Best practices for studies using digital data donation.

Q1 Mathematics
Quality & Quantity Pub Date : 2025-01-01 Epub Date: 2024-10-08 DOI:10.1007/s11135-024-01983-x
Thijs C Carrière, Laura Boeschoten, Bella Struminskaya, Heleen L Janssen, Niek C de Schipper, Theo Araujo
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引用次数: 0

Abstract

Digital trace data form a rich, growing source of data for social sciences and humanities. Data donation offers an innovative and ethical approach to collect these digital trace data. In data donation studies, participants request a copy of the digital trace data a data controller (e.g., large digital social media or video platforms) collected about them. The European Union's General Data Protection Regulation obliges platforms to provide such a copy. Next, the participant can choose to share (part of) this data copy with the researcher. This way, the researcher can obtain the digital trace data of interest with active consent of the participant. Setting up a data donation study involves several steps and considerations. If executed poorly, these steps might threaten a study's quality. In this paper, we introduce a workflow for setting up a robust data donation study. This workflow is based on error sources identified in the Total Error Framework for data donation by Boeschoten et al. (2022a) as well as on experiences in earlier data donation studies by the authors. The workflow is discussed in detail and linked to challenges and considerations for each step. We aim to provide a starting point with guidelines for researchers seeking to set up and conduct a data donation study.

使用数字数据捐赠进行研究的最佳实践。
数字痕迹数据为社会科学和人文科学提供了丰富的、不断增长的数据源。数据捐赠为收集这些数字痕迹数据提供了一种创新且符合道德规范的方法。在数据捐赠研究中,参与者要求获得数据控制者(如大型数字社交媒体或视频平台)收集的有关他们的数字痕迹数据的副本。欧盟《通用数据保护条例》规定,平台有义务提供此类副本。接下来,参与者可以选择与研究人员共享(部分)该数据副本。这样,研究人员就可以在参与者的主动同意下获得感兴趣的数字痕迹数据。建立数据捐赠研究涉及多个步骤和注意事项。如果执行不力,这些步骤可能会威胁到研究质量。在本文中,我们介绍了一个建立稳健数据捐赠研究的工作流程。该工作流程基于 Boeschoten 等人(2022a)在数据捐赠总误差框架(Total Error Framework)中确定的误差源,以及作者早期数据捐赠研究的经验。我们对工作流程进行了详细讨论,并将其与每个步骤所面临的挑战和注意事项联系起来。我们的目标是为寻求建立和开展数据捐赠研究的研究人员提供一个起点和指导原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quality & Quantity
Quality & Quantity 管理科学-统计学与概率论
CiteScore
4.60
自引率
0.00%
发文量
276
审稿时长
4-8 weeks
期刊介绍: Quality and Quantity constitutes a point of reference for European and non-European scholars to discuss instruments of methodology for more rigorous scientific results in the social sciences. In the era of biggish data, the journal also provides a publication venue for data scientists who are interested in proposing a new indicator to measure the latent aspects of social, cultural, and political events. Rather than leaning towards one specific methodological school, the journal publishes papers on a mixed method of quantitative and qualitative data. Furthermore, the journal’s key aim is to tackle some methodological pluralism across research cultures. In this context, the journal is open to papers addressing some general logic of empirical research and analysis of the validity and verification of social laws. Thus The journal accepts papers on science metrics and publication ethics and, their related issues affecting methodological practices among researchers. Quality and Quantity is an interdisciplinary journal which systematically correlates disciplines such as data and information sciences with the other humanities and social sciences. The journal extends discussion of interesting contributions in methodology to scholars worldwide, to promote the scientific development of social research.
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