Recent research has demonstrated that the network measure node strength or sum of a node’s connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).

最近的研究表明，网络测量节点强度或节点连接的总和大致相当于验证性因子分析 (CFA) 负载。这项研究的一个关键发现是节点强度代表了不同潜在原因的组合。在目前的研究中，我们试图通过制定因子载荷的网络等价物来规避这个问题，我们称之为网络载荷. 在两次模拟中，我们评估了这些网络负载是否可以有效地 (1) 分离多个潜在原因的影响和 (2) 估计因子模型的模拟因子负载矩阵。我们的研究结果表明，网络负载可以有效地做到这两点。此外，我们利用第二次模拟得出网络负载的效果大小指南。在第三次模拟中，当数据是从随机、因子和网络模型生成时，我们评估了因子和网络负载之间的异同。我们发现负载之间存在足够的差异，这使我们能够开发一种算法来预测称为负载比较测试的数据生成模型(LCT)。LCT 在预测数据生成模型时具有较高的敏感性和特异性。总之，我们的结果表明，当数据从因子模型生成时，网络负载可以提供与因子负载相似的信息，因此可以以类似的方式使用（例如，项目选择、测量不变性、因子分数）。