1,175 research outputs found

    Preface and Dedication

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    Sample Size in Ordinal Logistic Hierarchical Linear Modeling

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    Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study. Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM. The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy. Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category

    Railroad Law

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    Fool Me Once, Shame on Me; Fool Me Again and You\u27re Gonna Pay For It: An Analysis of Medicare\u27s New Reporting Requirements for Primary Payers and the Stiff Penalties Associated with Noncompliance

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    This article discusses the new requirements and the issues that currently face insurers, claimants, and attorneys in cases involving Medicare-eligible beneficiaries

    Product Liability Law

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    While Virginia is not typically seen as progressive in the field of product liability law, the Commonwealth is nonetheless a forum in which these product liability battles take place. This article summarizes selected decisions of the United States Court of Appeals for the Fourth Circuit, federal district courts in Virginia, and courts of the Commonwealth issued between July 1, 2004 and May 15, 2005. This article also includes a discussion of the most relevant legislative changes made by the Virginia General Assembly over the same time period. While a complete analysis of every decision and statute affecting product liability is not possible, this article summarizes those which should be the most useful to practitioners in Virginia

    Comparing periodic-orbit theory to perturbation theory in the asymmetric infinite square well

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    An infinite square well with a discontinuous step is one of the simplest systems to exhibit non-Newtonian ray-splitting periodic orbits in the semiclassical limit. This system is analyzed using both time-independent perturbation theory (PT) and periodic-orbit theory and the approximate formulas for the energy eigenvalues derived from these two approaches are compared. The periodic orbits of the system can be divided into classes according to how many times they reflect from the potential step. Different classes of orbits contribute to different orders of PT. The dominant term in the second-order PT correction is due to non-Newtonian orbits that reflect from the step exactly once. In the limit in which PT converges the periodic-orbit theory results agree with those of PT, but outside of this limit the periodic-orbit theory gives much more accurate results for energies above the potential step.Comment: 22 pages, 2 figures, 2 tables, submitted to Physical Review
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