Questions about networks, measurement, and causation

Markus, Keith A.

doi:10.1017/s0140525x10000658

Cited by 6 publications

(8 citation statements)

References 4 publications

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“…Analyzing broader constructs—such a symptom clusters (e.g., Anker et al, 2017)—versus single symptoms would also be amenable to this approach. Specifically, we would suggest—as others have (Eaton, 2015; Epskamp et al, in press; Markus, 2010; Stapel, 2015; Young, 2015)—that integrating latent variables into network analysis is the best way forward. We echo the suggestions made by Epskamp et al (in press), who recently proposed latent network modeling , in which latent variables are used to extract the most reliable variance from multiple measures of a symptom, and these latent variables subsequently act as the nodes in a network analysis.…”

Section: Discussionmentioning

confidence: 96%

Evidence that psychopathology symptom networks have limited replicability.

Forbes¹,

Wright²,

Markon³

et al. 2017

Journal of Abnormal Psychology

283

215

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Network analysis is quickly gaining popularity in psychopathology research as a method that aims to reveal causal relationships among individual symptoms. To date, four main types of psychopathology networks have been proposed: (1) association networks, (2) regularized concentration networks, (3) relative importance networks, and (4) directed acyclic graphs. We examined the replicability of these analyses based on symptoms of major depression and generalized anxiety between and within two highly similar epidemiological samples (i.e., the National Comorbidity Survey – Replication [n = 9282] and the National Survey of Mental Health and Wellbeing [n = 8841]). While association networks were stable, the three other types of network analysis (i.e., the conditional independence networks) had poor replicability between and within methods and samples. The detailed aspects of the models—such as the estimation of specific edges and the centrality of individual nodes—were particularly unstable. For example, 44% of the symptoms were estimated as the “most influential” on at least one centrality index across the six conditional independence networks in the full samples, and only 13–21% of the edges were consistently estimated across these networks. One of the likely reasons for the instability of the networks is the predominance of measurement error in the assessment of individual symptoms. We discuss the implications of these findings for the growing field of psychopathology network research, and conclude that novel results originating from psychopathology networks should be held to higher standards of evidence before they are ready for dissemination or implementation in the field.

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Section: Discussionmentioning

confidence: 96%

Evidence that psychopathology symptom networks have limited replicability.

Forbes¹,

Wright²,

Markon³

et al. 2017

Journal of Abnormal Psychology

283

215

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“…Network models are based on the premise that symptom inter-relationships reflect direct causal influences between symptoms, rather than underlying latent factors, as in the factor analysis framework. The exact relationship between factor and network models remains unclear, however (Molenaar, 2010; Ross, 2010), and various authors disagreed with the network proponents’ critique of the latent variable approach (Belzung et al 2010; Danks et al 2010; Haig & Vertue, 2010; Humphry & McGrane, 2010; Markus, 2010). Most importantly, no empirical comparison of these two approaches has yet been reported (Krueger et al 2010).…”

Section: Discussionmentioning

confidence: 99%

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

Foster

Mohler‐Kuo

2017

Epidemiol Psychiatr Sci

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First, caution is warranted when considering studies assessing dimensions of depression because general population-based studies and studies of depressed individuals generate different data that can lead to different conclusions. This problem likely generalises to other models based on the symptoms' inter-relationships such as network models. Second, whereas the overall severity aligns individuals on a continuum of disorder intensity that allows non-affected individuals to be distinguished from affected individuals, the clinical evaluation and treatment of depressed individuals should focus directly on each individual's symptom profile.

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“…The purported dichotomy between latent variable (common cause) models and network models has been criticized by several researchers (see, e.g., Haig & Vertue, 2010; Humphry & McGrane, 2010; Krueger et al, 2010; Markus, 2010; McFarland & Malta, 2010; Molenaar, 2010). First, it has been pointed out that latent variable models and network models are mathematically equivalent.…”

Section: The Network Approachmentioning

confidence: 99%

“…In this paper, we will refer to this perspective as the network approach, which has become the standard expression in the literature (e.g., Borsboom, 2017b;Fried & Cramer, 2017). 1 When the network approach was introduced, several researchers cast doubt on the necessity to counterpose latent variable (or common cause) models and network models (see, e.g., Ashton & Lee, 2012;Danks, Fancsali, Glymour, & Scheines, 2010;Haig & Vertue, 2010;Humphry & McGrane, 2010;Krueger, DeYoung, & Markon, 2010;Markus, 2010;McFarland & Malta, 2010;Molenaar, 2010). This critique, however, has not changed the basic rationale for the network approach, which even in recent articles is still based on the contrast between latent variable and network models (e.g., Hofmann, Curtiss, & McNally, 2016;McNally, 2016;Nuijten et al, 2016;Robinaugh et al, 2014;van der Maas, Kan, Marsman, & Stevenson, 2017).…”

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confidence: 99%

Don’t blame the model: Reconsidering the network approach to psychopathology.

Bringmann¹,

Eronen²

2018

Psychological Review

207

163

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Citation for published version (APA): Bringmann, L. F., & Eronen, M. I. (2018). Don't blame the model: Reconsidering the network approach to psychopathology. Psychological Review, 125(4), 606-615. https://doi.org/10.1037/rev0000108 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders.As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches, that can provide a better framework for conceptualizing mental disorders.Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward.

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Questions about networks, measurement, and causation

Cited by 6 publications

References 4 publications

Evidence that psychopathology symptom networks have limited replicability.

Evidence that psychopathology symptom networks have limited replicability.

New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals

Don’t blame the model: Reconsidering the network approach to psychopathology.

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