SAMPLING

This research examines an application of the statistical procedure of population sampling. The initial part of the examination describes sampling procedures, and illustrates the use of the procedures in an application. Following the description and illustration, the accuracy and appropriateness of the application is discussed.

Description of the Procedure, and An Illustration of the Use of the Procedure in An Application

Population sampling procedures are described in this section. This description is followed by an illustration of the use of the procedures in an application.

Classical statistical analysis is concerned with the analysis of sampled data. The analysis of sampled data permits the researcher to make inferences concerning total populations, "with the exclusions of any personal judgments or opinions" (Kazmier, 1991, p. 1). Random or equivalent procedures are employed to eliminate bias to the extent possible from the research sample in formal scholarly research. Random procedures are mathematically-based actions that assure that every member of a population to be sampled has a chance equal to that of every other member of the population to be selected as a part of the sample. In formal scholarly research, the research design is carefully planned and followed to the maximum possible extent. Control of extraneous variables is an important feature of the research design. The application of sampling procedur

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be normally distributed no matter what the distribution in the population is like, provided only that the samples are large enough. In most instances where inferential statistics are applied in hypothesis testing, population distributions are unknown. Therefore, the central limit theorem assumes a high-level of importance in hypothesis testing. The principal techniques of statistical inference are interval estimation and hypothesis testing (Ott, 1993, p. 125). Although statistical inference techniques are directly concerned with estimating values or with the testing of hypotheses concerning population parameters, the results of these procedures are related to alternative courses of action or to alternative decisions. As an example, the acceptance of a null hypothesis (or the inability to reject a null hypothesis) in statistical inference analysis which indicated that the criminal behavior reduction potential of a proposed policy is less than the potential for an alternative policy would also typically be associated with a decision relative to the implementation of the proposed policy. Illustration of Use in An Application To investigate the relationship between role ambiguity and role conflict, as independent variables, a

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