Everything on the Table: Tabular, Graphic, and Interactive Approaches for Interpreting and Presenting Monte Carlo Simulation Data

Abstract Monte Carlo simulation studies (MCSS) form a cornerstone for quantitative methods research. They are frequently used to evaluate and compare the properties of statistical methods and inform both future research and current best practices. However, the presentation of results from MCSS often leaves much to be desired, with findings typically conveyed via a series of elaborate tables from which readers are expected to derive meaning. The goal of this dissertation is to explore, summarize, and describe a framework for the presentation of MCSS, and show how modern computing and visualization techniques improve their interpretability. Chapter One describes this problem by introducing the logic of MCSS, how they are conducted, what findings typically look like, and current practices for their presentation. Chapter Two demonstrates methods for improving the display of static tabular data, specifically via formatting, effects ordering, and rotation. Chapter Three delves into semi-graphic and graphical approaches for aiding the presentation of tabular data via shaded tables, and extensions to the tableplot and the hypothesis-error plot frameworks. Chapter Four describes the use of interactive computing applets to aid the exploration of complex tabular data, and why this is an ideal approach. Throughout this work, emphasis is placed on how such techniques improve our understanding of a particular dataset or model. Claims are supported with applied demonstrations. Implementation of the ideas from each chapter have been coded within the R language for statistical computing and are available for adoption by other researchers in a dedicated package (SimDisplay). It is hoped that these ideas might enhance our understanding of how to best present MCSS findings and be drawn upon in both applied and academic environments.