Determining Qualitative Geographic Sample Size in the Presence of Spatial Autocorrelation

本研究将探讨具有相似属性的个体在地理空间上聚集的趋势对定性研究样本量的影响。 样本量是实证科学研究项目中的一个重要特征，影响定量研究中的可推广性和操纵/控制以及定性研究中的可转移性和发现。 选择最少数量的观测有助于确保抽样策略的充分性。 样本量不足会损害研究结果的可靠性和/或浪费资源。 由于具有相似属性的个体倾向于在地理空间中聚集而不是随机分布，这一特征被称为空间自相关，因此在设计研究项目时必须特别小心。 本项目将寻求在空间自相关的情况下确定定性研究的适当样本量。 该项目的理论基础借鉴了托马斯谢林的诺贝尔奖获奖作品，并将其应用于地理空间环境。 研究人员将使用一组模拟实验来评估指标，这些指标可用作确定一组不同抽样方法的适当样本量的指南。 他们还将进行一个定性抽样项目，以评估计算机实验室的结果与真实的世界的实地工作。这个研究项目将提高对空间自相关性进行定性研究的样本量确定的理解。 该项目将解决有争议的问题，定性样本量的确定在更一般的文献，补充什么是已知的定量样本量的确定，提供数值证据选择一个定性的样本量，并使定性研究人员更加意识到空间自相关对他们的研究的影响。 该项目将提供一个新的工具，以加强定性空间研究的严谨性，从而有助于使其执行更有效，其结果更广泛地接受。 该项目将为确定定性研究样本量提供数值证据，并将帮助定性空间研究人员更好地了解空间自相关如何使他们的数据收集复杂化并影响他们的研究结果。 该项目还将产生一个使用这种新的空间信息定性抽样方法的真实世界的例子。

This research project will examine impacts of the tendency for individuals with similar attributes to cluster in geographic space on the sample size of a qualitative research study. Sample size is a crucial feature in empirical scientific research projects, affecting generalizability and manipulation/control in quantitative studies and transferability and discovery in qualitative studies. Selecting a minimum number of observations helps ensure the adequacy of a sampling strategy. An insufficient sample size can undermine the soundness of research findings and/or waste resources. Because of the tendency for individuals with similar attributes to cluster in geographic space rather than to be distributed randomly, a characteristic known as spatial autocorrelation, special care must be taken in the design of research projects. This project will seek to determine the appropriate sample size for qualitative research in the presence of spatial autocorrelation. The theoretical foundations of this project draw on Thomas Schelling's Nobel Prize-winning work, adapting it for use in a geospatial context. The investigators will use a set of simulation experiments to assess metrics that can be used as guidelines for determining appropriate sample sizes for a set of different sampling approaches. They also will conduct a qualitative sampling project to evaluate the computer laboratory findings with real world field work.This research project will advance understanding of sample size determination for qualitative research conducted in the presence of spatial autocorrelation. The project will address the controversial issue of qualitative sample size determination in the more general literature, supplement what is known for quantitative sample size determination, furnish numerical evidence for selecting a qualitative sample size, and make qualitative researchers more aware of spatial autocorrelation impacts on their research. The project will provide a new tool to bolster the rigor of qualitative spatial research, thereby helping to make its execution more efficient and its findings more widely acceptable. The project will contribute numerical evidence for determining qualitative research sample size, and it will help qualitative spatial researchers better understand how spatial autocorrelation complicates their data collection and impacts their findings. The project also will yield a real-world example of the use of this new spatially informed qualitative sampling method.