Group processes: data-driven computational approaches
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. Computat...
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2017]
|
| Schriftenreihe: | Computational social sciences
|
| Schlagwörter: | |
| Zusammenfassung: | 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 |
| Umfang: | v, 206 Seiten Illustrationen, Diagramme (überwiegend farbig) |
| ISBN: | 9783319489407 |
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| spelling | Group processes data-driven computational approaches Andrew Pilny, Marshall Scott Poole, editors Cham, Switzerland Springer [2017] © 2017 v, 206 Seiten Illustrationen, Diagramme (überwiegend farbig) txt rdacontent n rdamedia nc rdacarrier Computational social sciences 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 Computer science Knowledge management Big data Data mining Computer simulation Social sciences Pilny, Andrew (DE-588)1130790576 edt Poole, Marshall Scott 1951- (DE-588)135623588 edt Erscheint auch als Online-Ausgabe 978-3-319-48941-4 |
| spellingShingle | Group processes data-driven computational approaches Computer science Knowledge management Big data Data mining Computer simulation Social sciences |
| title | Group processes data-driven computational approaches |
| title_auth | Group processes data-driven computational approaches |
| title_exact_search | Group processes data-driven computational approaches |
| title_full | Group processes data-driven computational approaches Andrew Pilny, Marshall Scott Poole, editors |
| title_fullStr | Group processes data-driven computational approaches Andrew Pilny, Marshall Scott Poole, editors |
| title_full_unstemmed | Group processes data-driven computational approaches Andrew Pilny, Marshall Scott Poole, editors |
| title_short | Group processes |
| title_sort | group processes data driven computational approaches |
| title_sub | data-driven computational approaches |
| topic | Computer science Knowledge management Big data Data mining Computer simulation Social sciences |
| topic_facet | Computer science Knowledge management Big data Data mining Computer simulation Social sciences |
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