OBers…New Approaches to Multilevel Methods and Statistics Feature Topic (FT) at Organizational Research Methods was recently published and is available at the following weblink: https://journals.sagepub.com/toc/orma/current
The articles in the FT make important contributions to the advancement of multilevel methods related to dynamic multilevel analyses, modeling multilevel variability, multilevel extension of statistical techniques, and endogeneity. A list of the articles with short summaries is available below. SAGE has made the FT articles open access until April 17, so please consider gaining access to the articles soon and forwarding the above weblink for the FT to any colleagues that may be interested.
FT Article List with Short Summaries
Multilevel Methods and Statistics: The Next Frontier
Rory Eckardt, Francis J. Yammarino, Shelley D. Dionne, and Seth M. Spain
Provides an overview of the history and current state of multilevel methods and statistics in the organizational sciences, discusses unresolved issues and future research topics, and outlines an agenda for future multilevel work.
Intensive Longitudinal Data Analyses With Dynamic Structural Equation Modeling
Le Zhou, Mo Wang, and Zhen Zhang
Discusses considerations and challenges of analyzing dynamic relationships with intensive longitudinal data (ILD) and advocates a new technique, dynamic structural modeling, that integrates two common longitudinal data analysis techniques (multilevel models for panel data and single-subject time series models) to uniquely address several of the key issues of working with multilevel ILD.
An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational
Models to Connect Theory, Model, and Data
Timothy Ballard, Hector Palada, Mark Griffin, and Andrew Neal
Develops a four-step approach to leverage and integrate computational and Bayesian perspectives to build and test multilevel theory in the same modeling framework.
The Co-evolution of Organizational and Network Structure: The Role of Multilevel
Mixing and Closure Mechanisms
Viviana Amati, Alessandro Lomi, Daniele Mascia, and Francesca Pallotti
Describes multilevel stochastic actor-oriented models and their application to examine between-level changes in network characteristics.
Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations
Jonas W. B. Lang, Paul D. Bliese, and J. Malte Runge
Examines statistical power considerations with consensus emergence models using a simulation and develops a tool in R for researchers to estimate statistical power with these models.
From Nuisance to Novel Research Questions: Using Multilevel Models to Predict
Houston F. Lester, Kristin L. Cullen-Lester, and Ryan W. Walters
Describes a technique, mixed-effects location- scale models, that involves the simultaneous modeling of the means (location) and variability (scale) of RCM, differentiates this from other approaches that model variability, and demonstrates its applicability to organizational research questions.
Meta-Analyses as a Multi-Level Model
Janaki Gooty, George C. Banks, Andrew C. Loignon, Scott Tonidandel, and Courtney E. Williams
Describes an extension to multilevel meta-analysis techniques to account for a third level of analysis pertaining to between-study dependencies, demonstrates the importance of accounting for this higher level, and develops a tool in R for researchers to use this extension.
Multiple-Membership Survival Analysis and Its Applications in Organizational Behavior
and Management Research
Hans Tierens, Nicky Dries, Mike Smet, and Luc Sels
Describes an extension to multilevel survival models to accommodate situations of multiple simultaneous nestings. Provides a step-by-step tutorial on the extension technique and demonstrates its applicability with an employee turnover dataset.
On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique,
John Antonakis, Nicolas Bastardoz, and Mikko Ronkko
Examines the potential for omitted variables to generate endogeneity issues with two-level random intercept multilevel models, assesses the potential presence of this endogeneity issue in multilevel research studies published in organizational science journals, and outlines an analytic approach to address the identified endogeneity issue.
Francis J. Yammarino, PhDSUNY Distinguished Professor of ManagementDirector, Bernard M. & Ruth R. Bass Center for Leadership StudiesSchool of Management | State University of New York at BinghamtonBinghamton, NY 13902-6000 | Phone: firstname.lastname@example.org://www.binghamton.edu/som/research/profile.html?id=fjyammo