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Description: 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 study, we sought to circumvent this issue by formulating a network equivalent called network loadings. In two simulations, we evaluated the extent to which network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the population or true factor loading matrix of factor models. We demonstrate that the network loadings could effectively separate multiple latent causes and estimate the population factor loadings. We also 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 generating model was a random or network model. We found sufficient differences between the loadings, which allowed us to develop an algorithm called Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the true 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). Moreover, we found that network loadings may be a general case that can be effectively applied to any data generating model.