Network Analysis of Competitive State Anxiety

• Main Authors: Richard Mullen, Eleri Sian Jones
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-01-01
• Series: Frontiers in Psychology
• Subjects: anxiety; network analysis; predictability; community detection; graph theory; state anxiety
• Online Access: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.586976/full

Competitive state anxiety is an integral feature of sports performance but despite its pervasiveness, there is still much debate concerning the measurement of the construct. Adopting a network approach that conceptualizes symptoms of a construct as paired associations, we proposed re-examining competitive state anxiety as a system of interacting components in a dataset of 485 competitive athletes from the United Kingdom. Following a process of data reduction, we estimated a network structure for 15 items from the modified Three Factor Anxiety Inventory using the graphical LASSO algorithm. We then examined network connectivity using node predictability. Exploratory graph analysis was used to detect communities in the network and bridge expected influence calculated to estimate the influence of items from one community to items in other communities. The resultant network produced a range of node predictability values. Community detection analysis derived three communities that corresponded with previous research and several nodes were identified that bridged these communities. We conclude that network analysis is a useful tool to explore the competitive state anxiety response and we discuss how the results of our analysis might inform the assessment of the construct and how this assessment might inform interventions.