Description
Book SynopsisThis handbook shows scholars how to conduct multilevel research. Chapters discuss the importance of context, dynamics, and complexity, and guide readers through the nuances of research design and analysis
Table of ContentsIntroduction
Part I: Multilevel Theory Chapter 1: On Finding Your Level
Stanley M. Gully and Jean M. Phillips
Chapter 2: Contextualizing Context in Organizational Research
Cheri Ostroff
Chapter 3: Ask Not What the Study of Context Can Do for You: Ask What You Can Do for the Study of Context
Rustin D. Meyer, Katie England, Elnora D. Kelly, Andrew Helbling, MinShuou Li, and Donna Outten
Chapter 4: The Only Constant Is Change: Expanding Theory by Incorporating Dynamic Properties Into One’s Models
Matthew A. Cronin and Jeffrey B. Vancouver
Chapter 5: The Means Are the End: Complexity Science in Organizational Research
Juliet R. Aiken, Paul J. Hanges, and Tiancheng Chen
Chapter 6: The Missing Levels of Microfoundations: A Call for Bottom-Up Theory and Methods
Robert E. Ployhart and Jonathan Hendricks
Chapter 7: Multilevel Emergence in Work Collectives
John E. Mathieu and Margaret M. Luciano
Chapter 8: Multilevel Thoughts on Social Networks
Daniel J. Brass and Stephen P. Borgatti
Chapter 9: Conceptual Foundations of Multilevel Social Networks
Srikanth Paruchuri, Martin C. Goossen, and Corey Phelps
Part II: Multilevel Measurement and Design Chapter 10: Introduction to Data Collection in Multilevel Research
Le Zhou, Yifan Song, Valeria Alterman, Yihao Liu, and Mo Wang
Chapter 11: Construct Validation in Multilevel Studies
Andrew T. Jebb, Louis Tay, Vincent Ng, and Sang Woo
Chapter 12: Multilevel Measurement: Agreement, Reliability, and Nonindependence
Dina V. Krasikova and James M. LeBreton
Chapter 13: Looking Within: An Examination, Combination, and Extension of Within-Person Methods Across Multiple Levels of Analysis
Daniel J. Beal and Allison S. Gabriel
Chapter 14: Power Analysis for Multilevel Research
Charles A. Scherbaum and Erik Pesner
Chapter 15: Explained Variance Measures for Multilevel Models
David M. LaHuis, Caitlin E. Blackmore, and Kinsey B. Bryant-Lees
Chapter 16: Missing Data in Multilevel Research
Simon Grund, Oliver Lüdtke, and Alexander Robitzsch
Part III: Multilevel Analysis Chapter 17: A Primer on Multilevel (Random Coefficient) Regression Modeling
Levi K. Shiverdecker and James M. LeBreton
Chapter 18: Dyadic Data Analysis
Andrew P. Knight and Stephen E. Humphrey
Chapter 19: A Primer on Multilevel Structural Modeling: User-Friendly Guidelines
Robert J. Vandenberg and Hettie A. Richardson
Chapter 20: Moderated Mediation in Multilevel Structural Equation Models: Decomposing Effects of Race on Math Achievement Within Versus Between High Schools in the United States
Michael J. Zyphur, Zhen Zhang, Kristopher J. Preacher, and Laura J. Bird
Chapter 21: Anything but Normal: The Challenges, Solutions, and Practical Considerations of Analyzing Nonnormal Multilevel Data
Miles A. Zachary, Curt B. Moore, and Gary A. Ballinger
Chapter 22: A Temporal Perspective on Emergence: Using Three-Level Mixed-Effects Models to Track Consensus Emergence in Groups
Jonas W. B. Lang and Paul D. Bliese
Chapter 23: Social Network Effects: Computational Modeling of Network Contagion and Climate Emergence
Daniel A. Newman and Wei Wang
Part IV. Reflections on Multilevel Research
Chapter 24: Cross-Level Models
Francis J. Yammarino and Janaki Gooty
Chapter 25: Panel Interview: Reflections on Multilevel Theory, Measurement, and Analysis
Michael E. Hoffman, David Chan, Gilad Chen, Fred Dansereau, Denise Rousseau, and Benjamin Schneider
Index
About the Editors
