A Combinatorial Optimization Based Sample Identification Method for Group Comparisons
Journal of Business Research
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Researchers often face having to reconcile their sample selection method of survey with the costs of collecting the actual sample. An appropriate justification of a sampling strategy is central to ensuring valid, reliable, and generalizable research results. This paper presents a combinatorial optimization method for identification of sample locations. Such an approach is viable when researchers need to identify sites from which to draw a nonprobability sample when the research objective is for comparative purposes. Findings indicate that using a combinatorial optimization method minimizes the population variation assumptions based upon predetermined demographic variables within the context of the research interest. When identifying the location from which to draw a nonprobability sample, an important requirement is to draw from the most homogeneous populations as possible to control for extraneous factors. In comparison to a standard convenience sample with no identified location strategy, results indicate that the proposed combinatorial optimization method minimizes population variability and thus decreases the cost of sample collection.
Nonprobability samples; Sample identification; Sample location identification; Sample selection; Sample location identification
Electrical and Computer Engineering | Engineering | Systems and Communications
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Raschke, R. L.,
Krishen, A. S.,
A Combinatorial Optimization Based Sample Identification Method for Group Comparisons.
Journal of Business Research, 66(9),