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Network analysis: An overview for mental health research

by Ebrahimi, Omid V. , Bringmann, Laura F. , Borsboom, Denny , Blanken, Tessa , Hoekstra, Ria H. A. , Marsman, Maarten , Gilbert, Allison , Heeren, Alexandre , Scutari, Marco , Henry, Teague Rhine , Briganti, Giovanni , Costantini, Giulio , Fried, Eiko I. , Christensen, Alexander P. , Golino, Hudson Fernandes , Morvan, Yannick , Ron, Jill de , Haslbeck, Jonas M. B. , Epskamp, Sacha , Isvoranu, Adela‐Maria , McNally, Richard J. , Huth, Karoline

in Bayes Theorem / Bayesian analysis / Biomedical Research - methods / Human health sciences / Humans / Hypotheses / Mathematical models / Medical research / Mental disorders / Mental Disorders - diagnosis / Mental Disorders - therapy / Mental Health / network analysis / network modeling / network psychometrics / network psychopathology / Psychiatrie / Psychiatry / Psychopathology / Quantitative psychology / Regression analysis / Sciences de la santé humaine

2024

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2024

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2024

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Journal Article

Network analysis: An overview for mental health research

Ebrahimi, Omid V.,

Bringmann, Laura F.,

Borsboom, Denny,

Blanken, Tessa,

Hoekstra, Ria H. A.,

Marsman, Maarten,

Gilbert, Allison,

Heeren, Alexandre,

Scutari, Marco,

Henry, Teague Rhine,

Briganti, Giovanni,

Costantini, Giulio,

Fried, Eiko I.,

Christensen, Alexander P.,

Golino, Hudson Fernandes,

Morvan, Yannick,

Ron, Jill de,

Haslbeck, Jonas M. B.,

Epskamp, Sacha,

Isvoranu, Adela‐Maria,

McNally, Richard J.,

Huth, Karoline

2024

Overview

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.

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Publisher

John Wiley & Sons, Inc,Wiley

Subject

Bayes Theorem

/ Bayesian analysis

/ Biomedical Research - methods

/ Human health sciences

/ Humans

/ Hypotheses

/ Mathematical models