368 pages | 42 B/W Illus.
The amount of data in our world has been exploding, and analyzing large data sets—so called big data—will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.
'This is an essential must-read book for any one interested in doing or being a consumer of big data. It is a brilliant collection of contributors and articles providing great clarity to a challenging and critical area. This is the exact book needed to help navigate the data science revolution.' — Steven G. Rogelberg, University of North Carolina, Charlotte, USA
'Many fields have embraced Big Data and have sought to exploit the potential benefits. The editorial team of this book has brought together a strong collection of chapters that explore the meaning and impact of Big Data on the field of I/O psychology. Despite a rapidly changing landscape, this book will certainly prove to be valuable resource.' — Ron Landis, Illinois Institute of Technology, USA
'Overall, this is an exciting book and there is much to learn here. The book provides a framework for understanding the Big Data movement (beyond just the buzz of it), some practical applications, and suggestions for how I-O psychology is changing in the light of this new landscape.' -Edgar E. Kausel, Associate Professor ofManagement, Faculty of Economic and Administrative Sciences, Universidad Católica de Chile, Santiago, Chile
Foreword Richard Klimoski
1. Building Understanding of the Data Science Revolution and IO Psychology Eden B. King, Scott Tonidandel, Jose M. Cortina, & Alexis A. Fink
Part I: Big Issues for Big Data Methods
2. Big Data Platform Jacqueline Ryan
3. Statistical Methods for Big Data: A Scenic Tour Frederick L. Oswald & Dan J. Putka
4. Twitter Analysis: Methods for Data Management and a Word Count Dictionary to Measure City-Level Job Satisfaction Ivan Hernandez, Daniel A. Newman, & Gahyun Jeon
5. Data Visualization Evan F. Sinar
6. Sensing Big Data: Multimodal Information Interfaces for Exploration of Large Data Sets Jeffrey Stanton
Part II: Big Ideas for Big Data in Organization
7. Implications of the Big Data Movement for the Advancement I-O Science and Practice Dan J. Putka & Frederick L. Oswald
8. Big Data in Talent Selection and Assessment A. James Illingworth, Michael Lippstreu, & Anne-Sophie Deprez-Sims
9. Big Data in Turnover/Retention John P. Hausknecht & Huisi (Jessica) Li
10. Using Big Data to Advance the Science of Team Effectiveness Steve W. J. Kozlowski, Georgia T. Chao, Chu-Hsiang (Daisy) Chang, & Rosemarie Fernandez
11. Using Big Data to Create Diversity and Inclusion in Organizations Whitney Botsford Morgan, Eric Dunleavy, & Peter D. DeVries
12. How Big Data Matters Richard A. Guzzo
The Series of the Society for Industrial and Organizational Psychology (SIOP)
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Launched in 1983 to make scientific contributions to the field, this series has attempted to publish books on cutting edge theory and research derived from practice in industrial and organizational psychology and related organizational science disciplines.
The goal of the series is to inform and stimulate research for SIOP members (students, practitioners and researchers) and people in related disciplines, including other subdisciplines of psychology, organizational behavior, human resource management, and labor and industrial relations.
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