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Modern Research Methods for the Study of Behavior in Organizations (SIOP Organizational Frontiers Series) - Hardcover

 
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Synopsis

The goal of the chapters in this SIOP Organizational Frontiers Series volume is to challenge researchers to break away from the rote application of traditional methodologies and to capitalize upon the wealth of data collection and analytic strategies available to them. In that spirit, many of the chapters in this book deal with methodologies that encourage organizational scientists to re-conceptualize phenomena of interest (e.g., experience sampling, catastrophe modeling), employ novel data collection strategies (e.g., data mining, Petri nets), and/or apply sophisticated analytic techniques (e.g., latent class analysis). The editors believe that these chapters provide compelling solutions for the complex problems faced by organizational researchers.  

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About the Author

Jose M. Cortina is a Professor in the Industrial/Organizational Psychology program at George Mason University. His recent research has involved topics in meta-analysis, structural equation modeling, significance testing, and philosophy of science, as well as predictors and outcomes of emotions in the workplace. He currently serves as Editor of Organizational Research Methods and is a former Associate Editor of the Journal of Applied Psychology. Dr. Cortina was honored by SIOP with the 2001 Ernest J. McCormick Award for Distinguished Early Career Contributions, by the Research Methods Division of the Academy of Management with the 2004 Robert O. McDonald Best Paper Award, and by the Organizational Research Methods Editorial Board with the 2012 Best Paper Award. He was also honored by George Mason University with a 2010 Teaching Excellence Award and by SIOP with the 2011 Distinguished Teaching Award.

Ronald S. Landis is Nambury S. Raju Endowed Professor in the College of Psychology at Illinois Institute of Technology. He has also served on the faculty at Tulane University, where he was awarded the Tulane President’s Award for Excellence in Graduate and Professional Teaching in 2004. He is a Fellow of SIOP and was honored by the Organizational Research Methods Editorial Board with the 2012 Best Paper Award. He has primary research interests in the areas of structural equation modeling, multiple regression, and other issues associated with measurement and the prediction of performance. He is currently an Associate Editor of the Journal of Business and Psychology and a former Associate Editor of Personnel Psychology.

Review

"When I first heard of the idea for this book a few years back, I was truly excited. Now that it's finished, I'm truly amazed. Professors Cortina and Landis not only identified a set of topics that will move organizational research forward, but also recruited some of the most knowledgeable people in the world to write on them. This book needs to be required reading in any research methods course oriented toward the organizational sciences. It will truly get students to think about research design issues very differently." - Bob Vandenberg, University of Georgia, Professor of Management, Past Editor, Organizational Research Methods

"Cortina and Landis bring a wide range of research methods that are not familiar to I/O psychologists to the attention of this community. Their introductions of techniques such as catastrophe theory, social network analysis, latent class analysis, petri nets, and experience sampling (to name only a few of the techniques described in this volume) will add breadth and depth to the toolbox of I/O scientists and practitioners alike." - Kevin R. Murphy, Colorado State University

"Scientific progress accelerates when newer methodological approaches allow for the novel examination of enduring issues. I am confident that the methodological approaches described in this wonderful volume will lead to advancements in many important domains for years to come." - Herman Aguinis, Kelley School of Business, Indiana University

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ISBN 10:  1138801704 ISBN 13:  9781138801707
Publisher: Routledge, 2014
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