Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research
Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research
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DOI：
10.1371/journal.pone.0174035
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发表时间：
2017-06-08
期刊：
影响因子：
3.7
通讯作者：
Epskamp, Sacha
中科院分区：
文献类型：
--
作者：
Golino, Hudson F.;Epskamp, Sacha
正确维数的估计是心理测量学中长期存在的问题。已经提出了几种方法，例如并行分析（PA）、Kaiser-Guttman 特征值大于一规则、多重平均部分程序（MAP）、使用拟合指数（如 BIC 和 EBIC）的最大似然方法以及较少使用和研究的称为非常简单结构（VSS）的方法。在本文中，将介绍一种估计维数的新方法，并通过模拟与上述传统技术进行比较。本文提出的方法称为探索性图分析（EGA），因为它基于具有使用 EBIC 指定的正则化参数的图形套索。维数使用 walktrap 进行验证，walktrap 是一种用于识别网络中社区的随机游走算法。总共模拟了 32,000 个数据集以适应已知的因子结构，数据集因不同标准而异：因子数量（2 和 4）、项目数量（5 和 10）、样本量（100、500、1000 和 5000）以及因子之间的相关性（正交、.20、.50 和.70），产生 64 种不同的条件。对于每种条件，使用 lavaan 模拟 500 个数据集。结果表明，EGA 在许多情况下的表现与并行分析、EBIC、eBIC 和 Kaiser-Guttman 规则相当，特别是当因子数量为 2 时。然而，当因子之间的相关性为 7 时，EGA 是唯一能够正确估计四因子结构中维数的技术，对于 5,000 个观测值的样本量，显示准确度为 100%。最后，EGA 用于估计真实数据集中的因子数量，以便将其性能与模拟研究中测试的其他六种技术进行比较。
The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA),since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal,.20,.50 and.70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were.7,showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.