College of Science and Health Theses and Dissertations

Date of Award

Spring 6-10-2022

Degree Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Goran Kuljanin, PhD

Second Advisor

Kimberly Quinn, PhD

Third Advisor

Shelly Rauvola, PhD


Studying team processes is critical to understanding how teams work to achieve team outcomes. To effectively study team processes, behavioral activities team members enact must be measured with sufficient granularity and intensity. Analyzing the detailed mechanics of team processes requires employing analytical methods sensitive to modeling the series of actions and interactions of team members as they execute taskwork and teamwork over time. Current empirical investigation of team processes lags with respect to intricately measuring and assessing team processes over time. Using dynamic network models, this dissertation sought to understand the behaviors responsible for interaction patterns amongst team members, how those interaction patterns and structures relate to team member behavior, and how interactive team processes relate to team outcomes. Specifically, this dissertation utilized interaction-level data from the National Basketball Association (NBA) and applied three dynamic network models to the data: Separable Temporal Exponential Random Graph Modeling (STERGM), Stochastic Actor-Oriented Modeling (SAOM), and Relational Event Modeling (REM). The purpose of this dissertation is to provide a descriptive foundation for future studies using theories of time to study team phenomena and to demonstrate the utility of dynamic network models. This dissertation details the theoretical foundations of team processes and network analysis, the temporal extensions of traditional network analyses, the utility and applicability of dynamic network models (STERGM, SAOM and REM) using NBA data, and shows insights these methods provide for studying team processes. Results of this dissertation showed reciprocity to be the strongest passing pattern amongst NBA teams, followed by transitive passing patterns. Specifically, NBA players in the 2016-2017 season frequently formed mutual (between two players) and transitive (between three players) passing relations. Player position and scoring behavior were not found to influence passing patterns, nor was home versus away status. Forming mutual and transitive ties related to team wins based on STERGM analyses but similar passing patterns were not found to predict wins with REM analyses, reinforcing methodological and analytical differences in these dynamic network methods. This dissertation discusses the applicability, utility, and implications of applying these dynamic network models to studying team processes and provides practical information about how these methods can be used to inform future research and practice on team dynamics.

SLP Collection