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A combinatorial optimization based sample identification method for group comparisons

机译:基于组合优化的样本识别方法用于群体比较

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摘要

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.
机译:研究人员经常面临不得不将他们的调查样本选择方法与收集实际样本的成本相协调的问题。抽样策略的适当论证对于确保有效,可靠和可概括的研究结果至关重要。本文提出了一种用于样本位置识别的组合优化方法。当研究人员出于比较目的而需要确定从中抽取非概率样本的站点时,这种方法是可行的。研究结果表明,在研究兴趣的背景下,使用组合优化方法可以根据预定的人口统计学变量使总体变化假设最小化。当确定从中抽取非概率样本的位置时,一项重要的要求是尽可能从最均匀的种群中抽取样本,以控制无关因素。与没有确定位置策略的标准便利样本相比,结果表明,所提出的组合优化方法最大程度地减少了种群变异性,从而降低了样本收集的成本。

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