Revised network loadings.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Alexander P Christensen, Hudson Golino, Francisco J Abad, Luis Eduardo Garrido
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引用次数: 0

Abstract

Psychometric assessment is the foundation of psychological research, where the accuracy of outcomes and their interpretations depend on measurement. Due to the widespread application of factor models, factor loadings are fundamental to modern psychometric assessment. Recent advances in network psychometrics introduced network loadings which aim to provide network models with a metric similar to factor loadings to assess measurement quality when the data are generated from a factor model. Our study revisits and refines the original network loadings to account for properties of (regularized) partial correlation networks, such as the reduction of partial correlation size as the number of variables increase, that were not considered previously. Using a simulation study, the revised network loadings demonstrated greater congruence with the simulated factor loadings across conditions relative to the original formulation. The simulation also evaluated how well correlations between factors can be captured by scores estimated with network loadings. The results show that not only can these network scores adequately estimate the simulated correlations between factors, they can do so without the need for rotation, a standard requirement for factor loadings. The consequence is that researchers do not need to choose a rotation with the revised network loadings, reducing the analytic degrees of freedom and eliminating this common source of variability in factor analysis. We discuss the interpretation of network loadings when data are believed to be generated from a network model and how they may fit into a network theory of measurement.

修改了网络负载。
心理测量评估是心理学研究的基础,其结果的准确性及其解释取决于测量。由于因子模型的广泛应用,因子负荷是现代心理测量评估的基础。网络心理测量学的最新进展引入了网络负载,其目的是为网络模型提供一个类似于因子负载的度量,以便在因子模型生成数据时评估测量质量。我们的研究重新审视并改进了原始网络负载,以考虑(正则化)部分相关网络的特性,例如随着变量数量的增加而减少部分相关大小,这是以前没有考虑到的。通过模拟研究,与原始公式相比,修正后的网络负载与不同条件下的模拟因子负载表现出更大的一致性。模拟还评估了通过网络负载估计的分数捕获因素之间的相关性的程度。结果表明,这些网络分数不仅可以充分估计因子之间的模拟相关性,而且可以在不需要旋转的情况下做到这一点,而旋转是因子加载的标准要求。其结果是,研究人员不需要选择一个旋转与修改后的网络负载,减少了分析自由度,消除了因子分析中这种常见的可变性来源。我们讨论了当数据被认为是由网络模型产生时网络负载的解释,以及它们如何适合于网络测量理论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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