Sparsely populated rural areas are susceptible to high levels of error in their data, making it difficult to examine patterns and trends across geographies. This article aims to advance research methods for rural researchers by offering guidelines for navigating high levels of error associated with the American Community Survey (ACS). The ACS presents a useful source of U.S. community level data for rural researchers to utilize in school–community research but not without its difficulties. The small population sizes of rural communities often translate to large margins of error in the data, presenting a degree of uncertainty in the actual measure. To illustrate challenges and best practice, the author conducts a case study of the relationship between the presence of schools and economic vitality of rural communities in Indiana using ACS data. The author demonstrates how to examine the error in the data, introduces options to reduce uncertainty, and ultimately, explains how to move forward with the data, working with the margin of error and acknowledging its presence in the analysis and results. This article offers suggestions and techniques to assist rural researchers in navigating ACS obstacles so that they might produce transparent results with as little uncertainty as possible.

中文翻译：

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农村研究人员美国社区调查 (ACS) 指南：解析使用二手数据进行农村研究的概念和技术方面☆

人口稀少的农村地区的数据很容易出现严重错误，因此很难检查跨地区的模式和趋势。本文旨在通过提供指导来解决与美国社区调查 (ACS) 相关的高水平错误，从而推进农村研究人员的研究方法。ACS 为农村研究人员提供了美国社区层面数据的有用来源，供其在学校社区研究中使用，但也存在困难。农村社区的人口规模较小，往往会导致数据误差较大，从而在实际测量中呈现一定程度的不确定性。为了说明挑战和最佳实践，作者使用 ACS 数据对印第安纳州学校的存在与农村社区的经济活力之间的关系进行了案例研究。作者演示了如何检查数据中的错误，介绍了减少不确定性的选项，最后解释了如何继续处理数据、处理误差幅度并承认其在分析和结果中的存在。本文提供了建议和技术来帮助农村研究人员克服 ACS 障碍，以便他们能够产生透明的结果，并且尽可能减少不确定性。