Marquette University, Milwaukee, WI, USA.
West J Nurs Res. 2023 Jan;45(1):93-99. doi: 10.1177/01939459221098468. Epub 2022 May 19.
The internet offers exciting opportunities for quick, cost-efficient, and widespread recruitment and data collection without face-to-face contact. Previous research has demonstrated success in reaching population subgroups not typically included in traditional recruitment methods, yet challenges in data quality protection remain paramount. This article describes using Amazon Mechanical Turk, Facebook groups, and email distribution lists to recruit older adults who live alone for a quantitative study using a cross-sectional online survey. Fraudulent survey takers became a major concern in this study, and a protocol was developed to identify and exclude suspicious data. Of 738 recorded participants, 117 responses were retained in the final sample. The majority of sham responses were collected from Facebook with the fewest number of issues identified in responses collected via targeted emailing. Implications for survey design, data analysis, and future research are discussed.
互联网为快速、经济高效且广泛的招募和数据收集提供了令人兴奋的机会,无需面对面接触。先前的研究已经证明,在接触传统招募方法通常不包括的人群方面取得了成功,但数据质量保护方面的挑战仍然至关重要。本文描述了使用 Amazon Mechanical Turk、Facebook 群组和电子邮件分发列表来招募独自居住的老年人,以进行一项使用横断面在线调查的定量研究。在这项研究中,欺诈性的调查参与者成为一个主要问题,因此制定了一项协议来识别和排除可疑数据。在记录的 738 名参与者中,有 117 份回复被保留在最终样本中。大部分虚假回复是通过 Facebook 收集的,而通过有针对性的电子邮件收集的回复中发现的问题最少。讨论了对调查设计、数据分析和未来研究的影响。