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Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations

Jonas Lang (UGent) , Paul D. Bliese and Malte Runge (UGent)
Author
Organization
Abstract
Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach-the consensus emergence model-builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research.
Keywords
JUSTICE CLIMATE, MODELS, REGRESSION, INTEGRATION, CONSTRUCTS, DYNAMICS, WORK, consensus emergence, power analysis, multilevel models

Citation

Please use this url to cite or link to this publication:

MLA
Lang, Jonas, et al. “Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations.” ORGANIZATIONAL RESEARCH METHODS, Sage Publications Inc, 2020, doi:10.1177/1094428119873950.
APA
Lang, J., Bliese, P. D., & Runge, M. (2020). Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations. ORGANIZATIONAL RESEARCH METHODS. https://doi.org/10.1177/1094428119873950
Chicago author-date
Lang, Jonas, Paul D. Bliese, and Malte Runge. 2020. “Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations.” ORGANIZATIONAL RESEARCH METHODS. https://doi.org/10.1177/1094428119873950.
Chicago author-date (all authors)
Lang, Jonas, Paul D. Bliese, and Malte Runge. 2020. “Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations.” ORGANIZATIONAL RESEARCH METHODS. doi:10.1177/1094428119873950.
Vancouver
1.
Lang J, Bliese PD, Runge M. Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations. ORGANIZATIONAL RESEARCH METHODS. 2020;
IEEE
[1]
J. Lang, P. D. Bliese, and M. Runge, “Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations,” ORGANIZATIONAL RESEARCH METHODS, 2020.
@article{8667908,
  abstract     = {Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach-the consensus emergence model-builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research.},
  articleno    = {UNSP 1094428119873950},
  author       = {Lang, Jonas and Bliese, Paul D. and Runge, Malte},
  issn         = {1094-4281},
  journal      = {ORGANIZATIONAL RESEARCH METHODS},
  keywords     = {JUSTICE CLIMATE,MODELS,REGRESSION,INTEGRATION,CONSTRUCTS,DYNAMICS,WORK,consensus emergence,power analysis,multilevel models},
  language     = {eng},
  pages        = {23},
  publisher    = {Sage Publications Inc},
  title        = {Detecting Consensus Emergence in Organizational Multilevel Data: Power Simulations},
  url          = {http://dx.doi.org/10.1177/1094428119873950},
  year         = {2020},
}

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