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This dissertation examines the relationship between sporting male culture and popular culture during the antebellum period. Sporting culture emerged in major northeastern cities in the 1830s and was mostly comprised of young, single white men. Often disconnected from families or apprentice systems, these men reveled in drinking, brothel-going, gambling and other urban exploits, and had an indelible impact on antebellum literature, print culture, reform work, and law. With their seemingly amoral attitude, individualist rhetoric, and excessive indulgence, sporting men were a source of fascination to the general public and incited marked consternation and concern from authors, reformers and politicians. By examining sporting culture I expand our understanding of the cultural responses to the intense social, political, and economic changes of the antebellum period. In this dissertation I examine popular sensational novels, newspapers, trial reports, reform work, seduction law, and the development of George Lippard's secret society, the Brotherhood of the Union, to show how the concern over sporting culture reflected fears and anxiety that modernization might not simply breed inequality—but could potentially corrupt the moral and patriotic sensibility of young white men across the nation. This dissertation considers why sporting culture, prostitution, the libertine, and seduction became sources of preoccupation in the northeastern city. I argue that all of these phenomena were connected: the fascination with illicit activity and the strength of the backlash against it came from the fear that capitalism and modernization might turn a class of potentially productive young white men into self-interested libertines, fracturing the potential for white male unity and cohesion.

Abstract
Many agencies are currently investigating whether releasing synthetic microdata could be a viable dissemination strategy for highly sensitive data, such as business data, for which disclosure avoidance regulations would otherwise prohibit the release of public use microdata. The U.S. Census Bureau has identified the Economic Census as a candidate program and has been developing synthetic data generators. The synthetic data should account for skewed and irregular distributions, satisfy predetermined edit constraints, and preserve selected privacy features. Previous research on these generators was confined to businesses that were in operation for the full year, ignoring the special features of births and deaths in the models. These generators preserve multivariate relationships and yield marginal totals that closely correspond to the published official statistics. However, these synthetic data consequently do not reflect the state of economic expansion or contraction. This missing information is a severe deficiency for the targeted data users comprising economists, policymakers, and methodologists, especially since the global pandemic of 2020. This paper introduces an approach that addresses this deficiency, producing partially synthetic data with high utility and privacy protection. We provide preliminary results using selected industry data from the 2012 Economic Census.

Abstract
The U.S. Census Bureau has historically used nearest neighbor (NN) or random hot deck (RHD) imputation to handle missing data for many types of survey data. Using these methods removes the need to parametrically model values in imputation models. With strong auxiliary information, NN imputation is preferred because it produces more precise estimates than RHD. In addition, NN imputation is robust against a misspecified response mechanism if missingness depends on the auxiliary variable, in contrast to RHD which ignores the auxiliary information. A compromise between these two methods is k-NN imputation, which identifies a set of the k closest neighbors ("donor pool") and randomly selects a single donor from this set. Recently these methods have been used for multiple imputation (MI), enabling variance estimation via the so-called Rubin's Combining Rules. The Approximate Bayesian Bootstrap (ABB) is a simple-to-implement algorithm that makes the RHD "proper" for MI. In concept, ABB should work to propagate uncertainty for NN MI; bootstrapping respondents mean each nonrespondent's one "nearest" donor will not be available for every imputation. However, we demonstrate through simulation that NN MI using ABB leads to variance underestimation. This underestimation is somewhat but not entirely attenuated with k-NN imputation. An alternative approach to variance estimation after MI, bootstrapped MI, eliminates the underestimation with NN imputation, but we show that it suffers from overestimation of variance with nonnegligible sampling fractions under both equal and unequal probability sampling designs. We propose a modification to bootstrapped MI to account for nonnegligible sampling fractions. We compare the performance of RHD and the various NN MI methods under a variety of sampling designs, sampling fractions, distribution shapes, and missingness mechanisms.

Probability sample selection procedures gift methodologists with quite a bit of control before data collection. Unfortunately, not all sample units respond and those that do will not always provide data on every questioned characteristic, which can lead to biased estimates of totals. In this paper, we focus entirely on the challenges of mitigating nonresponse bias effects in business surveys, using empirical examples from one survey to illustrate challenges common to many programs.

Abstract
Detailed breakdowns on totals are often collected in surveys, such as a breakdown of total product sales by product type. These multinomial data are often sparsely reported with wide variability in proportions across units. In addition, there are often true zeros that differ across units even within industry; for example, one establishment sells jeans but not shoes, and another sells shoes but not socks. It is quite common to have large fractions of missing data for these detailed items, even when totals are relatively completely observed. Hot-deck imputation, which fills in missing data with observed data values, is an attractive approach. The entire set of proportions can be simultaneously imputed to preserve multinomial distributions, and zero values can be imputed. However, it is not clear what variant of the hot deck is best. We describe a large set of "flavors" of the hot deck and compare them through simulation and by application to data from the 2012 Economic Census. We consider different ways to create the donor pool: choosing one nearest neighbor (NN), choosing from five NNs, or using all units as the donor pool. We also consider different ways to impute from the donor: directly impute the donor's vector of proportions or randomly draw from a multinomial distribution using this vector of proportions. We consider scenarios where a strong predictor of these multinomial distributions exists as well as when covariate information is weak.

BACKGROUND: Patellar instability is a common and understudied condition that disproportionally affects athletes and military personnel. The rate of post-traumatic osteoarthritis that develops following a patellar dislocation can be up to 50% of individuals 5–15 years after injury. Conservative treatment is the standard of care for patellar instability however, there are no evidence-informed rehabilitation guidelines in the scientific literature. The purpose of this study is to assess the effectiveness of blood-flow restriction training (BFRT) for patellar instability. Our hypotheses are that this strategy will improve patient-reported outcomes and accelerate restoration of symmetric strength and knee biomechanics necessary to safely return to activity. METHODS/DESIGN: This is a parallel-group, superiority, randomized, double-blinded, placebo-controlled clinical trial at the University of Kentucky, sports medicine clinic that aims to recruit 78 patients with acute patellar dislocations randomly allocated into two groups: (1) sham BFRT and (2) BFRT. Both groups will receive the current standard of care physical therapy 3 times per week for up to 9 weeks. Physical therapy sessions will consist of typical standard of care treatment followed by BFRT or sham BFRT. Primary outcomes include the Norwich Patellar Instability Scale, quadriceps strength, and imaging and biochemical biomarkers of cartilage degradation. DISCUSSION: The current standard of care for non-operative treatment of patellar instability is highly variable does not adequately address the mechanisms necessary to restore lower extremity function and protect the long-term health of articular cartilage following injury. This proposed novel intervention strategy uses an easily implementable therapy to evaluate if BFRT significantly improves patient-reported outcomes, function, and joint health over the first year of recovery. TRIAL REGISTRATION: Blood Flow Restriction Training, Aspiration, and Intraarticular Normal Saline (BRAINS) NCT04554212. Registered on ...

Policymakers, researchers, and state vocational rehabilitation administrators share an interest in the long-term outcomes of individuals participating in the public state vocational rehabilitation program. Yet, there is limited research in the area of job retention or the service delivery practices used to support individuals with disabilities to achieve long-term success in competitive integrated employment (CIE). This article begins to address this research gap by conducting a retrospective review of 139 records of individuals with autism spectrum disorder (ASD) that were referred to an employment support organization for CIE between October 1, 2009 and December 31, 2017. In comparison to the high national unemployment rate experienced by individuals with ASD, 104 individuals that were referred for CIE secured employment in 126 different jobs. Findings indicate that most study participants were able to move from moderate and intensive levels of support to minimal levels of support by 18 months of employment. It appears that employees with ASD benefit from the continual assessment and subsequent support that occurs during the extended service component of ongoing support services for supported and customized employment. Services that were offered during this phase included ongoing customization of the initial job, lateral job moves, and career advancement.