Network analysis: An overview for mental health research.
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| Title: | Network analysis: An overview for mental health research. |
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| Authors: | Briganti, Giovanni (AUTHOR), Scutari, Marco (AUTHOR), Epskamp, Sacha (AUTHOR), Borsboom, Denny (AUTHOR), Hoekstra, Ria H. A. (AUTHOR), Golino, Hudson Fernandes (AUTHOR), Christensen, Alexander P. (AUTHOR), Morvan, Yannick (AUTHOR), Ebrahimi, Omid V. (AUTHOR), Costantini, Giulio (AUTHOR), Heeren, Alexandre (AUTHOR), Ron, Jill de (AUTHOR), Bringmann, Laura F. (AUTHOR), Huth, Karoline (AUTHOR), Haslbeck, Jonas M. B. (AUTHOR), Isvoranu, Adela‐Maria (AUTHOR), Marsman, Maarten (AUTHOR), Blanken, Tessa (AUTHOR), Gilbert, Allison (AUTHOR), Henry, Teague Rhine (AUTHOR) |
| Source: | International Journal of Methods in Psychiatric Research. Dec2024, Vol. 33 Issue 4, p1-24. 24p. |
| Subjects: | Psychiatric research, Bayesian analysis, Causal inference, Panel analysis, Pathological psychology |
| Abstract: | Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Methods in Psychiatric Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10498931 |
| DOI: | 10.1002/mpr.2034 |
