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，其与"经典"心理测量学（如项目反应理论、结构方程模型、广义线性模型）中的统计模型，以及其他研究领域的方法已建立起多重关联。本文通过系统性文献检索，对这些进展进行了综述与整合，并以直观的可视化形式呈现深化见解。这一视角为心理测量工具集的拓展开辟了广阔前景——通过借鉴并学习其他研究领域中针对类似（甚至相同）问题开发的统计方法。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间的跨学科协作。此外，对这种关联性的认知能使方法论发展更具系统性与目标导向性，并实现富有成效的劳动分工——例如，统计方法论的开发与其通过软件工具在实证研究中的实践应用可分轨进行。最终，这些方法论进展为实证研究提供了新契机，并有望调和长期悬而未决的概念性问题，涉及心理测量构念乃至更广义的心理现象。