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检测虚假身份:一种改进基于网络的领导力与健康/幸福状况调查及研究的解决方案。

Detecting false identities: A solution to improve web-based surveys and research on leadership and health/well-being.

作者信息

Bernerth Jeremy B, Aguinis Herman, Taylor Erik C

机构信息

Department of Management.

出版信息

J Occup Health Psychol. 2021 Dec;26(6):564-581. doi: 10.1037/ocp0000281. Epub 2021 Jul 22.

Abstract

A challenge for leadership and health/well-being research and applications relying on web-based data collection is false identities-cases where participants are not members of the targeted population. To address this challenge, we investigated the effectiveness of a new approach consisting of using internet protocol (IP) address analysis to enhance the validity of web-based research involving constructs relevant in leadership and health/well-being research (e.g., leader-member exchange [LMX], physical [health] symptoms, job satisfaction, workplace stressors, and task performance). Specifically, we used study participants' IP addresses to gather information on their IP threat scores and internet service providers (ISPs). We then used IP threat scores and ISPs to distinguish between two types of respondents: (a) targeted and (b) nontargeted. Results of an empirical study involving nearly 1,000 participants showed that using information obtained from IP addresses to distinguish targeted from nontargeted participants resulted in data with fewer missed instructed-response items, higher within-person reliability, and a higher completion rate of open-ended questions. Comparing the entire sample against targeted participants showed different mean scores, factor structures, scale reliability estimates, and estimated size of substantive relationships among constructs. Differences in scale reliability and construct mean scores remained even after implementing existing procedures typically used to compare web-based and nonweb-based respondents, providing evidence that our proposed approach offers clear benefits not found in data-cleaning methodologies currently in use. Finally, we offer best-practice recommendations in the form of a decision-making tree for improving the validity of future web-based surveys and research in leadership and health/well-being and other domains. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

摘要

对于依赖基于网络的数据收集的领导力与健康/幸福研究及应用而言,一个挑战是虚假身份——即参与者并非目标人群成员的情况。为应对这一挑战,我们调查了一种新方法的有效性,该方法包括使用互联网协议(IP)地址分析,以提高涉及领导力与健康/幸福研究中相关构念(例如领导-成员交换[LMX]、身体[健康]症状、工作满意度、工作场所压力源和任务绩效)的基于网络的研究的有效性。具体而言,我们使用研究参与者的IP地址来收集有关其IP威胁分数和互联网服务提供商(ISP)的信息。然后,我们使用IP威胁分数和ISP来区分两类受访者:(a)目标受访者和(b)非目标受访者。一项涉及近1000名参与者的实证研究结果表明,使用从IP地址获得的信息来区分目标参与者和非目标参与者,会得到遗漏的指令响应项目更少、个体内信度更高且开放式问题完成率更高的数据。将整个样本与目标参与者进行比较,结果显示了不同的平均分数、因子结构、量表信度估计以及构念之间实质性关系的估计大小。即使在实施了通常用于比较基于网络和非基于网络的受访者的现有程序之后,量表信度和构念平均分数的差异仍然存在,这表明我们提出的方法提供了现有数据清理方法中未发现的明显优势。最后,我们以决策树的形式提供最佳实践建议,以提高未来基于网络的调查以及领导力与健康/幸福及其他领域研究的有效性。(PsycInfo数据库记录(c)2022美国心理学会,保留所有权利)

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