Psychol Methods. 2019 Feb;24(1):53. doi: 10.1037/met0000213.
Reports an error in "Parameter uncertainty in structural equation models: Confidence sets and fungible estimates" by Jolynn Pek and Hao Wu (, 2018[Dec], Vol 23[4], 635-653). In the article "Parameter Uncertainty in Structural Equation Models: Confidence Sets and Fungible Estimates," by Jolynn Pek and Hao Wu (, 2018, Vol. 23, No. 4, pp. 635-653. http://dx.doi.org/10.1037/met0000163), the copyright attribution was incorrect. The copyright should not have been "In the public domain." The online version of this article has been corrected. (The following abstract of the original article appeared in record 2018-00186-001.) Current concerns regarding the dependability of psychological findings call for methodological developments to provide additional evidence in support of scientific conclusions. This article highlights the value and importance of two distinct kinds of parameter uncertainty, which are quantified by confidence sets (CSs) and fungible parameter estimates (FPEs; Lee, MacCallum, & Browne, 2017); both provide essential information regarding the defensibility of scientific findings. Using the structural equation model, we introduce a general perturbation framework based on the likelihood function that unifies CSs and FPEs and sheds new light on the conceptual distinctions between them. A targeted illustration is then presented to demonstrate the factors which differentially influence CSs and FPEs, further highlighting their theoretical differences. With 3 empirical examples on initiating a conversation with a stranger (Bagozzi & Warshaw, 1988), posttraumatic growth of caregivers in the context of pediatric palliative care (Cadell et al., 2014), and the direct and indirect effects of spirituality on thriving among youth (Dowling, Gestsdottir, Anderson, von Eye, & Lerner, 2004), we illustrate how CSs and FPEs provide unique information which lead to better informed scientific conclusions. Finally, we discuss the importance of considering information afforded by CSs and FPEs in strengthening the basis of interpreting statistical results in substantive research, conclude with future research directions, and provide example OpenMx code for the computation of CSs and FPEs. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
报告由 Jolynn Pek 和 Hao Wu 撰写的“结构方程模型中的参数不确定性：置信集和可替换估计”（，2018 年 12 月，第 23 卷第 4 期，第 635-653 页）中的错误。在 Jolynn Pek 和 Hao Wu 的文章“结构方程模型中的参数不确定性：置信集和可替换估计”（，2018 年，第 23 卷，第 4 期，第 635-653 页。http://dx.doi.org/10.1037/met0000163）中，版权归属不正确。版权不应为“公有领域”。本文的在线版本已更正。（原始文章的以下摘要出现在记录 2018-00186-001 中。）当前人们对心理发现的可靠性感到担忧，这需要开展方法学研究，以提供更多证据支持科学结论。本文重点介绍了两种不同类型的参数不确定性的价值和重要性，这两种不确定性分别通过置信集（CS）和可替换参数估计（FPE）进行量化（Lee、MacCallum 和 Browne，2017）；它们都提供了关于科学发现的防御能力的重要信息。我们使用结构方程模型，引入了一个基于似然函数的通用扰动框架，该框架统一了 CS 和 FPE，并为它们之间的概念区别提供了新的见解。然后，进行了有针对性的说明，以展示影响 CS 和 FPE 的不同因素，进一步突出它们在理论上的差异。通过 3 个关于与陌生人交谈的初始研究实例（Bagozzi 和 Warshaw，1988）、儿科姑息治疗背景下护理人员的创伤后成长（Cadell 等人，2014）以及精神信仰对青少年发展的直接和间接影响（Dowling、Gestsdottir、Anderson、von Eye 和 Lerner，2004），我们说明了 CS 和 FPE 如何提供独特的信息，从而得出更明智的科学结论。最后，我们讨论了在实质性研究中考虑 CS 和 FPE 提供的信息对于加强解释统计结果基础的重要性，总结了未来的研究方向，并提供了用于计算 CS 和 FPE 的示例 OpenMx 代码。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
