Organization and Management Theory OMT

  • 1.  Multilevel FT in ORM

    Posted 13 days ago

    The 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

    Heterogeneous Variances

    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,

    and Recommendations

    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.



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    Rory Eckardt, PhD
    Associate Professor of Strategy
    School of Management
    Binghamton University
    tel. (607) 777-3437
    reckardt@binghamton.edu
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