國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Group processesdata-driven computati...

Pilny, Andrew.

Group processesdata-driven computational approaches /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Group processesedited by Andrew Pilny, Marshall Scott Poole.
其他題名:	data-driven computational approaches /
其他作者:	Pilny, Andrew.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	vi, 206 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Social sciencesStatistical methods
電子資源:	http://dx.doi.org/10.1007/978-3-319-48941-4
ISBN:	9783319489414$q(electronic bk.)

Group processesdata-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$q(electronic bk.)

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

224754
Social sciences
--Statistical methods

LC Class. No.: HA29

Dewey Class. No.: 300.15195

Group processesdata-driven computational approaches /
LDR:03066nmm a2200325 a 4500 001 508872
003 DE-He213
005 20170308021210.0
006 m d
007 cr nn 008maaau
008 171121s2017 gw s 0 eng d
020 $a 9783319489414$q(electronic bk.)
020 $a 9783319489407$q(paper)
024 7 $a 10.1007/978-3-319-48941-4 $2 doi
035 $a 978-3-319-48941-4
040 $a GP $c GP
041 0 $a eng
050 4 $a HA29
072 7 $a UGK $2 bicssc
072 7 $a COM072000 $2 bisacsh
082 0 4 $a 300.15195 $2 23
090 $a HA29 $b .G882 2017
245 0 0 $a Group processes $h [electronic resource] : $b data-driven computational approaches / $c edited by Andrew Pilny, Marshall Scott Poole.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a vi, 206 p. : $b ill., digital ; $c 24 cm.
490 1 $a Computational social sciences, $x 2509-9574
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.
650 0 $a Social sciences $x Statistical methods $x Data processing. $3 224754
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Methodology of the Social Sciences. $3 275266
650 2 4 $a Big Data/Analytics. $3 742047
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Industrial and Organizational Psychology. $3 759694
650 2 4 $a Knowledge Management. $3 277143
700 1 $a Pilny, Andrew. $3 775300
700 1 $a Poole, Marshall Scott. $3 775301
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Computational social sciences. $3 676494
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-48941-4
950 $a Computer Science (Springer-11645)