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Faculty members are influenced by a complex set of factors when making decisions about when to retire. These factors generally include both financial and non-financial characteristics. This study was designed to examine the non-financial factors related to the retirement process for selected faculty groups. Key components of the design included selecting faculty groups for analysis and identifying the non-financial factors related to the retirement process. Two faculty groups were selected: (a) faculty who had previously retired from another position and (b) faculty members with no plans to retire in the next three years. The non-financial factors were identified through a review of the literature and included (a) employment characteristics, (b) demographic characteristics, (c) activity measures, and (d) satisfaction items. The study was based on secondary analysis of NSOPF: 99 data. A combination of descriptive statistics and logistic regression was used to analyze the data. Major findings include (a) previously retired faculty members may be a substantial pool of qualified, productive talent intrinsically motivated to be part of an academic environment on a part-time basis because their financial status is not solely dependent on basic salary from the institution; (b) additional indicators distinguishing age at retirement from a career position versus age at retirement from all paid employment may also be needed to fully describe the issue; (c) employment status, years in current position, program area, age, gender, geographic region, average class size, and satisfaction with other aspects of the job (excluding instructional duties) were distinguishing characteristics of previously retired faculty members; (d) a sizeable portion of older faculty has not yet reached traditional retirement age; (e) the impact of uncapping mandatory retirement ages for tenured faculty may not have yet been fully realized — even eight years after the legislation took effect; (f) evidence does not support some of the objections from the higher education community in opposition to uncapping; and (g) control of institution, program area, years in current position, age, marital status, number of dependents, recent publications, career publications, and satisfaction were distinguishing characteristics of faculty members with no plans to retire in the next three years. / Ph. D.

For this study, I have constructed a logistic regression model, using existing datasets of environmental parameters to predict the probability of As concentrations above 5 parts per billion (ppb) in Virginia groundwater and to evaluate if geologic or other characteristics are linked to elevated As concentrations. Measured As concentrations in groundwater from the Virginia Tech Biological Systems Engineering (BSE) Household Water Quality dataset were used as the dependent variable to train (calibrate) the model. Geologic units, lithology, soil series and texture, land use, and physiographic province were used as regressors in the model. Initial models included all regressors, but during model refinement, attention was focused solely on geologic units. Two geologic units, Triassic-aged sedimentary rocks and Devonian-aged shales/sandstones, were identified as significant in the model; the presence of these units at a spatial location results in a higher probability for As occurrences in groundwater. Measured As concentrations in groundwater from an independent dataset collected by the Virginia Department of Health were used to test (validate) the model. Due to the structure of the As datasets, which included As concentrations mostly (95-99%) = 5 ppb, and thus few (1-5%) data in the range > 5 ppb, the regression model cannot be used reliably to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in groundwater. Results of this work suggest that homeowners with wells installed in these geologic units have their wells tested for As and regulators closely monitor public supply wells in these areas for As. / Master of Science

The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.

Incorporating survey weights into likelihood-based analysis is a controversial issue because the sampling weights are not simply equal to the reciprocal of selection probabilities but they are adjusted for various characteristics such as age, race, etc. Some adjustments are based on nonresponses as well. This adjustment is accomplished using a combination of probability calculations. When we build a logistic regression model to predict categorical outcomes with survey data, the sampling weights should be considered if the sampling design does not give each individual an equal chance of being selected in the sample. We rescale these weights to sum to an equivalent sample size because the variance is too small with the original weights. These new weights are called the adjusted weights. The old method is to apply quasi-likelihood maximization to make estimation with the adjusted weights. We develop a new method based on the correct likelihood for logistic regression to include the adjusted weights. In the new method, the adjusted weights are further used to adjust for both covariates and intercepts. We explore the differences and similarities between the quasi-likelihood and the correct likelihood methods. We use both binary logistic regression model and multinomial logistic regression model to estimate parameters and apply the methods to body mass index data from the Third National Health and Nutrition Examination Survey. The results show some similarities and differences between the old and new methods in parameter estimates, standard errors and statistical p-values.

The aim of this work is to decide the real usage of logistic regression in the market research tasks respecting the needs of final users of research results. The main argument for the final decision is the comparison of its output to the output of an alternative classification method used in practice -- a classification tree method. The topic is divided into three parts. The first part describes the theoretical framework and approaches linked to logistic regression (chapter 2 and 3). The second part analyses the experience with the usage of logistic regression in Czech market research companies (chapter 4) and the topic is closed by applying the method on real data and comparing the output to the classification tree output (chapter 5 and 6).

Master of Science / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / Gully erosion is a serious problem on military training lands resulting in not only soil erosion and environmental degradation, but also increased soldier injuries and equipment damage. Assessment of gully erosion occurring on Fort Riley was conducted in order to evaluate different gully location methods and to develop a gully prediction model based on logistic regression. Of the 360 sites visited, fifty two gullies were identified with the majority found using LiDAR based data. Logistic regression model was developed using topographic, landuse/landcover, and soil variables. Tests for multicollinearity were used to reduce the input variables such that each model input had a unique effect on the model output. The logistic regression determined that available water content was one of the most important factors affecting the formation of gullies. Additional important factors included particle size classification, runoff class, erosion class, and drainage class. Of the 1577 watersheds evaluated for the Fort Riley area, 192 watersheds were predicted to have gullies. Model accuracy was approximately 79% with an error of omission or false positive value of 10% and an error of commission or false negative value of 11%; which is a large improvement compared to previous methods used to locate gully erosion.

Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, and each experiment was replicated 100 times. For both methods and all DIF types, a test length of 20 was sufficient for satisfactory DIF detection. The detection rate increased significantly with sample size for each method. With the ANOVA DIF method and uniform DIF, there was a difference in detection rates between discrimination parameter types, which favored varying discrimination and decreased with increased sample size. The detection rate of non-uniform DIF using the ANOVA DIF method was higher with fixed discrimination parameters than with varying discrimination parameters when relative underlying ability was unequal. In the combination DIF case, there was a three-way interaction among the experimental factors discrimination type, relative ability, and sample size for both detection methods. The error rate for the ANOVA DIF detection method decreased as test length increased and increased as sample size increased. For both methods, the error rate was slightly higher with varying discrimination parameters than with fixed. For logistic regression, the error rate increased with sample size when relative underlying ability was unequal between groups. The logistic regression method detected uniform and non-uniform DIF at a higher rate than the ANOVA DIF method. Because the type of DIF present in real data is rarely known, the logistic regression method is recommended for most cases.

This study aims to investigate the determinants of capital structure in small and medium-sized enterprises (SMEs) in Malaysia and their effect on firms’ performance. The study addresses the following primary question: What are the factors that influence the capital structure of SMEs in Malaysia? The sample of this research is SMEs in the east coast region of Malaysia. Adopting a positivist paradigm, the research design includes a preliminary study comprising 25 interviews with the owner-managers of SMEs, which is analysed using thematic analysis. The results are used to finalise the conceptual framework for the main study, which takes the form of a self-completion questionnaire survey. Usable responses were received from 384 firms, giving a response rate of 75.3%. The survey data is analysed using a series of binomial logistic regression models. Results reveal that there was no indication for the impact of owner’s education and experience on capital structure decisions. Other owner-related factors, firm characteristics, management performance and environment were found to relate to all types of capital structure. Both complete and partial mediating effects are also discovered in this study. The results provide evidence to support the pecking order hypothesis (Myers, 1984; Myers and Majluf, 1984), agency theory (Jensen and Meckling, 1976) and culture model of Schwartz (1994). It appeared that owner-managers in Malaysia do not strive to adjust their capital structure towards some optimal debt ratio, which is contrary to the static trade-off theory (DeAngelo and Masulis, 1980) of capital structure. This study makes several important contributions to the existing studies of capital structure. This research led to the development of a model of capital structure determinants by integrating factors related to owner-managers, firms, culture, and environment. This study incorporates methodological triangulation that may mitigate the problem of the difficulties in accessing financial data of SMEs in Malaysia. This study also provides meaningful insight into the financing preferences of the owner-managers with relevant implementations to academics, business practitioners, financial providers and policymakers. The research findings should assist owner-managers in making optimal capital structure decisions as well as help the policymaker in making an appropriate policy on the financing.

Logistic regression is a widely used statistical method in data analysis and machine learning. When the capacity of data is large, it is time-consuming and even infeasible to perform big data machine learning using the traditional approach. Therefore, it is crucial to come up with an efficient way to evaluate feature combinations and update learning models. With the approach proposed by Yang, Wang, Xu, and Zhang (2018) a system can be represented using small enough matrices, which can be hosted in memory. These working sufficient statistics matrices can be applied in updating models in logistic regression. This study applies the working sufficient statistics approach in logistic regression machine learning to examine how this new method improves the performance. This study investigated the difference between the performance of this new working sufficient statistics approach and performance of the traditional approach on Spark\rq s machine learning package. The experiments showed that the working sufficient statistics method could improve the performance of training the logistic regression models when the input size was large.