Doctor of Philosophy, University of Akron, 2015, Civil Engineering

In the oil industry, underground pipelines are the most preferred means of transporting a large amount of liquid product. However, a considerable number of unforeseen incidents due to corrosion failure are reported each year. Since corrosion in underground pipelines is caused by physicochemical interactions between the material (steel pipeline) and the environment (soil), the assessment of soil as a corrosive environment is indispensable. Because of the complex characteristics of soil as a corrosion precursor influencing the dissolution process, soil cannot be explained fully by conventional semi-empirical methodologies defined in controlled settings. The uncertainties inherited from the dynamic and heterogeneous underground environment should be considered. Therefore, this work presents the unification of direct assessment of soil and in-line inspection (ILI) with a probabilistic model to categorize soil corrosion. To pursue this task, we employed a model-based clustering analysis via Gaussian mixture models. The analysis was performed on data collected from southeastern Mexico. The clustering approach helps to prioritize areas to be inspected in terms of underground conditions and can improve repair decision making beyond what is offered by current assessment methodologies. This study also addresses two important issues related to in-situ data: missing data and truncated data. The typical approaches for treating missing data utilized in civil engineering are ad hoc methods. However, these conventional approaches may cause several critical problems such as biased estimates, artificially reduced variance, and loss of statistical power. Therefore, this study presents a variant of EM algorithms called Informative EM (IEM) to perform clustering analysis without filling in missing values prior to the analysis. This model-based method introduces additional cluster-specific Bernoulli parameters to exploit the nonuniformity of the frequency of missing values across cl (open full item for complete abstract)

Committee: Robert Liang Dr. (Advisor); Chien-Chun Chan Dr. (Committee Member); Junliang Tao Dr. (Committee Member); Guo-Xiang Wang Dr. (Committee Member); Lan Zhang Dr. (Committee Member) Subjects: Civil Engineering

Master of Arts, Case Western Reserve University, 0, Psychology

At some point in their lives, everyone will experience a stressful life event. Usually, someone can cope with and make meaning from the event. However, the body of research investigating the impact of severe and/or chronic exposure to stressful life events on the brain shows that harmful effects of stress exposure accumulate. Considering the extant literature regarding religious meaning making in light of these findings and the robust literature on spiritual transformation following stressful life events, I developed three hypotheses: 1) stressful life events increase risk of (non)religious ID change, 2) earlier events continued to impact later ID changes, and 3) risk of ID change was similar across change groups. This study analyzed a nationally representative longitudinal dataset of US children born between 1980 and 1984 (N = 8984). The final analyses used multiple imputation to account for missing data and did not find evidence supporting the hypotheses.

Committee: Julie Exline Ph.D. (Committee Chair); Heath Demaree Ph.D. (Committee Member); Arin Connell Ph.D. (Committee Member) Subjects: Health; Mental Health; Psychology; Religion; Spirituality

Master of Science (MS), Ohio University, 2016, Industrial and Systems Engineering (Engineering and Technology)

A significant issue within hospitals is the occurrence and frequency of missing doses. A missing dose is when a medication is not available to be administered to the patient at the required time. Decreasing missing doses can improve the patient's quality of care and decrease cost of care, labor costs, and medication expenses. The objective of this research was to identify the cause(s) of missing doses and provide recommendations on how to reduce the frequency of missing doses. This project analyzed a year of quantitative data to identify conditions that are correlated with missing doses. These conditions were identified so that recommendations could be provided to reduce re-dispenses and interruptions within the medication distribution process. The analysis examined re-dispenses in terms of five different characteristics: time, nursing unit, medication, medication form, and the dispensing pharmacy.

Committee: Dale Masel (Committee Chair); Diana Schwerha (Committee Member); Tao Yuan (Committee Member); Douglas Bolon (Committee Member) Subjects: Engineering; Health Care; Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2010, EDU Policy and Leadership

The purpose of this study is to investigate the effects of missing data techniques in SEM under different multivariate distributional conditions. Using Monte Carlo simulations, this research examines the performance of four missing data methods in SEM: full information maximum likelihood (FIML), expectation maximum (EM) procedure, multiple imputation (MI), and similar response pattern imputation (SRPI) in the missing data mechanisms of missing completely at random (MCAR) and missing at random (MAR). The effects of three independent variables (sample size, missing proportion, and distribution shape) are investigated on parameter and standard error estimation, standard error coverage and model fit statistics. An inter-correlated 3-factor CFA model is used. The findings of this study indicate that FIML is the most robust method in terms of parameter estimate bias; FIML and MI generate almost equally accurate standard error coverage; and MI is the best in terms of estimation efficiency/accuracy and model rejection rate. The results of SRPI in this study are consistent with previous studies in the literature. Generally speaking, SRPI produces unbiased parameter estimates for factor loadings and factor correlations under MCAR. However, when there are severe missingness or nonnormality conditions in the data or when the sample size is very small, it has bias problems on error variance estimates for indicators with moderate to low factor loadings. Some of the merits regarding SRPI found in this study are that it is more efficient than FIML under MCAR when data not only have small to moderate missingness, but also are severely nonnormal; it was also found to be more efficient for factor loading estimates of those indicators with missing data in MAR when the missing percentage is high and the nonnormality condition is most severe. Recommendations regarding when to use each of the missing data techniques are provided at the end of the study. Future works are also discussed f (open full item for complete abstract)

Committee: Richard G. Lomax PhD (Advisor); Ann A. O’Connell PhD (Committee Member); Pamela M. Paxton PhD (Committee Member) Subjects: Educational Psychology; Statistics

Doctor of Philosophy, The Ohio State University, 2010, Statistics

Recently medical and public health research has focused on the development of models for longitudinal studies that aim to identify individuals at risk for obesity by tracking childhood weight gain. The National Longitudinal Surveys of Youth 79 (NLSY79), which includes a random sample of women with biometric information on their biological children collected biennially, has been considered. A mixed model with three levels of clustered random effects has been proposed for the estimation of child-specific weight trajectories while accounting the nested structure of the dataset. Included in this model is a regression equation approach to address any remaining heterogeneity in the within-child variances. Specifically, a model has been implemented to fit the log of the within-child variances as a function of time. This allows for more flexibility in modeling residual variances that appear to be increasing over time. Using the EM algorithm with a Newton-Raphson update all the parameters of the model are estimated simultaneously. A second aspect to the research that is presented is the analysis of missing data. Extensive exploratory analysis revealed that intermittent missingness was prevalent in the relevant subset of the NLSY79 dataset. Starting with the assumptions of MCAR and MAR selection models are built up to appropriately account for the missing mechanism at play. A factorization of the multinomial distribution as a product of dependent binary observations is applied to model intermittent missingness. Logit models for dependent binary observations are used to fit selection models for missingness under the assumptions of MAR and MCAR. The NMAR case for clustered longitudinal data is discussed as an area for future research.

Committee: Elizabeth Stasny PhD (Advisor); Christopher Hans PhD (Advisor); Eloise Kaizar PhD (Committee Member); John Casterline PhD (Committee Member) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2005, Psychology

Missing data are problematic for many statistical analyses, factor analysis included. Because factor analysis is widely used by applied social scientists, it is of interest to develop accurate, general-purpose methods for the handling of missing data in factor analysis. While a number of such missing data methods have been proposed, each individual method has its weaknesses. For example, difficulty in obtaining test statistics of overall model fit and reliance on asymptotic results for standard errors of parameter estimates are two weaknesses of previously-proposed methods. As an alternative to other general-purpose missing data methods, I develop Bayesian missing data methods specific to factor analysis. Novel to the social sciences, these Bayesian methods resolve many of the other missing data methods' weaknesses and yield accurate results in a variety of contexts. This dissertation details Bayesian factor analysis, the proposed Bayesian missing data methods, and the computation required for these methods. Data examples are also provided.

Committee: Trisha Van Zandt (Advisor) Subjects: