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Hormones, behavior, and social network analysis: exploring associations between cortisol, testosterone, and network
Olga Kornienko1, Katherine H Clemans2, Dorothée Out3
1Institute for Interdisciplinary Salivary Bioscience Research, Arizona State University, USA; Department of Psychology, Arizona State University, USA.
Hormones and Behavior
|July 30, 2014
Summary
Higher cortisol levels were linked to less social activity in nursing students. Testosterone levels did not show a connection to friendship network structures in this study.
Area of Science:
- Integrates social network analysis (SNA) and bioscience for studying human social ecologies.
- Applies interdisciplinary methods to explore biobehavioral processes.
Background:
- Hormonal influences on social behavior are complex and require advanced analytical approaches.
- Understanding social network structures provides insight into group dynamics and individual behavior.
Purpose of the Study:
- To investigate the association between salivary hormone levels and social network structures.
- To test hypotheses regarding the relationship between cortisol, testosterone, and network characteristics.
- To introduce a novel approach combining SNA and salivary bioscience.
Main Methods:
- Utilized exponential random graph modeling (ERGM) to analyze social network data.
- Collected friendship nominations and saliva samples from first-year nursing students (n=74).
- Assayed saliva for cortisol and testosterone levels.
Main Results:
- Salivary cortisol levels were inversely associated with the number of outgoing ties (network activity).
- Testosterone levels did not show a significant relationship with friendship network structure.
- ERGM revealed specific patterns in hormone-behavior associations within the social network.
Conclusions:
- Salivary cortisol is associated with reduced network activity, suggesting a role in social engagement.
- Testosterone does not appear to influence friendship network structure in this cohort.
- The integration of SNA and bioscience offers a powerful framework for examining hormone-behavior dynamics.
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