Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.

Background: The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment. Methods: We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system. Results: The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system.

Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. In this literature, researchers have previously provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency.

Background In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required. Methods This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window. Results Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network. Conclusions The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Most psychometric research relies heavily on patterns of correlations between items on which perceivers describe targets. Apart from the targets’ actual (“substantive”) characteristics, this type of data has been shown to also reflect a number of other, non-substantive sources of variation. This is problematic because each of these (semantic redundancy, attitudes, formal response styles) may all by itself account for correlations among items, which may then be misinterpreted in terms of substantive effects. We present an integrative theoretical account of how these non-substantive influences may affect the pattern of relationships between items. We also point out how this is relevant to the validity of conclusions drawn in research on “general factors” (of personality, psychopathology, and personality pathology) and on “network models”. Furthermore, we discuss various ways of dealing with the problem, which is necessary before any correlations between items may be interpreted in terms of substantive associations between the targets’ actual characteristics.

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Statistical innovations allow clinicians to estimate personalized networks from longitudinal data, for example data collected via the Experience Sampling Method (ESM). Such networks can generate insights that may be relevant for constructing case formulations, and therefore guide the selection of personalized treatment targets. While the notion of personalized networks aligns well with the way clinicians think and reason, there are currently several barriers to clinical implementation that limit the utility of such models. First, the most popular network estimation routines are data-driven and do not allow clinicians to incorporate their expertise and theory. Second, network models have many parameters, which can make accurate estimation challenging. Finally, network estimation requires technical skills that are not regularly taught in clinical programs. In this article, we introduce PREMISE, an approach that formally integrates case formulations with personalized network estimation. Using prior elicitation techniques, clinical working hypotheses are translated into formal models, which can subsequently inform network estimation from ESM data using Bayesian inference. PREMISE tackles the three challenges described above: Incorporating clinical information into network estimation systematically allows theoretical and data-driven integration, which in turn increases the accuracy of network estimation techniques. In addition, we implemented the principles of PREMISE into a practical web-based toolkit that generates intuitive feedback, thereby facilitating clinical implementation. To illustrate its clinical potential, we use PREMISE to estimate clinically informed networks for a client suffering from obsessive-compulsive disorder. We discuss open challenges in selecting statistical models for PREMISE, as well as specific future directions for clinical implementation.