Group Processes: Data-Driven Computational Approaches

ISBN-13: 9783319489407 / Angielski / Twarda / 2017 / 206 str.

Andrew Pilny; Marshall Scott Poole

Opis

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples.

Spis treści

Introduction.- Group Processes in the Age of CSS and Big Data.- Group Processes Inputs.- Response Surface Methodology for Nonlinear Factors.- Algorithms for Team Assembly.- Group Processes Dynamics.- Relational Event Analysis for Group Interaction.- Automated Text Mining to Analyze Communication Content.- Social Sequence Analysis Approach for Action Groups.- Group Processes Outputs.- Probablistic Graphical Models to Analyze Causality.- Machine Learning Classification to Understand Group Performance.

O autorze

Andrew Pilny (Ph.D., University of Illinois) is an assistant professor in the Department of Communication at the University of Kentucky. His main research areas concern the development of effective organizing social systems and structures. His research has been applied to nonprofit organizations, social movement groups, work teams, and dark organizations. Andy also specializes in social network analysis.

Marshall Scott Poole (Ph.D., University of Wisconsin) is the David L. Swanson Professor of Communication, Senior Research Scientist at the National Center for Supercomputing Applications, and Director of I-CHASS: The Institute for Computing in the Humanities, Arts, and Social Sciences at the University of Illinois. He is also a CCSS Fellow in the Organization Science Program at Vrije University in Amsterdam, Netherlands. His research interests include group and organizational communication, information and communication technologies, collaboration, organizational change and innovation, and theory construction.

 

Blurb

This volume introduces a series of different data-driven computational methods for analyzing group processes through didactic and tutorial-based examples. Group processes are of central importance to many sectors of society, including government, the military, health care, and corporations. Computational methods are better suited to handle (potentially huge) group process data than traditional methodologies because of their more flexible assumptions and capability to handle real-time trace data.

Indeed, the use of methods under the name of computational social science have exploded over the years. However, attention has been focused on original research rather than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Although the methods here can be applied to wider areas of social science, they are specifically tailored to group process research.

A number of data-driven methods adapted to group process research are demonstrated in this current volume. These include text mining, relational event modeling, social simulation, machine learning, social sequence analysis, and response surface analysis. In order to take advantage of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in various contexts, setting guidelines and best practices for future work to build upon.

This volume will be of great benefit to those willing to learn computational methods. These include academics like graduate students and faculty, multidisciplinary professionals and researchers working on organization and management science, and consultants for various types of organizations and groups.