Medium Node: Reducing Error in Computerized Survey Data Collection

由于调查数据收集费用不断上升，研究人员对调查数据质量的关切日益增加，因此有必要制定创新的数据收集办法。 这项研究将侧重于改进通过计算机辅助方法收集的调查数据。 该项目将包括与互联网数据收集和计算机辅助电话访谈数据收集系统有关的研究，沿着这些计算机辅助数据收集系统的变体。 研究的总体目标是提高调查数据的质量，重点是实现三个目标。 首先，该研究将评估四种诊断工具的使用，以识别计算机辅助的，采访者管理的数据收集工具中的测量错误，并将使用这些研究结果来告知计算机辅助的，采访者管理的数据收集工具的共同特征的视觉重新设计。 其次，本研究将评估自适应/响应式设计的使用，其中收集的数据的动态建模用于修改问卷，因为数据正在收集。 第三，本研究将评估日历和时间日记为基础的数据收集方法的应用，以帮助在行为自我报告的准确性，通过定制的问题，以满足个人受访者的需求。 这些目标下的任务将是综合性的，使研究人员能够改进调查设计/工具，并通过减少访谈者和答卷人的负担来提高数据质量。 跨学科项目团队包括统计学、心理学、社会学、调查研究和方法学以及计算机科学方面的专家。 该团队将利用与行业领导者--盖洛普和Abt SRBI --以及人口普查局的长期合作和伙伴关系来实现其目标和目的。这项研究将推进调查方法和相关领域的科学知识。 减少调查数据中的测量误差对于从调查数据中准确推断至关重要，确定最大限度地减少或减少调查数据中测量误差的新工具将改进跨领域调查得出的结论。 除了促进调查数据收集方面的发现和理解外，该项目还将通过实施一项教育计划，为培训未来的调查研究专业人员做出重大贡献，该计划旨在：（1）从相关社会科学和统计学科中招募和留住一批以方法论为导向的多样化学生;（2）将研究纳入UNL的现有和新课程;（3）将研究纳入UNL的教育计划。（3）为所有参与研究的学生提供实践研究的机会;及（4）发展学生研究员的专业技能。 其结果将具有广泛的相关性，并将在多学科和跨学科会议和讲习班上向联邦统计机构和调查行业的研究人员广泛传播。 这项活动得到了NSF-人口普查研究网络资助机会的支持。

Rising costs of survey data collection and researchers' growing concerns about the quality of survey data necessitate the development of innovative approaches to data collection. This research will focus on improving survey data collected from computer-assisted methods. The project will include research related to internet data collection and computer-assisted telephone interviews (CATI) data collection systems, along with the variants of these computer-assisted data collection systems. With an overall goal of improving the quality of data derived from surveys, the research will focus on accomplishing three objectives. First, the study will evaluate the use of four diagnostic tools for identifying measurement errors in computer-assisted, interviewer-administered data collection instruments, and it will use these findings to inform visual redesign of common features of computer-assisted, interviewer-administered data collection instruments. Second, the study will evaluate the use of adaptive/responsive designs in which a dynamic modeling of collected data is used to modify the questionnaire as the data are being collected. Third, the study will evaluate the application of calendar- and time diary-based data collection methods to aid in the accuracy of behavioral self-reports by tailoring questions to the needs of individual respondents. The tasks under these objectives will be integrative, allowing researchers to improve survey designs/instruments and enhance data quality by reducing interviewer and respondent burden. The interdisciplinary project team includes experts in statistics, psychology, sociology, survey research and methodology, and computer science. The team will leverage long-term collaborations and partner with industry leaders -- Gallup and Abt SRBI -- as well as Census to accomplish its goal and objectives.This research will advance scientific knowledge in survey methodology and related fields. Reducing measurement errors in survey data is critical to accurate inference from survey data, and identifying new tools that minimize or reduce measurement errors in survey data will improve conclusions made from surveys across fields. In addition to advancing discovery and understanding in survey data collection, the project will contribute significantly to the training of future survey research professionals through implementation of an education plan designed to: (1) recruit and retain a diverse methodologically-oriented student pool from related social science and statistical disciplines; (2) integrate research into existing and new curricula at UNL; (3) provide all participating students with hands-on research opportunities; and (4) develop student researchers' professional skills. The results will be broadly relevant and broadly disseminated at multidisciplinary and interdisciplinary conferences and workshops, to the federal statistical agencies, and to researchers in the survey industry. This activity is supported by the NSF-Census Research Network funding opportunity.