Job Satisfaction Survey Research Studies,

Job Satisfaction Survey

Research studies, whether they are clinical trial based, experimental, case study designed, or descriptive, must exhibit and command interest, enthusiasm, and passionate commitment. The research investigator must catch the essential quality of the excitement of discovery that comes from research well done if expected results are to be gained. To this end the researcher is bound by a very stringent protocol for the development of all research endeavors. Not only must the investigator clearly define the research problem but must also plainly state a research question, followed by a testable research null hypothesis. Contained within the format of the research purpose, question, and hypothesis are various inherent constraints that will alert the reader as to the investigator's knowledge of, and adherence to, those tenets that make for sound, credible, and purposeful research (Ohlson, 1998). Included in the aforementioned three research requirements are statements of, and a rational for the use of, specifically chosen variables (independent and dependent), measurement or data assessment tools, statistical data analysis techniques, and potential error sources. Wherein most research fails to deliver scientific information for the advancement of content knowledge is in the area of study error. The remainder of this report will examine one source of possible error, namely that associated with sampling. Once sampling theory has been examined the garnered information will be applied to a specific study using a job satisfaction survey.

Sampling. The most succinct and effective way to view research sampling is to look at the process as being a part of a whole that represents a larger connection (Ohlson, 1998). Briefly defined sampling is the taking any portion of a population or universe, as representative of that population or universe. Sampling alone can skew testing results, infuse uncontrollable error into a statistical process, and violate the empirical premise under which a phenomenon is being evaluated and assessed. As pointed out in the Lacy, et al. article (Winter, 2001) in order for accurate conclusions to be drawn about any testable situation there must exist an adequate sample size as well as employing an appropriate sampling structure. Further, according to the authors a sample drawn at random is an unbiased sample in the sense that no member of the population has a greater chance of being selected than any other member. At the same time non-random samples are, unfortunately, over or under-representations of a population and have a tendency to skew measurement results. There, are, however, research situations wherein randomization is not possible but this does not necessarily negate the research effort. The implication behind non-randomization is that the results of the study, whether they are differences, relationships, or effects, cannot be levied against a greater population. The results must be solely directed toward the sample itself. In other words, generalizations that a population will mirror the sample results is not possible - even though many researchers commit this particular research protocol transgression.