This section contains papers we published that contain datasets. I deserve none of the credit, all data were collected by other researchers/labs.
» Briganti, G., Kempenaers, C., Braun, S., Fried, E. I. , Linkowski, P. (accepted pending revisions). Network analysis of empathy items from the Interpersonal Reactivity Index in 1973 young adults. Psychiatry Research. PDF, Data. (Abstract)
The aim of this work is to perform a network analysis on the French adaptation of the Interpersonal Reactivity Index (IRI) scale from a large Belgian database and provide additional information for the construct of empathy. We analyze a database of 1973 healthy young adults who were queried on the IRI scale. A regularized partial correlation network is estimated. In the visualization of the model, items are displayed as nodes, edges represent regularized partial correlations between the nodes. Centrality denotes a node’s connectedness with other nodes in the network. The spinglass algorithm and the walktrap algorithm areu sed to identify communities of items, and state-of-the-art stability analyses are carried out. The spinglass algorithm identifies four communities, the walktrap algorithm five communities.Positive edges are found among nodes belonging to the same community as well as among nodes belonging to different communities. Item14 (“Other people’s misfortunes do not usually disturb me a great deal”) shows the highest degree centrality score. The network edges and node centrality order are accurately estimated. Network analysis highlights interesting connections between indicators of empathy; how these results impact empathy models must be assessed in further studies.
» Santos, H. P., Kossakowski, J. J., Schwartz, T. A., Beeber, L., & Fried, E. I. (2018). Longitudinal network structure of depression symptoms and self-efficacy in low- income mothers. PLoS ONE, 13, 1–16. PDF, Data. (Abstract)
Maternal depression was recently conceptualized as a network of interacting symptoms. Prior studies have shown that low self-efficacy, as an index of maternal functioning, is one important source of stress that worsens depression. We have limited information, however, on the specific relationships between depression symptoms and self-efficacy. In this study, we used regularized partial correlation networks to explore the multivariate relationships between maternal depression symptoms and self-efficacy over time. Depressed mothers (n = 306) completed the Center for Epidemiological Studies Depression (CES-D) scale at four time points, between four and eight weeks apart. We estimated (a) the network structure of the 20 CES-D depression symptoms and self-efficacy for each time point, (b) determined the centrality or structural importance of all variables, and (c) tested whether the network structure changed over time. In the resulting networks, self-efficacy was mostly negatively connected with depression symptoms. The strongest relationships among depression symptoms were ‘lonely—sleep difficulties’ and ‘inability to get going—crying’. ‘Feeling disliked’ and ‘concentration difficulty’ were the two most central symptoms. In comparing the network structures, we found that the network structures were moderately stable over time. This is the first study to investigate the network structure and their temporal stability of maternal depression symptoms and self-efficacy in low-income depressed mothers. We discuss how these findings might help future research to identify clinically relevant symptom-to-symptom relationships that could drive maternal depression processes, and potentially inform tailored interventions. We share data and analytical code, making our results fully reproducible.
» Haslbeck, J. M. B. & Fried, E. I. (2017). How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 18 Published Datasets. Psychological Medicine. PDF, Data. (Abstract)
Background. Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.
Methods. We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art net- work models to all datasets, and computed the predictability of all nodes.
Results. Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.
Conclusions. Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.
» Armour, C.*, Fried, E. I.*, Deserno, M. K., Tsai, J., & Pietrzak, R. H. (2017). A Network Analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. Journal of Anxiety Disorders. PDF, Data. (Abstract)
Object: Recent developments in psychometrics enable the application of network models to analyze psychological disorders, such as PTSD. Instead of understanding symptoms as indicators of an underlying common cause, this approach suggests symptoms co-occur in syndromes due to causal interactions. The current study has two goals: (1) examine the network structure among the 20 DSM-5 PTSD symptoms, and (2) incorporate clinically relevant variables to the network to investigate whether PTSD symptoms exhibit differential relationships with suicidal ideation, depression, anxiety, physical quality of life (QoL), mental QoL, age, and sex. Method: We utilized a nationally representative U.S. military veteran’s sample; and analyzed the data from a subsample of 221 veterans who reported clinically significant DSM-5 PTSD symptoms. Networks were estimated using Gaussian graphical models and the lasso regularization. Results: The 20-item DSM-5 PTSD network revealed that symptoms were positively connected within the network. The most central symptoms were negative trauma-related emotions, flashbacks, detachment, and physiological cue reactivity. Especially strong connections emerged between nightmares and flashbacks; blame of self or others and negative trauma-related emotions, detachment and restricted affect; and hypervigilance and exaggerated startle response. Incorporation of clinically relevant covariates into the network revealed paths between self-destructive behavior and suicidal ideation; concentration difficulties and anxiety, depression, and mental QoL; and depression and restricted affect. Conclusion: These results demonstrate the utility of a network approach in modeling the structure of DSM-5 PTSD symptoms, and suggest differential associations between specific DSM-5 PTSD symptoms and clinical outcomes in trauma survivors. Implications of these results for informing the assessment and treatment of this disorder, are discussed.
» Epskamp, S. & Fried, E. I. (accepted). A Tutorial on Regularized Partial Correlation Networks. Psychological Methods. Preprint & Data. (Abstract)
Recent years have seen an emergence of network modeling for psychological behaviors, moods and attitudes. In this framework, psychological variables are understood to directly interact with each another rather than being caused by an unobserved latent entity. Here we introduce the reader to the most popularly used network model for estimating such psychological networks: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure on cross-sectional psychological data. We demonstrate the method in an empirical example on post-traumatic stress disorder data, showing the effect of the hyperparameter that needs to be manually set by the researcher. In addition, we list several common problems and questions arising in the estimation of regularized partial correlation networks.
» Epskamp, S., Borsboom, D., Fried, E. I. (2017). Estimating psychological networks and their accuracy: a tutorial paper. Behavioral Research Methods. PDF, Data. (Abstract)
The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.