Search for: All records

Computational literacy, broadly defined as having the skills and knowledge to use computing technology, provides new avenues for students to connect sociological concepts with empirical cases. However, integrating computational literacy into undergraduate sociology curricula presents logistical and other challenges for instructors. This article explores how to overcome these challenges by using software to integrate computational skill building into existing topical courses. A case study of a social network analysis course is presented to illustrate this integration in practice. Although students’ experiences in this course were positive, we identify key difficulties in administering the course, such as time constraints and students’ varied computational expertise. We discuss recommendations for overcoming these difficulties and conclude that developing computational literacy in topical electives is possible. Such an approach provides students new ways to engage with complex social issues. 

The use of network analysis as a tool has increased exponentially as more clinical researchers see the benefits of network data for modeling of infectious disease transmission or translational activities in a variety of areas, including patient-caregiving teams, provider networks, patient-support networks, and adoption of health behaviors or treatments, to name a few. Yet, relational data such as network data carry a higher risk of deductive disclosure. Cases of reidentification have occurred and this is expected to become more common as computational ability increases. Recent data sharing policies aim to promote reproducibility, support replicability, and protect federal investment in the effort to collect these research data by making them available for secondary analyses. However, typical practices to protect individual-level clinical research data may not be sufficiently protective of participant privacy in the case of network data, nor in some cases do they permit secondary data analysis. When sharing data, researchers must balance security, accessibility, reproducibility, and adaptability (suitability for secondary analyses). Here, we provide background about applying network analysis to health and clinical research, describe the pros and cons of applying typical practices for sharing clinical data to network data, and provide recommendations for sharing network data.