Stability and variability of personality networks. A tutorial on recent developments in network psychometrics

Network models of driver behavior

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

Visualizing Psychological Networks: A Tutorial in R

Networks have emerged as a popular method for studying mental disorders. Psychopathology networks consist of aspects (e.g., symptoms) of mental disorders (nodes) and the connections between those aspects (edges). Unfortunately, the visual presentation of networks can occasionally be misleading. For instance, researchers may be tempted to conclude that nodes that appear close together are highly related, and that nodes that are far apart are less related. Yet this is not always the case. In networks plotted with force-directed algorithms, the most popular approach, the spatial arrangement of nodes is not easily interpretable. However, other plotting approaches can render node positioning interpretable. We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks. We compare the strengths and weaknesses of each method, noting how to properly interpret each type of plotting approach.

Identifying core deficits in a dimensional model of Borderline Personality Disorder features: A network analysis

Researchers have proposed three core deficits of Borderline Personality Disorder (BPD): emotion dysregulation, interpersonal problems, and self-identity disturbance. Previous methods for testing these deficits rest on problematic assumptions (e.g., the assumption that observable/measured features of BPD, such as chaotic relationships and affective intensity, occur independently). A network model of psychopathology assumes that observable features of disorders directly interact, and network analytic methods quantify how central each feature is. We conducted a network analysis of core deficits of BPD features using a large (n = 4386) sample of participants with a range of BPD features. The most central features of participants in the High BPD group were loneliness, recklessness/impulsivity, and intense moods, supporting models of emotion dysregulation and interpersonal problems. The networks of BPD features did not differ between men and women. We provide directions for future research to enhance our understanding of how networks of BPD features change over time.

Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples

The growing literature conceptualizing mental disorders like posttraumatic stress disorder (PTSD) as networks of interacting symptoms faces three key challenges. Prior studies predominantly used (a) small samples with low power for precise estimation, (b) nonclinical samples, and (c) single samples. This renders network structures in clinical data, and the extent to which networks replicate across data sets, unknown. To overcome these limitations, the present cross-cultural multisite study estimated regularized partial correlation networks of 16 PTSD symptoms across four data sets of traumatized patients receiving treatment for PTSD (total N = 2,782). Despite differences in culture, trauma type, and severity of the samples, considerable similarities emerged, with moderate to high correlations between symptom profiles (0.43-0.82), network structures (0.62-0.74), and centrality estimates (0.63-0.75). We discuss the importance of future replicability efforts to improve clinical psychological science and provide code, model output, and correlation matrices to make the results of this article fully reproducible.

PTSD symptomics: network analyses in the field of psychotraumatology

Recent years have seen increasing attention on posttraumatic stress disorder (PTSD) research. While research has largely focused on the dichotomy between patients diagnosed with mental disorders and healthy controls - in other words, investigations at the level of diagnoses - recent work has focused on psychopathology symptoms. Symptomics research in the area of PTSD has been scarce so far, although several studies have focused on investigating the network structures of PTSD symptoms. The present special issue of EJPT adds to the literature by curating additional PTSD network studies, each looking at a different aspect of PTSD. We hope that this special issue encourages researchers to conceptualize and model PTSD data from a network perspective, which arguably has the potential to inform and improve the efficacy of therapeutic interventions.

Gender Differences in Posttraumatic Stress Symptoms after a Terrorist Attack: A Network Approach

Background: Posttraumatic stress symptoms are more prevalent in women than in men. To improve our understanding of gender differences in PTSD, detailed knowledge about the underlying symptom networks and gender specific symptom profiles is needed. Objective: We aimed to describe the gender differences in levels of individual posttraumatic stress symptoms after a terrorist attack, as well as identify possible gender differences in associations between posttraumatic stress symptoms. Method: This study used survey data from ministerial employees directly (n = 190) and indirectly (n = 1,615) exposed to the 2011 Oslo bombing. Data was collected approximately 10 months after the event. In order to investigate gender differences in levels of symptoms, we used bootstrapped means and standard deviations. Network analyses were conducted to identify gender differences in the associations between posttraumatic stress symptoms. Results: Women reported higher levels of all symptoms, and the strongest effect sizes were found for symptoms of re-experiencing, and anxious and dysphoric arousal. Among individuals with considerable levels of posttraumatic stress symptoms, women reported higher levels of physiological cue activity and exaggerated startle response. No significant gender differences in the networks of posttraumatic stress were found. Conclusions: The present results find no indication that the gender difference in prevalence of PTSD can be explained by differences in associations between symptoms. In order to determine if this finding can be applied to other participants and circumstances, future studies should seek to replicate this study in both community and clinical samples.