Revealing the dynamic network structure of the Beck Depression Inventory-II

Bringmann, Laura F.; Lemmens, Lotte H.J.M.; Huibers, Marcus J.H.; Borsboom, Denny; Tuerlinckx, Francis

doi:10.1017/s0033291714001809

Cited by 299 publications

(298 citation statements)

References 47 publications

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“…If we spread 10 thermometers (the indicators) across a large hall and aim to measure the temperature (the latent variable), the measurements will be highly correlated because they originate from the same common cause (the reflective latent variable temperature); the correlations among thermometers are spurious and disappear once we condition on the latent variable. For depression, however, the assumption that symptom correlations are spurious is not only inconsistent with common sense (insomnia -> fatigue -> concentration problems) and residual dependencies among symptoms, but also contrasts with studies demonstrating that symptoms influence each other directly in complex dynamic systems Bringmann, Lemmens, Huibers, Borsboom, & Tuerlinckx, 2015;Fried, 2015).…”

Section: A New Perspective On Depression Sum-scoresmentioning

confidence: 91%

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Fried

Borkulo

Epskamp

et al. 2016

Psychological Assessment

245

241

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In depression research, symptoms are routinely assessed via rating scales and added to construct sum-scores. These scores are used as a proxy for depression severity in cross-sectional research, and differences in sum-scores over time are taken to reflect changes in an underlying depression construct. To allow for such interpretations, rating scales must (a) measure a single construct, and (b) measure that construct in the same way across time. These requirements are referred to as unidimensionality and measurement invariance. We investigated these 2 requirements in 2 large prospective studies (combined n = 3,509) in which overall depression levels decrease, examining 4 common depression rating scales (1 self-report, 3 clinician-report) with different time intervals between assessments (between 6 weeks and 2 years). A consistent pattern of results emerged. For all instruments, neither unidimensionality nor measurement invariance appeared remotely tenable. At least 3 factors were required to describe each scale, and the factor structure changed over time. Typically, the structure became less multifactorial as depression severity decreased (without however reaching unidimensionality). The decrease in the sum-scores was accompanied by an increase in the variances of the sum-scores, and increases in internal consistency. These findings challenge the common interpretation of sum-scores and their changes as reflecting 1 underlying construct. The violations of common measurement requirements are sufficiently severe to suggest alternative interpretations of depression sum-scores as formative instead of reflective measures. We discuss the possible causes of these violations such as response shift bias, restriction of range, and regression to the mean.

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Section: A New Perspective On Depression Sum-scoresmentioning

confidence: 91%

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Fried

Borkulo

Epskamp

et al. 2016

Psychological Assessment

245

241

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“…Another method that can take into account temporal dependencies between the emotions of two individuals is multilevel vector-autoregressive modeling and the related random intercepts cross-lagged panel models (Bringmann et al, 2013;Bringmann, Lemmens, Huibers, Borsboom, & Tuerlinckx, 2015;Hamaker, Kuiper, & Grasman, 2015;Koval, Butler, Hollenstein, Lanteigne, & Kuppens, 2015;Randall & Butler, 2013). In addition, such models can also be studied in the frequency domain, instead of the standard time domain (Liu & Molenaar, 2016;Sadler, Ethier, Gunn, Duong, & Woody, 2009).…”

Section: Introductionmentioning

confidence: 99%

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Bringmann

Ferrer

Hamaker

et al. 2018

Multivariate Behavioral Research

111

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Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

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“…Network analyses recently have been applied to several mental disorders, including major depressive disorder (Bringmann, Lemmens, Huibers, Borsboom, & Tuerlinckx, 2015), posttraumatic stress disorder (McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2015), and substance use disorder (Rhemtulla, Fried, Aggen, Tuerlinckx, Kendler, & Borsboom, 2016). Applying network analysis to DSM-IV symptoms of substance abuse in adult twins, for instance, has indicated that using a drug more than planned was among the most central substance abuse indicators (Rhemtulla, Fried, Aggen, Tuerlinckx, Kendler, & Borsboom, 2016).…”

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confidence: 99%

What features of psychopathy might be central? A network analysis of the Psychopathy Checklist-Revised (PCL-R) in three large samples.

Verschuere

Grothe

Waldorp

et al. 2018

Journal of Abnormal Psychology

102

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Despite a wealth of research, the core features of psychopathy remain hotly debated.Using network analysis, an innovative and increasingly popular statistical tool, we mapped the network structure of psychopathy, as operationalized by the Psychopathy Checklist-Revised (PCL-R; Hare, 2003) in two large U.S. offender samples (n NIMH = 1559; n Wisconsin = 3954), and one large Dutch forensic psychiatric sample (n TBS = 1937). Centrality indices were highly stable within each sample, and indicated that Callousness/lack of empathy was the most central PCL-R item in the two U.S.samples, which aligns with classic clinical descriptions and prototypicality studies of psychopathy. The similarities across the U.S. samples offer some support regarding generalizability, but there were also striking differences between the U.S. samples and the Dutch sample, wherein the latter Callousnesss/lack of empathy was also fairly central but Irresponsibility and Parasitic Lifestyle were even more central. The findings raise the important possibility that network-structures do not only reflect the structure of the constructs under study, but also the sample from which the data derive. The results further raise the possibility of cross-cultural differences in the phenotypic structure of psychopathy, PCL-R measurement variance, or both.Network analyses may help elucidate the core characteristics of psychopathological constructs, including psychopathy, as well as provide a new tool for assessing measurement invariance across cultures.

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Revealing the dynamic network structure of the Beck Depression Inventory-II

Cited by 299 publications

References 47 publications

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Measuring depression over time . . . Or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression.

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

What features of psychopathy might be central? A network analysis of the Psychopathy Checklist-Revised (PCL-R) in three large samples.

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