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Group Processes: Data-Driven Computational Approaches (Computational Social Sciences)

Amazon.com Price:  $76.07 (as of 12/05/2019 16:16 PST- Details)

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Description

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 take care of (potentially huge) group process data than traditional methodologies as a result of their more flexible assumptions and capability to take care of real-time trace data.
Indeed, using methods under the name of computational social science have exploded through the years. On the other hand, attention has been focused on original research fairly than pedagogy, leaving those interested in obtaining computational skills lacking a much needed resource. Even if 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. With a purpose to make the most of these new opportunities, this book provides clear examples (e.g., providing code) of group processes in quite a lot of 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 quite a lot of types of organizations and groups.

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