Symptom Network Analysis Tools for Applied Researchers With Cross-Sectional and Panel Data - A Brief Overview and Multiverse Analysis

In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several t...

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Bibliographic Details
Published in:Psychological reports Vol. 128; no. 6; p. 4740
Main Author: Freichel, René
Format: Journal Article
Language:English
Published: United States 01.12.2025
Subjects:
Chronic Disease
Cross-Sectional Studies
Humans
Models, Statistical
network analysis
multiverse analysis
psychopathology
panel data models
ISSN:1558-691X, 1558-691X
Online Access:Get more information
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Summary:In recent years, there has been a growing interest in utilizing symptom-network models to study psychopathology and relevant risk factors, such as cognitive and physical health. Various methodological approaches can be employed by researchers analyzing cross-sectional and panel data (i.e., several time points over an extended period). This paper provides an overview of some commonly used analytical tools, including moderated network models, network comparison tests, cross-lagged network analysis, and panel graphical vector-autoregression (VAR) models. Using an easily accessible dataset (easySHARE), this study demonstrates the use of different analytical approaches when investigating (a) the association between mental health and cognitive functioning, and (b) the role of chronic disease in mediating or moderating this association. This multiverse analysis showcases both converging and diverging evidence from different analytical avenues. These findings underscore the importance of multiverse investigations to increase transparency and communicate the extent to which conclusions depend on analytical choices.
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ISSN:1558-691X
1558-691X
DOI:10.1177/00332941231213649