Objective: Network analysis allows us to identify the most interconnected (i.e., central) symptoms, and multiple authors have suggested that these symptoms might be important treatment targets. This is because change in central symptoms (relative to others) should have greater impact on change in all other symptoms. It has been argued that networks derived from cross-sectional data may help identify such important symptoms. We tested this hypothesis in social anxiety disorder. Method: We first estimated a state-of-the-art regularized partial correlation network based on participants with social anxiety disorder (N = 910) to determine which symptoms were more central. Next, we tested whether change in these central symptoms were indeed more related to overall symptom change in a separate dataset of participants with social anxiety disorder who underwent a variety of treatments (N = 244). We also tested whether relatively superficial item properties (infrequency of endorsement and variance of items) might account for any effects shown for central symptoms. Results: Centrality indices successfully predicted how strongly changes in items correlated with change in the remainder of the items. Findings were limited to the measure used in the network and did not generalize to three other measures related to social anxiety severity. In contrast, infrequency of endorsement showed associations across all measures. Conclusions: The transfer of recently published results from cross-sectional network analyses to treatment data is unlikely to be straightforward.

Recently, there has been a growing emphasis on embedding open and reproducible approaches into research. One essential step in accomplishing this larger goal is to embed such practices into undergraduate and postgraduate research training. However, this often requires substantial time and resources to implement. Also, while many pedagogical resources are regularly developed for this purpose, they are not often openly and actively shared with the wider community. The creation and public sharing of open educational resources is useful for educators who wish to embed open scholarship and reproducibility into their teaching and learning. In this article, we describe and openly share a bank of teaching resources and lesson plans on the broad topics of open scholarship, open science, replication, and reproducibility that can be integrated into taught courses, to support educators and instructors. These resources were created as part of the Society for the Improvement of Psychological Science (SIPS) hackathon at the 2021 Annual Conference, and we detail this collaborative process in the article. By sharing these open pedagogical resources, we aim to reduce the labour required to develop and implement open scholarship content to further the open scholarship and open educational materials movement.

Scientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, and specifying them requires modeling skills. Unfortunately, in psychological science, theories are often not precise, and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. Many Modelers consist of mixed teams of modelers and non-modelers that collaborate to create a formal theory of a phenomenon. We report a proof of concept of this approach, which we piloted as a three-hour hackathon at the SIPS 2021 conference. We find that (a) psychologists who have never developed a formal model can become excited about formal modeling and theorizing; (b) a division of labor in formal theorizing could be possible where only one or a few team members possess the prerequisite modeling expertise; and (c) first working prototypes of a theoretical model can be created in a short period of time.

Background: Dynamic indices calculated from ecological momentary assessment (EMA) data can model individual-level symptom fluctuations over time. However, the clinical utility for using these complex metrics to characterize comorbid symptomatology and to predict future symptom changes has not been adequately examined. Methods: Women (N = 35) with social anxiety disorder and a history of major depression completed a clinical diagnostic interview and self-report measures at baseline and approximately two months later. In between these assessments, participants completed EMA surveys on mood and anxiety symptoms five times a day for approximately 30 days (T = 5,250). Symptom severity and dynamic indices (i.e., variance, inertia) during EMA were calculated. The relative predictive value of these indices for characterizing baseline symptomatology, as well as the relative prospective predictive value of dynamic indices predicting future depressive symptomatology was assessed. Results: Baseline depressive symptoms were associated with mean levels of anxiety (b = .38, p = .001); whereas, baseline social anxiety symptoms were associated with mean levels of depression during EMA (b = -.38, p = .013). Moment-to-moment variance in anxiety during EMA (b = .22, p = .020) and baseline self-report measures of depression (b = .50, p < .001) predicted future depressive symptoms. Conclusions: Moment-to-moment fluctuations in anxiety may constitute a unique early warning sign of future increases in depression for individuals with mood and anxiety comorbidity.