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Detecting consensus emergence in organizational multilevel data : power simulations

Jonas Lang (UGent) , Paul D. Bliese and Jan 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
consensus emergence, power analysis, multilevel models, justice climate, models, regression, integration, constructs, dynamics, work

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, 2019.
APA
Lang, J., Bliese, P. D., & Runge, J. M. (2019). Detecting consensus emergence in organizational multilevel data : power simulations. ORGANIZATIONAL RESEARCH METHODS.
Chicago author-date
Lang, Jonas, Paul D. Bliese, and Jan Malte Runge. 2019. “Detecting Consensus Emergence in Organizational Multilevel Data : Power Simulations.” ORGANIZATIONAL RESEARCH METHODS.
Chicago author-date (all authors)
Lang, Jonas, Paul D. Bliese, and Jan Malte Runge. 2019. “Detecting Consensus Emergence in Organizational Multilevel Data : Power Simulations.” ORGANIZATIONAL RESEARCH METHODS.
Vancouver
1.
Lang J, Bliese PD, Runge JM. Detecting consensus emergence in organizational multilevel data : power simulations. ORGANIZATIONAL RESEARCH METHODS. 2019;
IEEE
[1]
J. Lang, P. D. Bliese, and J. M. Runge, “Detecting consensus emergence in organizational multilevel data : power simulations,” ORGANIZATIONAL RESEARCH METHODS, 2019.
@article{8632708,
  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    = {1094428119873950},
  author       = {Lang, Jonas and Bliese, Paul D. and Runge, Jan Malte},
  issn         = {1094-4281},
  journal      = {ORGANIZATIONAL RESEARCH METHODS},
  keywords     = {consensus emergence,power analysis,multilevel models,justice climate,models,regression,integration,constructs,dynamics,work},
  language     = {eng},
  pages        = {23},
  title        = {Detecting consensus emergence in organizational multilevel data : power simulations},
  url          = {http://dx.doi.org/10.1177/1094428119873950},
  year         = {2019},
}

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