Statistical Aspects of Group Dynamics : Multilevel Methods for Emergent Processes in Teams
Baurne, Yvette LU
(2025)
- Abstract
- Teams are dynamic systems that develop, adapt, and change as members interact and respond to their environments. Theory in organizational research emphasizes that team phenomena are multilevel, temporal, and often nonlinear. Yet, the statistical methods commonly used to study teams have lagged behind these theoretical advances, limiting empirical progress. This dissertation addresses this methodological shortcoming through three papers.
In Paper I, we critically examine existing approaches to the empirical estimation of consensus emergence, the process through which initially diverse individual perceptions converge into a shared team perspective. We introduce a formal statistical definition of consensus emergence and demonstrate... (More) - Teams are dynamic systems that develop, adapt, and change as members interact and respond to their environments. Theory in organizational research emphasizes that team phenomena are multilevel, temporal, and often nonlinear. Yet, the statistical methods commonly used to study teams have lagged behind these theoretical advances, limiting empirical progress. This dissertation addresses this methodological shortcoming through three papers.
In Paper I, we critically examine existing approaches to the empirical estimation of consensus emergence, the process through which initially diverse individual perceptions converge into a shared team perspective. We introduce a formal statistical definition of consensus emergence and demonstrate common pitfalls, such as conflated variance components and model misspecification.
In Paper II, we extend heterogeneous variance models by integrating Gaussian processes. This framework provides a flexible way to capture nonlinear changes in variability over time, thereby allowing richer insights into how convergence and divergence unfold within teams.
In Paper III, we turn to the evolution and consequences of emergent states. Using the development of new venture teams as an empirical context, we propose a joint modeling framework to study how trust trajectories are shaped by significant events and, in turn, how trust predicts member departure. The model further accounts for non-ignorable missing data through a shared-parameter specification.
Together, these contributions advance the methodological toolkit for studying emergent team phenomena. By aligning statistical models more closely with theoretical advances, the dissertation provides researchers with tools to rigorously examine how collective states form, evolve, and influence outcomes in dynamic organizational settings. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/970364cb-79d3-433b-ab4b-2b003bce2038
- author
- Baurne, Yvette
LU
- supervisor
-
- Jonas Wallin LU
- Frédéric Delmar LU
- opponent
-
- Professor Hamaker, Ellen, Utrecht University
- organization
- publishing date
- 2025
- type
- Thesis
- publication status
- published
- subject
- keywords
- emergent states, heterogeneous variance models, multilevel modeling, Gaussian processes, joint modeling
- pages
- 200 pages
- publisher
- Lunds universitet
- defense location
- EC3:207
- defense date
- 2025-10-21 10:15:00
- ISBN
- 978-91-8104-639-7
- 978-91-8104-638-0
- project
- The Statistics of Entrepreneurship
- language
- English
- LU publication?
- yes
- id
- 970364cb-79d3-433b-ab4b-2b003bce2038
- date added to LUP
- 2025-09-25 16:38:15
- date last changed
- 2025-09-26 13:27:21
@phdthesis{970364cb-79d3-433b-ab4b-2b003bce2038,
abstract = {{Teams are dynamic systems that develop, adapt, and change as members interact and respond to their environments. Theory in organizational research emphasizes that team phenomena are multilevel, temporal, and often nonlinear. Yet, the statistical methods commonly used to study teams have lagged behind these theoretical advances, limiting empirical progress. This dissertation addresses this methodological shortcoming through three papers.<br/><br/>In Paper I, we critically examine existing approaches to the empirical estimation of consensus emergence, the process through which initially diverse individual perceptions converge into a shared team perspective. We introduce a formal statistical definition of consensus emergence and demonstrate common pitfalls, such as conflated variance components and model misspecification.<br/><br/>In Paper II, we extend heterogeneous variance models by integrating Gaussian processes. This framework provides a flexible way to capture nonlinear changes in variability over time, thereby allowing richer insights into how convergence and divergence unfold within teams.<br/><br/>In Paper III, we turn to the evolution and consequences of emergent states. Using the development of new venture teams as an empirical context, we propose a joint modeling framework to study how trust trajectories are shaped by significant events and, in turn, how trust predicts member departure. The model further accounts for non-ignorable missing data through a shared-parameter specification.<br/><br/>Together, these contributions advance the methodological toolkit for studying emergent team phenomena. By aligning statistical models more closely with theoretical advances, the dissertation provides researchers with tools to rigorously examine how collective states form, evolve, and influence outcomes in dynamic organizational settings.}},
author = {{Baurne, Yvette}},
isbn = {{978-91-8104-639-7}},
keywords = {{emergent states; heterogeneous variance models; multilevel modeling; Gaussian processes; joint modeling}},
language = {{eng}},
publisher = {{Lunds universitet}},
school = {{Lund University}},
title = {{Statistical Aspects of Group Dynamics : Multilevel Methods for Emergent Processes in Teams}},
url = {{https://lup.lub.lu.se/search/files/228453817/Kappa_Yvette_Baurne_onlineversion.pdf}},
year = {{2025}},
}