Survey Research Center, Institute for Social Research, University of Michigan-Ann Arbor, Ann Arbor, Michigan, United States of America.
PLoS One. 2023 Aug 4;18(8):e0289695. doi: 10.1371/journal.pone.0289695. eCollection 2023.
In the United States, increasing access to the internet, the increasing costs of large-scale face-to-face data collections, and the general reluctance of the public to participate in intrusive in-person data collections all mean that new approaches to nationally representative surveys are urgently needed. The COVID-19 pandemic accelerated the need for faster, higher-quality alternatives to face-to-face data collection. These trends place a high priority on the evaluation of innovative web-based data collection methods that are convenient for the U.S. public and yield scientific information of high quality. The web mode is particularly appealing because it is relatively inexpensive, it is logistically flexible to implement, and it affords a high level of privacy and confidentiality when correctly implemented. With this study, we aimed to conduct a methodological evaluation of a sequential mixed-mode web/mail data collection protocol, including modular survey design concepts, which was implemented on a national probability sample in the U.S. in 2020-2021. We implemented randomized experiments to test theoretically-informed hypotheses that 1) the use of mail and increased incentives to follow up with households that did not respond to an invitation to complete a household screening questionnaire online would help to recruit different types of households; and 2) the use of modular survey design, which involves splitting a lengthy self-administered survey up into multiple parts that can be completed at a respondent's convenience, would improve survey completion rates. We find support for the use of mail and increased incentives to follow up with households that have not responded to a web-based screening questionnaire. We did not find support for the use of modular design in this context. Simple descriptive analyses also suggest that attempted telephone reminders may be helpful for the main survey.
在美国，互联网的普及、大规模面对面数据收集成本的增加，以及公众普遍不愿意参与侵入性的面对面数据收集，所有这些都意味着迫切需要新的方法来进行全国代表性调查。COVID-19 大流行加速了对更快、更高质量的面对面数据收集替代方案的需求。这些趋势高度重视评估创新的基于网络的数据收集方法，这些方法方便美国公众使用，并产生高质量的科学信息。网络模式特别有吸引力，因为它相对便宜，在实施方面具有灵活的后勤保障，并且在正确实施时提供高水平的隐私和保密性。在这项研究中，我们旨在对 2020-2021 年在美国全国概率样本中实施的顺序混合模式网络/邮件数据收集协议进行方法评估，包括模块化调查设计概念。我们实施了随机实验，以检验理论上有依据的假设，即 1）使用邮件和增加对未响应在线家庭筛选问卷邀请的家庭进行后续跟进的激励措施，将有助于招募不同类型的家庭；2）使用模块化调查设计，即将冗长的自我管理调查分成多个部分，以便受访者在方便的时候完成，将提高调查完成率。我们发现，使用邮件和增加对未响应网络筛选问卷的家庭进行后续跟进的激励措施是有帮助的。在这种情况下，我们没有发现模块化设计的使用支持。简单的描述性分析还表明，尝试电话提醒可能对主调查有帮助。
