Since the introduction of network psychometrics, several connections to statistical models in "classical" psychometrics (i.e., IRT, SEM, GLM) as well as to approaches from other research fields have been established. In this paper, these developments have been reviewed and synthesized and, based on an exploratory literature search, further advanced and presented in an accessible visual format. This perspective opens up promising opportunities to extend the psychometric-toolbox by incorporating and learning from statistical methodologies developed in other research domains, which often address similar or even identical problems. Highlighting these methodological commonalities may also foster collaboration across research fields that have traditionally remained largely independent. Moreover, awareness of these connections may render methodological development more systematic and goal-directed and may enable a meaningful division of labor, for example between the development of statistical methodology and its practical implementation for empirical research through software tools. Finally, these methodological advances provide new opportunities for empirical research and may contribute to a reconciliation with longstanding conceptual issues concerning psychometric constructs and, more broadly, psychological phenomena.

翻译：自网络心理测量学提出以来，该领域已与"经典"心理测量学中的统计模型（如项目反应理论、结构方程模型、广义线性模型）以及其他研究领域的方法建立了多项联系。本文通过综述与整合这些发展成果，在探索性文献检索基础上将其进一步深化，并以直观可视化的形式呈现。这一视角为拓展心理测量工具箱开辟了富有前景的途径——通过引入并借鉴其他研究领域中应对相似甚至相同问题的统计方法论。强调这些方法论共性也有助于促进传统上相对独立的研究领域之间的合作。此外，对这种关联性的认知能使方法开发更具系统性和目标导向性，并实现有意义的劳动分工，例如在统计方法开发与通过软件工具将其应用于实证研究的实践之间进行分工。最后，这些方法论进步为实证研究提供了新机遇，并可能有助于调和心理测量构念乃至更广泛心理现象中长期存在的概念性争议。