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Group processes = data-driven comput...

Pilny, Andrew.

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Group processes = data-driven computational approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Group processes/ edited by Andrew Pilny, Marshall Scott Poole.
Reminder of title:	data-driven computational approaches /
other author:	Pilny, Andrew.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	vi, 206 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Response Surface Models to Analyze Nonlinear Group Phenomena -- Causal Inference using Bayesian Network -- A Relational Event Approach to Modeling Behavioral Dynamics -- Text Mining Tutorial -- Sequential Synchronization Analysis -- Group Analysis using Machine Learning Techniques -- Simulation and Virtual Experimentation: Grounding with Empirical Data.
Contained By:	Springer eBooks
Subject:	Social sciences - Statistical methods -
Online resource:	http://dx.doi.org/10.1007/978-3-319-48941-4
ISBN:	9783319489414

Group processes = data-driven computational approaches /
Group processesdata-driven computational approaches /[electronic resource] :edited by Andrew Pilny, Marshall Scott Poole. - Cham :Springer International Publishing :2017. - vi, 206 p. :ill., digital ;24 cm. - Computational social sciences,2509-9574. - Computational social sciences..

Introduction -- Response Surface Models to Analyze Nonlinear Group Phenomena -- Causal Inference using Bayesian Network -- A Relational Event Approach to Modeling Behavioral Dynamics -- Text Mining Tutorial -- Sequential Synchronization Analysis -- Group Analysis using Machine Learning Techniques -- Simulation and Virtual Experimentation: Grounding with Empirical Data.

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.

ISBN: 9783319489414

Standard No.: 10.1007/978-3-319-48941-4doiSubjects--Topical Terms:

560158
Social sciences
--Statistical methods

LC Class. No.: HA29

Dewey Class. No.: 300.15195

Group processes = data-driven computational approaches /
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505 0 $a Introduction -- Response Surface Models to Analyze Nonlinear Group Phenomena -- Causal Inference using Bayesian Network -- A Relational Event Approach to Modeling Behavioral Dynamics -- Text Mining Tutorial -- Sequential Synchronization Analysis -- Group Analysis using Machine Learning Techniques -- Simulation and Virtual Experimentation: Grounding with Empirical Data.
520 $a 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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