Shi Dingjing, Christensen Alexander P, Day Eric Anthony, Golino Hudson F, Garrido Luis Eduardo
Department of Psychology, University of Oklahoma, Norman, OK, USA.
Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA.
Multivariate Behav Res. 2025 Mar-Apr;60(2):184-210. doi: 10.1080/00273171.2024.2395941. Epub 2024 Sep 16.
To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.
为了理解心理数据，审视变量的结构和维度至关重要。在本研究中，我们在网络心理测量模型中，考察了与传统基于广义线性模型选择算子（GLASSO）的探索性图分析（EGA）不同的估计算法，以评估数据的维度结构。该研究应用贝叶斯共轭先验或杰弗里斯先验来估计图结构，然后使用鲁汶社区检测算法对节点组进行划分和识别，这使得多维度和单维度因子结构得以检测。蒙特卡洛模拟表明，与基于GLASSO的EGA和传统平行分析（PA）相比，这两种替代贝叶斯估计算法具有相当或更好的性能。在估计多维度因子结构时，基于分析的方法（即EGA.分析）在准确性和平均偏差/绝对误差之间表现出最佳平衡，准确性最高与EGA相当，但误差最小。基于抽样的方法（EGA.抽样）比PA具有更高的准确性和更小的误差；准确性低于EGA，但误差也比EGA小。在不同数据条件下，这两种算法的技术性能比EGA和PA更稳定。在估计单维度结构时，PA技术表现最佳，其次是EGA，然后是EGA.分析和EGA.抽样。此外，该研究探索了四种全贝叶斯技术来评估网络心理测量学中的维度。结果表明，在小样本量下使用贝叶斯假设检验或推导图结构的后验样本时，性能更优。该研究建议使用EGA.分析技术作为评估维度的替代工具，并主张EGA.抽样方法作为一种有价值的替代技术的有用性。研究结果还表明，将基于正则化的网络建模EGA方法扩展到贝叶斯框架有令人鼓舞的结果，并讨论了这一工作方向的未来发展。该研究在R语言中通过两个实证例子说明了这些技术的实际应用。
