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Group Processes

Data-Driven Computational Approaches

Autor: Andrew Pilny, Marshall Scott Poole

Język:

Oprawa: Twarda

Wydawca: Springer International Publishing AG

Dostępność: Dostępna u dostawcy

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This volume introduces a series of different data-driven computational methods for analyzing group p...

Informacje o książce

Autor

Andrew Pilny, Marshall Scott Poole

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2017

strony

206

EAN

9783319489407

ISBN

3319489402

Enbook ID

15194733

Wydawca

Springer International Publishing AG

Waga

4439

Wymiary

155 x 235 x 14

Pełny opis

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.

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Data-Driven Computational Approaches

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