Cosemans Tim, Rosseel Yves, Gelper Sarah
Eindhoven University of Technology, The Netherlands.
Ghent University, Belgium.
Educ Psychol Meas. 2022 Oct;82(5):880-910. doi: 10.1177/00131644211059089. Epub 2021 Dec 28.
Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.
探索性图分析（EGA）是一种常用技术，旨在帮助社会科学家发现潜在变量。然而，研究结果可能会受到研究人员在研究过程中所做方法学决策的影响。在本文中，我们关注关于保留因子数量的选择：我们将最近开发的EGA的性能与各种传统因子保留标准进行比较。我们同时使用连续数据和二元数据，因为在后一种情况下关于此类标准准确性的证据很少。基于不同样本量、主要因子的共同度、因子间相关性、偏度和相关度量所产生的情景的模拟结果表明，在偏差和准确性方面，EGA在大多数情况下优于所考虑的传统因子保留标准。此外，我们表明，对于二元数据，因子保留决策最好使用皮尔逊相关性而非四分相关系数，这与普遍看法相矛盾。
