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The sampling design is a fundamental part of data collection for scientifically based decision making As mention in the book of research method a sample design is “decisions have to be made as the type of sample to be used”(Sekaran, 2003, 147). A well-developed sampling design plays a critical role in ensuring that data are sufficient to draw the conclusions needed.
“When elements in the population have a known chance of being chosen as subjects in the sample, we resort to a probability sampling design. Probability sampling can be either unrestricted (simple random sampling) or restricted (complex probability sampling) in nature.” (Sekaran, 2003, 267)
These data consist of a random sample of 100 persons of the CPS (current population data survey) for weekly earnings of wage and salary workers. The data that we gather includes information on wages and some characteristics of the workers such as sex, numbers of years of education, years of work experiences, occupational status, region of residence and union membership.
Our sample type is random sample. We chose this type because every person in the population had an equal opportunity to appear in the sample, any person can be selected besides age, race, or wage. “In random sampling, each item or element of the population has an equal chance of being chosen at each draw. A sample is random if the method for obtaining the sample meets the criterion of randomness (each element having an equal chance at each draw). The actual composition of the sample itself does not determine whether or not it was a random sample.” (Australian Bureau of Statistic [ABS], 2007, 1)
The sample size was not appropriate for this research because it is a very small population. According to what I read in the book research methods for business samples that are to small may lead to inaccurate results, but at the same time samples that are to large may waste a lot of time. “Use a sample large enough so that we can...