Computational Methods To Examine Team Communication

The primary focus of this book is an examination of longitudinal team communication and its impact on team performance. This theoretically-grounded, holistic examination of team communication includes cross-condition comparisons of team (i.e., distributed/in person, unrestricted/time pressured, two performance episodes) and employs multiple quantitative methodological approaches to examine the phenomena of interest. This book simultaneously provides practical content for researchers and practitioners in the social sciences and humanities. Included are step-by-step instructions for the methodologies employed, and distillations of findings via Managerial Minutes that highlight best practices and/or examples to help enhance team communication in practice.

Computational Methods for Communication Science showcases the use of innovative computational methods in the study of communication. This book discusses the validity of using big data in communication science and showcases a number of new methods and applications in the fields of text and network analysis. Computational methods have the potential to greatly enhance the scientific study of communication because they allow us to move towards collaborative large-N studies of actual behavior in its social context. This requires us to develop new skills and infrastructure and meet the challenges of open, valid, reliable, and ethical "big data" research. This volume brings together a number of leading scholars in this emerging field, contributing to the increasing development and adaptation of computational methods in communication science. The chapters in this book were originally published as a special issue of the journal Communication Methods and Measures.

This collection provides a primer to the process and promise of computational modeling for industrial-organizational psychologists. With contributions by global experts in the field, the book is designed to expand readers’ appreciation for computational modeling via chapters focused on key modeling achievements in domains relevant to industrial-organizational psychology, including decision making in organizations, diversity and inclusion, learning and training, leadership, and teams. To move the use of computational modeling forward, the book includes specific how-to-chapters on two of the most commonly used modeling approaches: agent-based modeling and system dynamics modeling. It also gives guidance on how to evaluate these models qualitatively and quantitatively, and offers advice on how to read, review, and publish papers with computational models. The authors provide an extensive description of the myriad of values computational modeling can bring to the field, highlighting how they offer a more transparent, precise way to represent theories and can be simulated to offer a test of the internal consistency of a theory and allow for predictions. This is accompanied by an overview of the history of computational modeling as it relates to I-O psychology. Throughout, the authors reflect on computational modeling’s journey, looking back to its history as they imagine its future in I-O psychology. Each contribution demonstrates the value and opportunities computational modeling can provide the individual researcher, research teams, and fields of I-O psychology and management. This volume is an ideal resource for anyone interested in computational modeling, from scholarly consumers to computational model creators.

This volume considers the current research of group communication scholars, provides an overview of major foci in the discipline, and points toward possible trajectories for future scholarship. It establishes group communication’s central role within research on human behaviour and fosters an identity for group communication researchers.

Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.

Artificial intelligence provides an environmentally rich paradigm within which design research based on computational constructions can be carried out. This has been one of the foundations for the developing field called "design computing". Recently, there has been a growing interest in what designers do when they design and how they use computational tools. This forms the basis of a newly emergent field called "design cognition" that draws partly on cognitive science. This new conference series aims to provide a bridge between the two fields of "design computing" and "design cognition". The papers in this volume are from the "First International Conference on Design Computing and Cognition" (DCC'04) held at the Massachusetts Institute of Technology, USA. They represent state-of-the art research and development in design computing and cognition. They are of particular interest to researchers, developers and users of advanced computation in design and those who need to gain a better understanding of designing.

Artificial intelligence provides an environmentally rich paradigm within which design research based on computational constructions can be carried out. This has been one of the foundations for the developing field called "design computing". Recently, there has been a growing interest in what designers do when they design and how they use computational tools. This forms the basis of a newly emergent field called "design cognition" that draws partly on cognitive science. This new conference series aims to provide a bridge between the two fields of "design computing" and "design cognition". The papers in this volume are from the "First International Conference on Design Computing and Cognition" (DCC'04) held at the Massachusetts Institute of Technology, USA. They represent state-of-the art research and development in design computing and cognition. They are of particular interest to researchers, developers and users of advanced computation in design and those who need to gain a better understanding of designing.