A combinatorial optimization based sample identification method for group comparisons

Robyn L. Raschke, Anjala S. Krishen, Pushkin Kachroo and Pankaj Maheshwari

Journal of Business Research, 2013, vol. 66, issue 9, 1267-1271

Abstract: 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.

Keywords: Sample identification; Sample selection; Sample location identification; Nonprobability samples (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:66:y:2013:i:9:p:1267-1271

DOI: 10.1016/j.jbusres.2012.02.024

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