Our tools let participants and partnering researchers and analysts examine how networks influence important goals, behaviors, or important case studies at various levels.
Scientifically, we use psychological surveys, network goal analysis, and computer visualization to describe and explain these powerful systems. This allows participants or collaborating researchers to then brainstorm network goal interventions to help achieve goals, sustain performance, or improve network relations and climates in all types of social, organizational, and international systems.
Eligible individuals or groups can sign-up to participate in our survey research (IRB Approved Protocol number 18-144). Participants will assess their own important goal and the critical network associated with it. Or, they can examine their more general life, work, or case study systems. Once finished, our Lab sends participants (or partnering researchers) a confidential file from which they can visualize their system online with dynamic graphics. Our reports are for private use only.
Once participants or researchers receive their confidential report(s), typically by email, they can start visualizing how their networks are functioning. The computerized report also displays relatively easy-to-understand graphs and statistics, presuming a basic familiarity with the roles in dynamic network theory. Importantly, this report breaks down participants' systems into meaningful sub-networks, to better understand motivational dynamics, such as examining one's system support and conflict sub-networks.
After participants or partnering researchers/analysts have visualized and analyzed their network goal graphs and results, the Lab encourages them to work on network goal interventions. For this, participants can start applying techniques from dynamic network theory to improve their systems, such as intervening to get more partner goal strivers, more competent system supporters, and reducing dysfunctional conflict and resistance around the goal. Click here for some intervention suggestions, which would be contextualized to the specific system under study. Our approach continues to integrate and call upon the field of psychological and behavioral science to inform evidenced-based approaches. This can be self-taught to some extent or gained through advanced education. In all, the survey-based results in conjunction with using propositions from dynamic network theory should provide many insights into effective change interventions.
We also use other social network analysis programs to transform concepts from dynamic network theory into computer visuals. The concepts in DNT can be used in a wide range of visualization programs.