Unidimensionality is fundamental to psychometrics. Despite the recent focus on dimensionality assessment in network psychometrics, unidimensionality assessment remains a challenge. Community detection algorithms are the most common approach to estimate dimensionality in networks. Many community detection algorithms maximize an objective criterion called modularity. A limitation of modularity is that it penalizes unidimensional structures in networks, favoring two or more communities (dimensions). In this study, this penalization is discussed and a solution is offered. Then, a Monte Carlo simulation using one- and two-factor models is performed. Key to the simulation was the condition of model error or the misfit of the population factor model to the generated data. Based on previous simulation studies, several community detection algorithms that have performed well with unidimensional structures (Leading Eigenvalue, Leiden, Louvain, and Walktrap) were compared. A grid search was performed on the tunable parameters of these algorithms to determine the optimal trade-off between unidimensional and bidimensional recovery. The best-performing parameters for each algorithm were then compared against each other as well as maximum likelihood factor analysis and parallel analysis (PA) with mean and 95th percentile eigenvalues. Overall, the Leiden and Louvain algorithms and PA methods were the most accurate methods to recover unidimensional and bidimensional structures and were the most robust to model error. More nuanced method recommendations for specific unidimensional and bidimensional conditions are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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一维社区检测：蒙特卡罗模拟、网格搜索和比较。


单维性是心理测量学的基础。尽管最近关注网络心理测量学中的维度评估，但单维度评估仍然是一个挑战。社区检测算法是估计网络维度的最常见方法。许多社区检测算法最大化称为模块化的客观标准。模块化的局限性在于它不利于网络中的一维结构，而有利于两个或多个社区（维度）。在本研究中，讨论了这种惩罚并提供了解决方案。然后，使用一因素和二因素模型进行蒙特卡罗模拟。模拟的关键是模型误差的情况或人口因素模型与生成的数据不匹配的情况。基于之前的仿真研究，对几种在一维结构上表现良好的社区检测算法（Leading Eigenvalue、Leiden、Louvain 和 Walktrap）进行了比较。对这些算法的可调参数进行网格搜索，以确定一维和二维恢复之间的最佳权衡。然后将每种算法的最佳性能参数相互比较，并使用最大似然因子分析和具有平均值和第 95 个百分位特征值的并行分析 (PA)。总体而言，Leiden 和 Louvain 算法以及 PA 方法是恢复一维和二维结构的最准确方法，并且对模型误差最稳健。提供了针对特定一维和二维条件的更细致的方法建议。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。