In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Results from longitudinal studies are ambiguous regarding the direction of effects between job stressors and burnout over time. We meta-analyzed possible reciprocal relations between job stressors and burnout in k = 48 longitudinal studies (N = 26,319), accounting for variation of time intervals in primary studies by using continuous time meta-analysis. Additionally, we analyzed whether country-level job resources (job control and job support; k = 31 European studies, N = 17,747) moderated the effect of job stressors on burnout (stressor-effect) and the effect of burnout on job stressors (strain-effect). Further, we analyzed the replicability of the primary studies by assessing between-study heterogeneity, publication bias, and statistical power. Reciprocal effects between job stressors and burnout exist. The stressor-effect is small, whereas the strain-effect is larger and moderated by job control and job support. Analyses of the different burnout symptoms (emotional) exhaustion and depersonalization/cynicism demonstrated that reciprocal relations between emotional exhaustion and job stressors exist, but depersonalization/cynicism is not directly related to job stressors. Between-study heterogeneity was comparable with other psychological studies, whereas statistical power of primary studies was comparatively large. Conclusions are limited because few primary studies used time intervals of less than 12 months, more than two measurement occasions, and objective measures of stressors. Overall, results imply the need for extended job stress models and new job stress interventions that help employees cope with burnout symptoms. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The lead-lag structure of multivariate time-ordered observations and the possibility to disentangle between-person (BP) from within-person (WP) sources of variance are major assets of longitudinal (panel) data. Hence, psychologists are making increasing use of such data, often with the intent to delineate the dynamic properties of psychological mechanisms, understood as a sequence of causal effects that govern psychological functioning. However, even with longitudinal data, psychological mechanisms are not easily identified. In this article, we show how an adequate representation of time may enhance the tenability of causal interpretations in the context of multivariate longitudinal data analysis. We anchor our considerations with an example that illustrates some of the main problems and questions faced by applied researchers and practitioners. We distinguish between static versus dynamic and discrete versus continuous time modeling approaches and discuss their advantages and disadvantages. We place particular emphasis on different ways of addressing BP differences and stress their dual role as potential confounds versus valuable sources of information for improving estimation and aiding causal inference. We conclude by outlining an approach that offers the potential of better integration of information on BP differences and WP changes in the search for causal mechanisms along with a discussion of current problems and limitations.

The COVID-19 pandemic and associated lockdowns have posed unique and severe challenges to our global society. To gain an integrative understanding of pervasive social and mental health impacts in 3522 Berlin residents aged 18 to 65, we systematically investigated the structural and temporal relationship between a variety of psychological indicators of vulnerability, resilience and social cohesion before, during and after the first lockdown in Germany using a retrospective longitudinal study design. Factor analyses revealed that (a) vulnerability and resilience indicators converged on one general bipolar factor, (b) residual variance of resilience indicators formed a distinct factor of adaptive coping capacities and (c) social cohesion could be reliably measured with a hierarchical model including four first-order dimensions of trust, a sense of belonging, social interactions and social engagement, and one second-order social cohesion factor. In the second step, latent change score models revealed that overall psychological vulnerability increased during the first lockdown and decreased again during re-opening, although not to baseline levels. Levels of social cohesion, in contrast, first decreased and then increased again during re-opening. Furthermore, participants who increased in vulnerability simultaneously decreased in social cohesion and adaptive coping during lockdown. While higher pre-lockdown levels of social cohesion predicted a stronger lockdown effect on mental health, individuals with higher social cohesion during the lockdown and positive change in coping abilities and social cohesion during re-opening showed better mental health recovery, highlighting the important role of social capacities in both amplifying but also overcoming the multiple challenges of this collective crisis.

The present article provides a didactic presentation and extension of selected features of Pearl’s DAG-based approach to causal inference for researchers familiar with structural equation modeling. We illustrate key concepts using a cross-lagged panel design. We distinguish between (a) forecasts of the value of an outcome variable after an intervention and (b) predictions of future values of an outcome variable. We consider the mean level and variance of the outcome variable as well as the probability that the outcome will fall within an acceptable range. We extend this basic approach to include additive random effects, allowing us to distinguish between average effects of interventions and person-specific effects of interventions. We derive optimal person-specific treatment levels and show that optimal treatment levels may differ across individuals. We present worked examples using simulated data based on the results of a prior empirical study of the relationship between blood insulin and glucose levels.