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Call for Papers: ORM FT on Multilevel Methods and Statistics

  • 1.  Call for Papers: ORM FT on Multilevel Methods and Statistics

    Posted 07-03-2017 15:06
    Organizational Research Methods Feature Topic (special issue): New Approaches to Multilevel Methods and Statistics

    Call for Papers

    Multilevel perspectives have the potential to significantly improve our understanding of organizational phenomena and are thus frequently advocated in a large number of micro and macro research areas (Hitt, Beamish, Jackson & Mathieu, 2007; Mathieu & Chen, 2011).  While the general availability and knowledge of multilevel methods and statistics have vastly increased over the past several decades, it is important that we continue to develop new methodological approaches as many important multilevel research questions cannot be adequately addressed with current methods and statistics.  This applies to aspects of multilevel research that have received considerable attention in the past, such as top-down multilevel effects (e.g., LoPilato & Vandenberg, 2015), as well as areas that have received less attention, such as emergence and bottom-up effects (Hitt et al., 2007; Kozlowski, Chao, Grand, Braun & Kuljanin, 2013; Moliterno & Ployhart, 2016), multilevel networks (Moliterno & Mahony, 2011; Wang, Robins, Pattison & Lazega, 2013), and alternative cross-level models (Yammarino & Gooty, in press).  

    The purpose of this Feature Topic (FT) is to encourage the development and application of novel methods and statistics that significantly advance our ability to empirically investigate multilevel research questions in the organizational sciences.  We are open to any approach that makes a significant methodological or statistical contribution to multilevel research. Some potential topics in three broad aspects of multilevel research are provided below. 

    Bottom-up Effects and Processes

     - Approaches to overcoming the inherent challenges associated with testing the impact of a lower-level independent variable on a higher-level dependent variable (whether it be main, interactive or mediation effects) that stem from the observation mismatching that typically occurs with bottom-up statistical analyses (cf. Felin, Foss & Ployhart, 2015). For example: a) measurement approaches that disaggregate a higher-level dependent variable into a lower-level independent variable; b) quasi-experimental designs that involve exogenous mobility events; c) approaches that leverage field/lab data collection and computational modeling/agent-based simulations; d) novel uses of existing multilevel techniques (e.g., residual files of Random Coefficient Modeling (RCM)/Hierarchical Linear Modeling (HLM) or Within-and-Between Analysis (WABA) to track constructs progression from lower levels to higher levels)

     - Approaches to capture emergence and examine mechanisms. For example: a) generalized measurement approaches to capture compilation and fuzzy composition forms of emergence; b) wearable technology and experience sampling to collect fine-grained data associated with social dynamics involved with emergence; c) analytical approaches (e.g., renormalization group, temporal network approaches, topic modeling) to analyze the large and multifaceted data collected from wearable technology and experience sampling approaches

    Top-down Effects and Processes

     - New modeling approaches to cross-level direct effects that overcome the inherent challenges associated with analyzing these effects with RCM/HLM
     - Alternative approaches to address modeling challenges associated with simultaneous multi-unit hierarchical nesting (e.g., multi-group membership)
     - Novel econometric techniques to analyze cross-level effects
     - Approaches to handle endogeneity issues in traditional multilevel techniques

    Multilevel Networks

     - Network approaches to detect communities and their hierarchical structures in organizations
     - Network approaches to identify individual and group influencers in organizations  
     - Within- and cross-level dynamics in multilayer organizational networks 
     - Multilevel extensions of exponential random graph models

    Completed full manuscript submissions for the FT are due by July 1, 2018. Authors may begin submitting manuscripts for the FT on June 1, 2018 (and not before that date). When submitting completed manuscripts, be sure to select from the dropdown menu the FT on Multilevel Methods and Statistics.

    The Guest Editors for this Feature Topic are: Rory Eckardt, Binghamton University (reckardt@binghamton.edu); Seth Spain, Binghamton University (sspain@binghamton.edu); Shelley Dionne, Binghamton U. (sdionne@binghamton.edu); Thomas Moliterno, University of Massachusetts Amherst (moliterno@isenberg.umass.edu); and Francis Yammarino, Binghamton University (fjyammo@binghamton.edu).   Questions about possible submissions are welcomed.

    The full Call for Papers is available in the July 2017 issue of ORM.

    --
    Rory Eckardt, PhD
    Assistant Professor of Strategy
    School of Management
    Binghamton University
    tel. (607) 777-3437