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powersim: simulation-based power analysis for linear and generalized linear models


  • Joerg Luedicke

    () (Yale University
    University of Florida)


A widespread tool in the context of a point null hypothesis significance testing framework is the computation of statistical power, especially in the planning stage of quantitative studies. However, asymptotic power formulas are often not readily available for certain tests, or are too restrictive in their underlying assumptions to be of much use in practice. The Stata package -powersim- exploits the flexibility of a simulation-based approach by providing a facility for automated power simulations in the context of linear and generalized linear regression models. The package supports a wide variety of uni- and multivariate covariate distributions, and all family and link choices that are implemented in Stata's -glm- command. The package mainly serves two purposes: first, it provides access to simulation-based power analyses for researchers without much experience in simulation studies. Second, it provides a convenient simulation facility for more advanced users who can easily complement the automated data generation with their own code for creating more complex synthetic datasets. The presentation will discuss some advantages of the simulation-based power analysis approach and will go through a number of worked examples to demonstrate key features of the package.

Suggested Citation

  • Joerg Luedicke, 2013. "powersim: simulation-based power analysis for linear and generalized linear models," 2013 Stata Conference 13, Stata Users Group.
  • Handle: RePEc:boc:norl13:13

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