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Data science methods increasingly are utilized to analyze theoretically derived psychological research questions. This article provides a case study of a student-focused research experience that introduced basic data science skills and their utility for psychological research, providing practical learning experiences for students interested in learning computational social science skills. Skills included programming; acquiring, visualizing, and managing data; performing specialized analyses; and building knowledge about open-science practices. Using examples from their teaching experiences, the authors describe how these skills can be incorporated into an active and engaging student learning experience that culminates in computational social science projects and presentations.

Employing a natural language processing approach, we analyzed textual content derived from publicly-available athlete victim impact statements (VIS) from the Larry Nassar trial (N = 111) to examine psychosocial responses to sexual violence. To explore potential differences in a non-sports context, we conducted similar analyses on a sample of #MeToo tweets (N = 45,848). Our research focused on the semantic content of VIS, including positive and negative affect, power and trust dynamics, well-being, and post-traumatic growth. We hypothesized that athletes' reactions to sexual violence would be more likely to contain language related to power and trust. Traditional null-hypothesis significance testing and network analyses were used to identify the psychosocial indicators unique to sexual violence disclosures in a sports context. Results indicated differential use of language related to negative affect, trust, power dynamics, and post-traumatic growth in sports versus non-sports contexts. We discuss clinical, practical, and policy-based implications for risk reduction and intervention. [ABSTRACT FROM AUTHOR]

Social network analysis (SNA) is a uniquely situated methodology to examine the social connections between players on a team, and how team structure may be related to self-reported team cohesion and perceived support for reporting concussion symptoms. Team belonging was positively associated with number of friend.ship ties (degree, r = .23, p < .05), intermediate ties between teammates (betweenness; r = .21, p < .05), and support from both teammates (r =.21, p < .05) and important others (r = .21, p < .05) for reporting concussion symptoms. Additionally, an SNA-derived measure of social influence, eigenvector centrality, was associated with football identity (r = .34, p <.01), and less support from important others (r = -.24, p < .05) regarding symptom reporting. Discussion focuses on why consideration of social influence dynamics may help improve concussion-related education efforts. [ABSTRACT FROM AUTHOR]