Search

When it comes to cybersecurity incidents – public opinion matters. But how do voters form opinions in the aftermath of cyberattacks that are shrouded in ambiguity? How do people account for the uncertainty inherent in cyberspace to forge preferences following attacks? This article seeks to answer these questions by introducing an uncertainty threshold mechanism predicting the level of attributional certainty required for the public to support economic, diplomatic or military responses following cyberattacks. Using a discrete-choice experimental design with 2025 US respondents, we find lower attributional certainty is associated with less support for retaliation, yet this mechanism is contingent on the suspected identity of the attacker and partisan identity. Diplomatic allies possess a reservoir of good will that amplifies the effect of uncertainty, while rivals are less often given the benefit of the doubt. We demonstrate that uncertainty encourages the use of cognitive schemas to overcome ambiguity, and that people fall back upon pre-existing and politically guided views about the suspected country behind an attack. If the ambiguity surrounding cyberattacks has typically been discussed as an operational and strategic concern, this article shifts the focus of attention to the human level and positions the mass public as a forgotten yet important party during cyber conflict.

Slides for a workshop at the Conference on Higher Education Pedagogy 2024.

Introduction: Racial/ethnic disparities in living donor kidney transplantation (LDKT) are a persistent challenge. Although nearly all directed donations are from members of patients’ social networks, little is known about which social network members take steps toward living kidney donation, which do not, and what mechanisms contribute to racial/ethnic LDKT disparities. Methods: We describe the design and rationale of the Friends and Family of Kidney Transplant Patients Study, a factorial experimental fielding two interventions designed to promote LKD discussions. Participants are kidney transplant candidates at two centers who are interviewed and delivered an intervention by trained center research coordinators. The search intervention advises patients on which social network members are most likely to be LKD contraindication-free; the script intervention advises patients on how to initiate effective LKD discussions. Participants are randomized into four conditions: no intervention, search only, script only, or both search and script. Patients also complete a survey and optionally provide social network member contact information so they can be surveyed directly. This study will seek to enroll 200 transplant candidates. The primary outcome is LDKT receipt. Secondary outcomes include live donor screening and medical evaluations and outcomes. Tertiary outcomes include LDKT self-efficacy, concerns, knowledge, and willingness, measured before and after the interventions. Conclusion: This study will assess the effectiveness of two interventions to promote LKD and ameliorate Black-White disparities. It will also collect unprecedented information on transplant candidates’ social network members, enabling future work to address network member structural barriers to LKD.

Mental wellness is a critical component of healthy development in emerging adulthood and serves to protect against stress and promote resilience against psychopathology. Emotion regulation is a key mechanism for effective prevention because of its role in socio-emotional competence and its transdiagnostic significance for psychopathology. In this feasibility study, a brief, time and cost-effective emotion regulation training program for emerging adults (BERT) was developed and tested using the RE-AIM framework. Importantly, building interventions within the context of an implementation framework, such as the RE-AIM framework, enhances the chances that an intervention will be able to scale out and scale up. First, the brainwriting premortem method was utilized to refine program content, conducting focus groups a priori to identify potential program failures prior to program implementation. Undergraduate students (n = 12) attended four focus groups presenting initial program content. Four clinicians were also interviewed to determine program barriers. Qualitative analyses aggregated participant feedback to identify compliments, changes, and concerns about BERT and critical feedback was immediately implemented prior to initial testing. BERT was rooted in cognitive-behavioral practices and informed by the Gross model of emotion regulation. The 5-week program was then examined in a college sample (N = 42) to evaluate implementation (low attrition, high content engagement, favorable attitudes, low incidence of technical errors, costs), reach (enrollment and completion demographics comparable to the population in which recruitment took place), and efficacy (positive change in emotion regulation pre- to post-program). Of the recruited participants, 36 remained in the study where 27 completed at least 80% of program content. Repeated-measures ANOVAs exhibited significant improvements in emotion regulation, psychological distress, and negative affectivity, suggesting promisin

This report recounts an exploratory investigation of the needs and experiences of instructors who teach with data in the social sciences at Virginia Tech, as part of an ITHAKA S+R project. The landscape of teaching with data in the social sciences at Virginia Tech is complex, involving instructors with a variety of interests and expertise, courses across the undergraduate curriculum, and students with a range of prior knowledge and personal goals related to their own learning. Participants discussed a variety of needs related to student competency, access and technology, program structures, and pedagogy. Multifaceted approaches from multiple university collaborators will be key to addressing these needs. By building on existing digital literacies programming, expanding shared resources like online modules, and exploring opportunities for peer education and further professional development, we can better prepare Virginia Tech students to learn, create, and take action with data throughout their lives.

Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augmentation but may not scale to big data or appease skeptics. We consider a third alternative: data augmentation with online crowdsourcing. Three empirical cases illustrate strengths and limitations of crowdsourcing, using Amazon Mechanical Turk to verify automated coding, link online databases, and gather data on online resources. Using these, we develop best practice guidelines and a reporting template to enhance reproducibility. Carefully designed, correctly applied, and rigorously documented crowdsourcing help address concerns about big data's usefulness for social research.

Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augmentation but may not scale to big data or appease skeptics. We consider a third alternative: data augmentation with online crowdsourcing. Three empirical cases illustrate strengths and limitations of crowdsourcing, using Amazon Mechanical Turk to verify automated coding, link online databases, and gather data on online resources. Using these, we develop best practice guidelines and a reporting template to enhance reproducibility. Carefully designed, correctly applied, and rigorously documented crowdsourcing help address concerns about big data's usefulness for social research.

How to best disseminate one’s research and get credit for one’s work? How to best and fairly assess the quality and impact of a given individual, group, or institution’s research? These are questions with which many are struggling, from individual researchers to departments, to a global world of research institutions. Recently, the Faculty Senate and University Libraries surveyed the faculty of our large, public research university to explore their perspective on these questions and more. In this presentation we present a summary of results from 501 respondents (out of 4451 faculty in total) representing different types of faculty (both within and outside of tenured and tenure-track positions), at different ranks, and from different disciplines. Results shared will indicate trends within the faculty on topics such as, the current most commonly used profile systems (top 5: Google Scholar, ORCID ID, LinkedIn, Elements (internal system), and ResearchGate); which profile systems are used most for: networking and connecting with colleagues (top 3: LinkedIn, Twitter, and ResearchGate), tracking research impact metrics (top 3: Google Scholar, ORCID, Elements (internal system)), showcasing one’s work to increase visibility (top 3:Google Scholar, ResearchGate, self-published sites); what types of research metrics are relied on (top 3: journal reputation (separate from impact factor), number of publications, and citation counts to individual works); the perceived fairness of evaluation by level of review (e.g., department, college, and university levels) and how they differ; and summaries of qualitative responses to questions such as why faculty rely on certain profile systems or research metrics, and perspectives on how fair research evaluation could be accomplished, within or across disciplines. Results will be summarized at the institutional level with breakout analysis of results from some disciplinary fields or other subsets. For us, these results from faculty across a r

Computationally-intensive, cross-disciplinary research collaborations are difficult to create and maintain over time, though many yield impressive results. The need for researchers to share, maintain, and manage data is increasing, while also integrating new tools and approaches to make their work more impactful. The University Libraries at Virginia Tech has a team of disciplinary data and informatics consultants working to connect research environments on campus with emerging library services enabling collaboration across disciplines. Partnerships with university-level research service providers, such as high-performance computing (HPC) services and statistical data consulting, have presented interesting use cases and innovative solutions to common problems. While traditional library services may not overlap with high performance computing environments, new library services (such as data management, publishing, curation, archiving, and preservation) provide new avenues for collaboration and situate the libraries in a unique position in relation to research ecosystems. Moving large datasets from HPC environments into research environments present significant barriers to research data sharing between collaborators; working with libraries to make these datasets better organized and documented lowers some of these barriers. Discipline-specific informatics consulting allows researchers to integrate new tools and approaches to solve research questions. Here, we highlight the utilization, need, and scope of informatics and research data management services in and around libraries, while also providing examples of how these services have created new collaborations and adoption of improved research practices surrounding data management and integration of computationally intensive techniques (e.g. bioinformatics, humanistic informatics, etc.). This work lays the foundation for these services in an academic setting and the influence of such on the practice and experience of u

This report summarizes findings from a 2005 survey of 52 graduates of the Theological Education for Emerging Ministries (TEEM) program of the Evangelical Lutheran Church in America regarding their ministry preparation and call experiences. It is part of a larger study of non-residential seminary outcomes including the published articles "Preparation in Context" (Porter 2016, https://doi.org/10.1007/s13644-016-0246-5).