引入新的方法来识别欺诈性答案 (采样与西西弗斯):基于网络的LGBTQ2S+混合方法研究
Kinnon Ross MacKinnon1,2, Naail Khan3, Katherine M Newman3
1School of Social Work, York University, Toronto, ON, Canada.
Journal of medical Internet research
|March 17, 2025
概括
这项研究成功招募了950多名在LGBTQ2S+社区内探索转变的个人. 它开发了一种新的方法,以确保数据完整性,并了解各种经验.
科学领域:
- 社会学 社会学 社会学
- 心理学 心理学 心理学
- 性别研究是性别研究.
背景情况:
- 脱离是LGBTQ2S+群体中一个新兴的体验,需要敏感的研究.
- 转型研究的挑战包括定义现象和招募参与者.
- 面对生活挑战的弹性是一个关键主题.
研究的目的:
- 为研究LGBTQ2S+人群中与转变相关的现象提出采样和招募方法.
- 引入一种新的协议,用于识别和删除机器人,诈骗和不合格的调查回复.
- 介绍研究样本的初步社会人口统计结果.
主要方法:
- 向615多个组织和提供者分发了英语,法语和西班牙语的学习传单.
- 在5个社交媒体平台上推广付费广告,达到超过770万个帐户.
- 开发了一个系统的协议来识别和排除欺诈性回复,包括虚拟选.
主要成果:
- 957名符合条件的参与者 (69.5%) 从1377个初始响应中被纳入分析数据集.
- 样本的平均年龄为25.87岁,主要是白人 (78.7%) 和居住在美国 (73.6%).
- 超过一半的人报告多性取向 (56.8%);33.2%的人认定为非二进制,43.2%为跨性别者,40.5%为变性.
结论:
- 尽管面临招聘挑战,但获得了超过950名符合条件的参与者的异质样本.
- 该研究引入了一种创新的策略,用于采样难以接触的,配资值得的群体.
- 开发的方法有效地消除了欺诈性回复,提高了数据可靠性.
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