Abstract：
The Gaussian graphical model (GGM) has recently grown popular in psychological research, with a large body of estimation methods being proposed and discussed across various fields of study, and several algorithms being identified and recommend as applicable to psychological data sets. Such high-dimensional model estimation, however, is not trivial, and algorithms tend to perform differently in different settings. In addition, psychological research poses unique challenges, including placing a strong focus on weak edges (e.g., bridge edges), handling data measured on ordered scales, and relatively limited sample sizes. As a result, there is currently no consensus regarding which estimation procedure performs best in which setting. In this large-scale simulation study, we aimed to overcome this gap in the literature by comparing the performance of several estimation algorithms suitable for Gaussian and skewed ordered categorical data across a multitude of settings, as to arrive at concrete guidelines from applied researchers. In total, we investigated 60 different metrics across 564,000 simulated data sets. We summarized our findings through a platform that allows for manually exploring simulation results. Overall, we found that an exchange between discovery (e.g., sensitivity, edge weight correlation) and caution (e.g., specificity, precision) should always be expected, and achieving both-which is a requirement for perfect replicability-is difficult. Further, we identified that the estimation method is best chosen in light of each research question and have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
摘要：
高斯图形模型（GGM）最近在心理学研究中越来越流行，随着各种研究领域的大量估计方法被提出和讨论，以及几种算法被识别并推荐适用于心理数据集。这样的高维模型估计，然而,不是微不足道的，和算法在不同的设置中往往表现不同。此外,心理学研究提出了独特的挑战，包括将重点放在弱边缘上(例如，桥边)，处理在有序秤上测量的数据，和相对有限的样本量。因此,目前，关于哪种估计程序在哪种情况下表现最好，尚无共识。在这个大规模的模拟研究中,我们的目的是克服这一差距，在文献中通过比较几种估计算法适用于高斯和偏斜有序分类数据在许多设置的性能，从应用研究人员那里得出具体的指导方针。总的来说,我们调查了56.4万个模拟数据集的60个不同指标.我们通过一个允许手动探索模拟结果的平台总结了我们的发现。总的来说,我们发现发现之间的交换(例如，灵敏度,边缘权重相关性)和谨慎(例如，特异性，精度)应该始终是预期的，实现这两者-这是完美可复制性的要求-是困难的。Further,我们确定，估计方法是最好的选择，根据每个研究问题，并强调，除了理想的渐近性质和低样本量发现，根据该领域最常见的研究问题得出的结果。(PsycInfo数据库记录(c)2023年APA，保留所有权利)。
