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).

中文翻译：

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检测虚假身份：改进基于网络的领导力和健康/福祉调查和研究的解决方案。

依赖基于网络的数据收集的领导力和健康/福祉研究和应用面临的挑战是虚假身份——参与者不是目标人群的成员。为了应对这一挑战，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) 地址分析来提高基于网络的研究的有效性，这些研究涉及与领导力和健康/福祉研究相关的结构（例如，领导者-成员交换 [LMX]、身体 [健康] 症状、工作满意度、工作压力源和任务绩效）。具体来说，我们使用研究参与者的 IP 地址来收集有关其 IP 威胁评分和互联网服务提供商 (ISP) 的信息。然后，我们使用 IP 威胁评分和 ISP 来区分两种类型的受访者：(a) 目标和 (b) 非目标。一项涉及近 1,000 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导致数据遗漏的指示回答项目更少，个人内部可靠性更高，开放式问题的完成率更高. 将整个样本与目标参与者进行比较显示了不同的平均分数、因子结构、量表可靠性估计以及构造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，量表可靠性和构造平均分数的差异仍然存在，这证明我们提出的方法提供了当前使用的数据清理方法中没有的明显好处。最后，我们以决策树的形式提供最佳实践建议，以提高未来在领导力、健康/福祉和其他领域进行的基于网络的调查和研究的有效性。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。