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 Final 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.
With these thoughts in mind:
Post an explanation of two
advantages of using probability sampling over nonprobability sampling.
Then explain which sampling strategy you intend to use for your Final
Project, including empirical justification for your sampling related to
representation, reliability, and validity.
Be sure to support your postings and responses with specific references to the Learning Resources.
Read a selection of your colleagues’ postings.