東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Group processes = data-driven comput...

Pilny, Andrew.

FindBook

Google Book

Amazon

博客來

Group processes = data-driven computational approaches /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Group processes/ edited 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.
內容註:	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
標題:	Social sciences - Statistical methods -
電子資源:	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 /
LDR:03066nmm a2200325 a 4500 001 2092565
003 DE-He213
005 20170308021210.0
006 m d
007 cr nn 008maaau
008 171117s2017 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 560158
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Methodology of the Social Sciences. $3 895565
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Industrial and Organizational Psychology. $3 3039266
650 2 4 $a Knowledge Management. $3 900242
700 1 $a Pilny, Andrew. $3 3226530
700 1 $a Poole, Marshall Scott. $3 3226531
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Computational social sciences. $3 2056273
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-48941-4
950 $a Computer Science (Springer-11645)