Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented: one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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研究探索性图分析和传统技术的性能来识别潜在因素的数量：模拟和教程。


探索性图分析（EGA）是最近在网络心理测量学框架内提出的一项新技术，用于估计多元数据背后的因素数量。与其他方法不同，EGA 生成视觉引导网络图，它不仅指示要保留的维度数量，还指示哪些项目聚集在一起及其关联程度。尽管之前的研究发现 EGA 优于传统方法，但它们在考虑的条件上受到限制。这些问题通过广泛的模拟研究得到解决，该研究结合了实践中可能发现的各种合理结构，包括连续和二分数据，以及一维和多维结构。此外，还提出了两种新的 EGA 技术：一种将 EGA 扩展为也可以处理一维结构，另一种基于三角最大过滤图方法 (EGAtmfg)。将两种 EGA 技术与 5 种广泛使用的因子分析技术进行了比较。总体而言，EGA 和 EGAtmfg 的性能与最准确的传统方法（并行分析）一样好，并且能够产生所有评估方法中最佳的大样本特性。为了促进 EGA 的使用和应用，我们提供了一个简单的 R 教程，介绍如何应用和解释 EGA，使用著名的心理工具：马洛-克罗恩社会愿望量表的分数。 （PsycINFO 数据库记录 (c) 2020 APA，保留所有权利）。