EAGER: Identifying Methodological and Ethical Challenges in Online Research of Hard-to-Reach Populations during the COVID-19 pandemic

EAGER：识别 COVID-19 大流行期间难以接触人群在线研究的方法和伦理挑战

• 批准号: 2126469
• 负责人: Yeon Jung Yu
• 金额: $ 2.99万
• 依托单位: Western Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126469&HistoricalAwards=false
• 关键词: EAGER; Identifying; Methodological; Ethical; Challenges

The COVID-19 pandemic has necessitated the retooling of techniques for collecting human behavioral data, in ways that present new challenges to the reliability and validity of that data. But it has also provided an opportunity for innovating methods of data collection and analysis, particularly with respect to the analysis of social networks. This project pilots a new methodological technique, asking whether digital platforms have a higher degree of efficacy in engaging more vulnerable populations. Findings from this research will disseminated in a way that aims to benefit public health efforts. This scientific study will also broaden the participation of the underrepresented groups in the co-production of scientific knowledge. The broader scientific question proposed is whether valid and reliable data can be gathered virtually from a marginalized population. It pilots a new digital ethnographic method for identifying social networks among marginalized populations by coupling the social network mapping of personal (egocentric) social networks with qualitative data collection methods (semi-structured interviews that are focused on ensuring data valid through the building of rapport between research participants and the research team, and observational techniques within social media and other online platforms). If the combination of social network analysis (SNA) and digital ethnographic techniques can reliably yield valid data for marginalized/stigmatized populations, digital venues may prove to be a safer and more efficient space for engaging with vulnerable populations, which in turn may potentially allow for the rapid accumulation of data of hard-to-reach populations. This exploratory project intends to make methodological contributions to social science research methods in regards to studying hidden/marginalized populations through the identification of network properties, cluster boundaries, and the social spaces of various marginalized groups. As a result, this project will enhance the intellectual debates on social network theories and methodology, and may come to serve as a model by providing alternative research methods to study hard-to-reach populations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

COVID-19大流行需要重新调整收集人类行为数据的技术，这对数据的可靠性和有效性提出了新的挑战。但它也为数据收集和分析方法的创新提供了机会，特别是在分析社交网络方面。该项目试点了一种新的方法技术，询问数字平台是否在吸引更多弱势群体方面具有更高的效率。这项研究的结果将以有利于公共卫生工作的方式传播。这项科学研究还将扩大代表性不足的群体参与科学知识的共同生产。提出的更广泛的科学问题是，是否可以从边缘化人口中收集有效和可靠的数据。它试行了一种新的数字民族志方法，通过将个人（以自我为中心）社交网络的社会网络绘图与定性数据收集方法（半结构化访谈，侧重于通过在研究参与者和研究团队之间建立融洽关系来确保数据有效，以及社交媒体和其他在线平台内的观察技术）相结合，来确定边缘化人群中的社交网络。如果社会网络分析和数字人种学技术的结合能够可靠地为边缘化/污名化人群提供有效数据，那么数字场所可能会成为与弱势人群接触的更安全、更有效的空间，这反过来又可能使难以接触到的人群的数据得以快速积累。该探索性项目旨在通过识别网络属性，集群边界和各种边缘化群体的社会空间，为研究隐藏/边缘化群体的社会科学研究方法做出方法学贡献。因此，该项目将加强对社会网络理论和方法论的学术讨论，并可能成为研究难以接触人群的替代研究方法的典范。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。