-probability sampling, some of the notable advantages of probability sampling include simplicity as well as the non-technical aspects of the approach (Sharma, 2017). However, the fair share of its disadvantages is evident in its ineffectiveness, especially in large populations.

References

Bacher, J. (2019). Probability and Nonprobability Sampling: Representative Surveys of hard-to-reach and hard-to-ask populations. Current surveys between the poles of theory and practice.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American journal of theoretical and applied statistics, 5(1), 1-4.

Sharma, G. (2017). Pros and cons of different sampling techniques. International Journal of Applied Research, 3(7), 749-752.

Yegidis, B. L., Weinbach, R. W., & Myers, L. L. (2017). Research methods for social workers. Pearson.

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