Discussion Post: Probability and Nonprobability Sampling
A sample is a finite part of a population whose properties are studied to gain information about the whole. Survey researchers define a sample as a set of respondents (people) selected from a larger population for the purpose of a survey. There are two major sampling methods: probability and nonprobability. Probability sampling includes any method of sampling that utilizes random selection. This is meant to ensure that each element of the population has at least a probability of being in the sample. In nonprobability sampling, the opposite holds. One popular misconception is that probability sampling is ideal and optimal (and thus superior to nonprobability sampling), but this is not necessarily true. In fact, data from an optimal nonprobability-sampling scheme is preferred over data from a poorly executed probability scheme. Information that you gather during the literature review of a research study can help you decide which strategy would be optimal for your study.
To prepare for this Discussion, consider advantages of probability sampling over nonprobability sampling. Then select which sampling strategy you intend to use for your Project, and think about how you would empirically justify your sampling related to representation, reliability, and validity. Make sure you include specific applied examples and empirical citations.
Post: an explanation of two advantages of using probability sampling over nonprobability sampling. Then explain which sampling strategy you intend to use for your Research Prospal including empirical justification for your sampling related to representation, reliability, and validity.
The response should include a reference list. Using one-inch margins, Times New Roman 12 pnt font, double-space and APA style of writing and citations.