TY - BOOK ID - 21470242 TI - Group Processes : Data-Driven Computational Approaches AU - Pilny, Andrew. AU - Poole, Marshall Scott. PY - 2017 SN - 3319489410 3319489402 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Social sciences KW - Statistical methods KW - Data processing. KW - Computer science. KW - Knowledge management. KW - Big data. KW - Data mining. KW - Computer simulation. KW - Social sciences. KW - Computer Science. KW - Simulation and Modeling. KW - Methodology of the Social Sciences. KW - Big Data/Analytics. KW - Data Mining and Knowledge Discovery. KW - Industrial and Organizational Psychology. KW - Knowledge Management. KW - Applied psychology. KW - Methodology. KW - Management of knowledge assets KW - Management KW - Information technology KW - Intellectual capital KW - Organizational learning KW - Applied psychology KW - Psychagogy KW - Psychology, Practical KW - Social psychotechnics KW - Psychology KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Data sets, Large KW - Large data sets KW - Data sets KW - Computer modeling KW - Computer models KW - Modeling, Computer KW - Models, Computer KW - Simulation, Computer KW - Electromechanical analogies KW - Mathematical models KW - Simulation methods KW - Model-integrated computing KW - Industrial psychology. KW - Business psychology KW - Industrial psychology KW - Psychotechnics KW - Industrial engineering KW - Personnel management KW - Psychology, Applied KW - Industrial psychologists KW - Behavioral sciences KW - Human sciences KW - Sciences, Social KW - Social science KW - Social studies KW - Civilization UR - https://www.unicat.be/uniCat?func=search&query=sysid:21470242 AB - 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. ER -