Manufacturing a personage: photography and American literary celebrity, 1839-1860

Title from PDF of title page (University of Missouri--Columbia, viewed on September 5, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. John EvelevIncludes bibliographical references.M. A. University of Missouri--Columbia 2013.Dissertations, Academic -- University of Missouri--Columbia -- English."May 2013"The purpose of this thesis is to investigate the ways in which the daguerreotype influenced literary celebrity in the United States from the time of its invention in 1839 to the beginning of the Civil War in 1860. The daguerreotype was introduced to America in the midst of the printing revolution that took place in the second quarter of the 19th century. Advancements in printing technology, transportation, and public education created a mass readership that made literary celebrity possible, and the accurate visual representations of authors that the daguerreotype created played a crucial role in the public imagination of these celebrity authors. After a brief overview of the historical background of the era and the project's methodological approach, this study considers the effect of the daguerreotype on the writing and reputations of Nathaniel Hawthorne, Edgar Allan Poe, and Herman Melville. The first chapter provides a reading of The House of the Seven Gables, arguing that daguerreotypy figures in the novel as a function of Hawthorne's concern with the public/private divide. The second chapter reads Poe's "The Literati of New York City" in relation to his photographic portraiture to explore how the construction of his public reputation was predicated on his visual image. Finally, the third chapter intervenes in the critical dialogue surrounding Melville's Pierre, or the Ambiguities to investigate the way in which Pierre's literary celebrity comments upon the compositional history of the novel

CEM: Coarsened Exact Matching in Stata

We introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In coarsened exact matching, users temporarily coarsen their data, exact match on these coarsened data, and then run their analysis on the uncoarsened, matched data. Coarsened exact matching bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is monotonic imbalance bounding (so that reducing the maximum imbalance on one variable has no effect on others), does not require a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, balances all nonlinearities and interactions in sample (i.e., not merely in expectation), and works with multiply imputed datasets. Other matching methods inherit many of the coarsened exact matching method’s properties when applied to further match data preprocessed by coarsened exact matching.

Hypoglossal schwannoma masquerading as a carotid body tumor.

Study Objective. To describe the clinical presentation, evaluation, and treatment of a hypoglossal schwannoma. Methods. We report an unusual case of a hypoglossal schwannoma presenting as a pulsatile level II neck mass at the bifurcation of the external and internal carotid arteries, mimicking a carotid body tumor. Radiologic findings are reviewed in detail. Results. A 59-year-old female presented to a tertiary care medical center with complaints of a pulsatile right-sided neck mass. An MRA of the neck was obtained demonstrating a 5 cm mass located at the carotid artery bifurcation and causing splaying of the internal and external carotids. Based on clinical presentation and imaging, a diagnosis of a carotid body tumor was conferred and the patient scheduled for excision. Intraoperatively, the mass was noted to arise from the hypoglossal nerve, remaining independent of the carotid artery. On histopathologic analysis, the mass was determined to be consistent with hypoglossal schwannoma. Conclusion. Though rare, the hypoglossal schwannoma should remain a consideration in the evaluation of a parapharyngeal space mass. As this report demonstrates, the clinical and radiologic presentation of a hypoglossal schwannoma may closely mimic that of the more common carotid body tumor

Amelia II: A Program for Missing Data

Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization with bootstrapping algorithm that works faster, with larger numbers of variables, and is far easier to use, than various Markov chain Monte Carlo approaches, but gives essentially the same answers. The program also improves imputation models by allowing researchers to put Bayesian priors on individual cell values, thereby including a great deal of potentially valuable and extensive information. It also includes features to accurately impute cross-sectional datasets, individual time series, or sets of time series for different cross-sections. A full set of graphical diagnostics are also available. The program is easy to use, and the simplicity of the algorithm makes it far more robust; both a simple command line and extensive graphical user interface are included

Essays in Political Methodology

This dissertation provides three novel methodologies to the field of political science. In the first chapter, I describe how to make causal inferences in the face of dynamic strategies. Traditional causal inference methods assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and post-treatment bias. I resolve this dilemma by adapting methods from biostatistics and use these methods to estimate the effectiveness of an inherently dynamic process: a candidate's decision to "go negative." Drawing on U.S. statewide elections (2000-2006), I find, in contrast to the previous literature, that negative advertising is an effective strategy for non-incumbents. In the second chapter, I develop a method for handling measurement error. Social scientists devote considerable effort to mitigating measurement error during data collection but then ignore the issue during analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. This chapter develops an easy-to-use alternative without these problems as a special case of extreme measurement error and corrects for both. In the final chapter, I introduce a model for detecting changepoints in the distribution of contributions data because it allows for overdispersion, a key feature of contributions data. While many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with their location. I demonstrate the usefulness of the model in data from the 2012 Republican primary and the 2008 U.S. Senate elections.Governmen

A Unified Approach to Measurement Error and Missing Data: Overview, Sociological Methods and Research

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions (Blackwell, Honaker, and King, 2014b).Governmen