In this thesis, network analysis is introduced as new methodical and conceptual alternative to the common practices of modelling psychological constructs. This happens primarily against the background of a discussion of latent variable modelling and one of its applications: classificatory diagnostics. Being an alternative, network analysis offers usage of analytical methods, especially from graph theory, that are hardly accessible by statistical analysis common in psychology. A sample of N =

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... participants of the project "fit2work" was used to estimate symptom networks of the BSI-53. The stability of these estimations was assessed. Also, several concepts of network theory were investigated partly explorative, partly confirmatory. Especially the mathematical concept of network-connectivity and its matching onto several theoretical constructs such as self-perpetuation of mental disorders or the definitory delimitation of mental illness is focused on. The estimation of the networks resulted in complex structures and patterns of correlation of individual factors of psychological burden in the sample. On item-level, some aggregation of structural aspects of networks presented itself. The examinations of connectivity found no significant changes in the period of participation in "fit2work" and no differences between the subsamples of the responders and non-responders. The findings are discussed regarding their methodical and epistemological implications. On the one side, this discussion focuses on the possible conclusions regarding viable interventions and preventive measures based on cross-sectional analyses. On the other side, it focuses on how connectivity is currently investigated and how it can be linked with theoretical contents. Finally, some concluding remarks concerning network theory's role in science are presented.