TY - GEN
T1 - Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls
AU - Antone, Brennan
AU - Gupta, Aryaman
AU - Bell, Suzanne
AU - DeChurch, Leslie
AU - Contractor, Noshir
N1 - Funding Information:
This material is based upon work supported by NASA under award numbers NNX15AM32G, NNX15AM26G, and 80NSSC18K0221. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - We demonstrate an approach to perform significance testing on the association between two different network-level properties, based on the observation of multiple networks over time. This approach may be applied, for instance, to evaluate how patterns of social relationships within teams are associated with team performance on different tasks. We apply this approach to understand the team processes of crews in long-duration space exploration analogs. Using data collected from crews in NASA analogs, we identify how interpersonal network patterns among crew members relate to performance on various tasks. In our significance testing, we control for complex interdependencies between network ties: structural patterns, such as reciprocity, and temporal patterns in how ties tend to form or dissolve over time. To accomplish this, Separable Temporal Exponential Random Graph Models (STERGMs) are used as a parametric approach for sampling from the null distribution, in order to calculate p-values.
AB - We demonstrate an approach to perform significance testing on the association between two different network-level properties, based on the observation of multiple networks over time. This approach may be applied, for instance, to evaluate how patterns of social relationships within teams are associated with team performance on different tasks. We apply this approach to understand the team processes of crews in long-duration space exploration analogs. Using data collected from crews in NASA analogs, we identify how interpersonal network patterns among crew members relate to performance on various tasks. In our significance testing, we control for complex interdependencies between network ties: structural patterns, such as reciprocity, and temporal patterns in how ties tend to form or dissolve over time. To accomplish this, Separable Temporal Exponential Random Graph Models (STERGMs) are used as a parametric approach for sampling from the null distribution, in order to calculate p-values.
KW - Network properties
KW - Separable temporal exponential random graph models
KW - Team performance
UR - http://www.scopus.com/inward/record.url?scp=85087881192&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087881192&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36683-4_81
DO - 10.1007/978-3-030-36683-4_81
M3 - Conference contribution
AN - SCOPUS:85087881192
SN - 9783030366827
T3 - Studies in Computational Intelligence
SP - 1018
EP - 1030
BT - Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
A2 - Cherifi, Hocine
A2 - Gaito, Sabrina
A2 - Mendes, José Fernendo
A2 - Moro, Esteban
A2 - Rocha, Luis Mateus
PB - Springer
T2 - 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
Y2 - 10 December 2019 through 12 December 2019
ER -