Statistical modeling using mouse movements to model measurement error and improve data quality in web surveys

使用鼠标移动进行统计建模，对测量误差进行建模并提高网络调查中的数据质量

• 批准号: 396057129
• 负责人: Professorin Dr. Sonja Greven
• 金额: --
• 依托单位: Universitätsbibliothek Mannheim
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/396057129?language=en
• 关键词: Statistical; modeling; using; mouse; movements

The overarching goal of this project is the improvement of web survey data collection by detecting issues in the instrument and on part of individual participants. In the first funding period, we have demonstrated the usefulness of mouse-tracking paradata for this purpose based on a case study, and developed sophisticated techniques for their analysis. In this continuation, we aim to further develop and extend these methods, and evaluate their robustness in the field. We will take a two-pronged interdisciplinary approach, building on our prior work and experience. The first strand of work packages concerns the statistical methodology for analyzing the multidimensional time-series that make up interaction paradata. Here, we will extend our analysis methods beyond mouse movements alone to handle all observable user interactions (including, but not limited to, keyboard input, touchscreen data, and further device information), apply deep learning to uncover yet-undiscovered features that go beyond our manually defined indicators, and investigate heterogeneity between user groups. As a second, independent but closely linked, survey research strand, we will apply and evaluate our old and newly developed methods in the field, investigate how they generalize to larger and more diverse samples, and guide the methods development to meet the requirements of survey practitioners. In addition, we will assess our methods’ potential to detect not only hard questions, but inattentive and fraudulent respondents, addressing two pertinent issues facing survey providers. Lastly, we will supply survey practitioners with best practices for collecting this class of data, through development and testing of consent procedures. In parallel, we will create data collection and processing infrastructure to underpin our efforts, and that we will make available to researchers as open-source software with added documentation, tutorials and learning materials. Throughout both strands, we will work closely with project partners who maintain survey panels, and have established collaborations with survey providers to ensure that our plans are feasible. Together, we will create a new dataset that will aid our and others’ future investigations of paradata.

该项目的总体目标是通过检测仪器和个人参与者的问题来改进网络调查数据收集。在第一个资助期间，我们基于案例研究证明了鼠标跟踪参数数据的有用性，并开发了复杂的分析技术。在此延续中，我们的目标是进一步开发和扩展这些方法，并评估它们在该领域的稳健性。我们将在我们之前的工作和经验的基础上，采取双管齐下的跨学科方法。工作包的第一部分涉及用于分析构成交互参数数据的多维时间序列的统计方法。在这里，我们将把我们的分析方法扩展到仅鼠标移动之外，以处理所有可观察的用户交互（包括但不限于键盘输入、触摸屏数据和进一步的设备信息），应用深度学习来发现超出我们手动定义指标的尚未发现的特征，并调查用户组之间的异质性。作为第二个独立但紧密联系的调查研究分支，我们将在该领域应用和评估我们新开发的旧方法，研究它们如何推广到更大、更多样化的样本，并指导方法开发以满足调查从业者的要求。此外，我们将评估我们的方法不仅可以检测困难问题，还可以检测粗心和欺诈性受访者的潜力，解决调查提供者面临的两个相关问题。最后，我们将通过开发和测试同意程序，为调查从业者提供收集此类数据的最佳实践。与此同时，我们将创建数据收集和处理基础设施来支持我们的工作，并将以开源软件的形式向研究人员提供，并添加文档、教程和学习材料。在这两个方面，我们将与维护调查小组的项目合作伙伴密切合作，并与调查提供商建立合作关系，以确保我们的计划可行。我们将共同创建一个新的数据集，以帮助我们和其他人未来对平行数据的研究。