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Studies of Monte Carlo Methodology for Assessing Convergence, Incorporating Decision Making, and Manipulating Continuous Variables

  • Author(s): Zelinsky, Nicole
  • Advisor(s): Vevea, Jack L.
  • et al.
Creative Commons 'BY-NC' version 4.0 license
Abstract

In this dissertation, I explore challenges that simulation researchers face. First, I argue that compared to using inferential models, assessing simulation convergence is the superior method to determine the number of dataset replications to use when conducting a simulation. I devise a novel way of assessing simulation convergence with rounded cumulative means and apply it to four examples alongside a more conventional, analytical technique. Second, I highlight the importance of incorporating statistical decisions into the simulation process. I illustrate that with examples of decisions surrounding model selection, convergence of an individual model’s estimates, and modifications made during preliminary statistical analyses (e.g., due to outliers or a perceived assumption failure). Third, I compare the use of continuous manipulated variables that have been discretized into levels to those generated along the full continuum of possible values. Based on linear and non-linear simulation examples, discretized manipulated variables appear more effective than continuous variables, mainly due to the taxing process of establishing simulation convergence along a continuum as opposed to a point estimate.

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