The purpose of this study was to investigate the relationship between cigarette smoking and socio-demographic variables based on the empirical literature and the primitive theories in the field. Two regression approaches, logistic regression and linear multiple regression, were conducted on the two most recent Arizona Adult Tobacco Surveys to test the hypothesized models. The results showed that cigarette smokers in Arizona are mainly residents who have not completed a four-year college degree, who are unemployed, White, non-Hispanic, or young to middle-aged adults. Among the socio-demographic predictors of interest, education is the most important variable in identifying cigarette smokers, even though the predictive power of these socio-demographic variables is small. Practical and methodological implications of these findings are discussed.

Diversity in the population along with the diversity of testing usage has resulted in smaller identified groups of test takers. In addition, computer adaptive testing sometimes results in a relatively small number of items being used for a particular assessment. The need and use for statistical techniques that are able to effectively detect differential item functioning (DIF) when the population is small and or the assessment is short is necessary. Identification of empirically biased items is a crucial step in creating equitable and construct-valid assessments. Parshall and Miller (1995) compared the conventional asymptotic Mantel-Haenszel (MH) with the exact test (ET) for the detection of DIF with small sample sizes. Several studies have since compared the performance of MH to logistic regression (LR) under a variety of conditions. Both Swaminathan and Rogers (1990), and Hildalgo and López-Pina (2004) demonstrated that MH and LR were comparable in their detection of items with DIF. This study followed by comparing the performance of the MH, the ET, and LR performance when both the sample size is small and test length is short. The purpose of this Monte Carlo simulation study was to expand on the research done by Parshall and Miller (1995) by examining power and power with effect size measures for each of the three DIF detection procedures. The following variables were manipulated in this study: focal group sample size, percent of items with DIF, and magnitude of DIF. For each condition, a small reference group size of 200 was utilized as well as a short, 10-item test. The results demonstrated that in general, LR was slightly more powerful in detecting items with DIF. In most conditions, however, power was well below the acceptable rate of 80%. As the size of the focal group and the magnitude of DIF increased, the three procedures were more likely to reach acceptable power. Also, all three procedures demonstrated the highest power for the most discriminating item. Collectively, the results from this research provide information in the area of small sample size and DIF detection.

This study is an analysis of adolescent risk behaviors contributing to an increased rate of suicidal ideation for 12 to 18 year olds. The Youth Risk Behavior Surveillance System Survey (YRBSS) is an epidemiologic survey designed to monitor the prevalence of risky behaviors of adolescents in middle and high school1. The YRBSS is a complex sample survey with a three-stage cluster design. Multiple logistic regression is used to analyze the data, including methods of analysis to address issues in complex survey design. Results of this study indicate several different risk factors that influence the rate of suicidal ideation among adolescents, including alcohol and drug use, sexual risky behaviors, unhealthy weight loss methods, depressed mood, sex and race/ethnicity. The conclusions of this study indicate that many risk factors associated with suicidal ideation are behaviors that could be addressed with early intervention strategies to reduce the risk of suicidal ideation.

To significantly improve the early detection of cervical precancers and cancers, LightTouch™ is under development by SpectRx Inc.. LightTouch™ identifies cancers and precancers quickly by using a spectrometer to analyze light reflected from the cervix. Data from the spectrometer is then used to create an image of the cervix that highlights the location and severity of disease. Our research is conducted to find the appropriate models that can be used to generate map-like image showing disease tissue from normal and further diagnose the cervical cancerous conditions. Through large work of explanatory variable search and reduction, logistic regression and Partial Least Square Regression successfully applied to our modeling process. These models were validated by 60/40 cross validation and 10 folder cross validation. Further examination of model performance, such as AUC, sensitivity and specificity, threshold had been conducted.

Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

Most American universities and colleges require students to provide faculty evaluation at end of each academic term, as a way of measuring faculty teaching performance. Although some analysts think that this kind of evaluation does not necessarily provide a good measurement of teaching effectiveness, there is a growing agreement in the academic world about its reliability. This study attempts to find any strong statistical evidence supporting faculty evaluation by students as a measure of faculty teaching effectiveness. Emphasis will be on analyzing relationships between instructor ratings by students and corresponding students’ grades. Various statistical methods are applied to analyze a sample of real data and derive conclusions. Methods considered include multivariate statistical analysis, principal component analysis, Pearson's correlation coefficient, Spearman's and Kendall’s rank correlation coefficients, linear and logistic regression analysis.

Objectives This thesis is based on 4 papers that were all written with the same intent, which was to describe and demonstrate how epidemiologic concepts and data can serve as a basis for improved validity of probabilistic conclusions in forensic medicine (FM). Conclusions based on probability are common in FM, and the validity of probabilistic conclusions is dependant on their foundation, which is often no more than personal experience. Forensic epidemiology (FE) describes the use and application of epidemiologic methods and data to questions encountered in the practice of FM, as a means of providing an evidence-based foundation, and thus increased validity, for certain types of opinions. The 4 papers comprising this thesis describe 4 unique applications of FE that have the common goal of assessing probabilities associated with evidence gathered during the course of the investigation of traumatic injury and death.   Materials and Methods Paper I used a case study of a fatal traffic crash in which the seat position of the surviving occupant was uncertain as an example for describing a probabilistic approach to the investigation of occupant position in a fatal crash. The methods involved the matching of the occupants’ injuries to the vehicular and crash evidence in order to assess the probability that the surviving occupant was either the driver or passenger of the vehicle at the time of the crash. In the second and third papers, epidemiologic data pertaining to traffic crash-related injuries from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) was used to assess the utility and strength of evidence, such as vehicle deformation and occupant injury of a particular severity and pattern, as a means of assessing the probability of an uncertain issue of interest. The issue of interest in Paper II was the seat position of the occupant at the time of a rollover crash (similar to Paper I), and the association that was investigated was the relationship between the degree of downward roof deformation and likelihood of a serious head and neck injury in the occupant. The analysis was directed at the circumstance in which a vehicle has sustained roof deformation on one side but not the other, and only one of the occupants has sustained a serious head or neck injury. In Paper III the issue of interest was whether an occupant was using a seat belt prior to being ejected from a passenger vehicle, when there was evidence that the seat belt could have unlatched during a crash, and thus it was uncertain whether the occupant was restrained and then ejected after the seat belt unlatched, or unrestrained. Of particular interest was the relative frequency of injury to the upper extremity closest to the side window (the outboard upper extremity [OUE]), as several prior authors have postulated that during ejection when the seat belt has become unlatched the retracting seat belt would invariably cinch around the OUE and cause serious injury. In Paper IV the focus of the analysis was the predictability of the distribution of skull and cervical spine fractures associated with fatal falls as a function of the fall circumstances. Swedish autopsy data were used as the source material for this study. Results In Paper I the indifferent pre-crash probability that the survivor was the driver (0.5) was modified by the evidence to arrive at a post-test odds of 19 to 1 that he was driving. In Paper II NASS-CDS data for 960 (unweighted) occupants of rollover crashes were included in the analysis. The association between downward roof deformation and head and neck injury severity (as represented by a composite numerical value [HNISS] ranging from 1 to 75) was as follows: for each unit increase of the HNISS there were increased odds of 4% that the occupant was exposed to >8 cm of roof crush versus <8 cm; 6% for >15 cm compared to <8 cm, and 11% for >30 cm of roof crush compared to <8 cm. In Paper III NASS-CDS data for 232,931 (weighted) ejected occupants were included in the analysis, with 497 coded as seat belt failures, and 232,434 coded as unbelted. Of the 7 injury types included in the analysis, only OUE and serious head injury were found to have a significant adjusted association with seat belt failure, (OR=3.87, [95% CI 1.2, 13.0] and 3.1, [95% CI 1.0, 9.7], respectively). The results were used to construct a table of post-test probabilities that combined the derived sensitivity and (1 - specificity) rates with a range of pre-crash seat belt use rates so that the results could be used in an investigation of a suspected case of belt latch failure. In Paper IV, the circumstances of 1,008 fatal falls were grouped in 3 categories of increasing fall height; falls occurring at ground level, falls from a height of <3 meters or down stairs, and falls from ≥3 meters. Logistic regression modeling revealed significantly increased odds of skull base and lower cervical fracture in the middle (<3 m) and upper (≥3 m) fall height groups, relative to ground level falls, as follows: (lower cervical <3 m falls, OR = 2.55 [1.32, 4.92]; lower cervical ≥3 m falls, OR = 2.23 [0.98, 5.08]; skull base <3 m falls, OR = 1.82 [1.32, 2.50]; skull base ≥3 m falls, OR = 2.30 [1.55, 3.40]). Additionally, C0-C1 dislocations were strongly related to fall height, with an OR of 8.3 for the injury in a ≥3 m fall versus ground level. Conclusions In this thesis 4 applications of FE methodology were described. In all of the applications epidemiologic data resulting from prior FM investigations were analyzed in order to draw probabilistic conclusions that could be reliably applied to the circumstances of a specific investigation. It is hoped that this thesis will serve to demonstrate the utility of FE in enhancing evidence-based practice in FM.

Tuberculosis (TB) is called a disease of poverty and is the main cause of death from infectious diseases among adults. In 1993 the World Health Organisation (WHO) declared TB to be a global emergency; however there were still approximately 1.4 million deaths due to TB in 2011. This thesis contains a detailed study of the existing literature regarding the global risk factors of TB. The risk factors identified from the literature review search which were also available from the NFHS-3 survey were then analysed to determine how well we could identify respondents who are at high risk of TB. We looked at the stigma and misconceptions people have regarding TB and include detailed reports from the existing literature of how a persons wealth, health, education, nutrition, and HIV status affect how likely the person is to have TB. The difference in the risk factor distribution for the TB and non-TB populations were examined and classification trees, nearest neighbours, and logistic regression models were trialled to determine if it was possible for respondents who were at high risk of TB to be identified. Finally gender-specific statistically likely directed acyclic graphs were created to visualise the most likely associations between the variables.

In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions may be defined using Polya trees. This dissertation addresses statistical problems for which the Polya tree idea can be utilized to provide efficient and practical methodological solutions. One problem considered is the estimation of risks, odds ratios, or other similar measures that are derived by specifying a threshold for an observed continuous variable. It has been previously shown that fitting a linear model to the continuous outcome under the assumption of a logistic error distribution leads to more efficient odds ratio estimates. We will show that deviations from the assumption of logistic error can result in great bias in odds ratio estimates. A one-step approximation to the Savage-Dickey ratio will be presented as a Bayesian test for distributional assumptions in the traditional logistic regression model. The approximation utilizes least-squares estimates in the place of a full Bayesian Markov Chain simulation, and the equivalence of inferences based on the two implementations will be shown. A framework for flexible, semiparametric estimation of risks in the case that the assumption of logistic error is rejected will be proposed. A second application deals with regression scenarios in which residuals are correlated and their distribution evolves over an ordinal covariate such as time. In the context of prediction, such complex error distributions need to be modeled carefully and flexibly. The proposed model introduces dependent, but separate Polya tree priors for each time point, thus pooling information across time points to model gradual changes in distributional shapes. Theoretical properties of the proposed model will be outlined, and its potential predictive advantages in simulated scenarios and real data will be demonstrated.

As intra-industry trade increases in U.S. agricultural and food processing industries, the historical agricultural trade surplus is tightening. In efforts to maintain the trade surplus a focus has shifted towards the promotion of agricultural and processed food exports among small and medium sized firms. This study intends to identify and evaluate the potential for exports among small to medium sized agricultural and food processing firms in Kentucky through a collection of survey data. The objectives of this thesis are to identify the state’s product marketing opportunities and product specifications for international exports while identifying transaction requirements for potential exports. An analysis of the constraints and challenges faced by firms in the decision to export reveals rational behavior Binary logistic regression analysis is used to identify the impact of firm characteristics, perceived marketing conditions and information constraints, and financial aspects on a firm’s decision to export. A second logit regression analyzes the impact on a non-exporting firm’s interest in international marketing opportunities. The lack of international market information, financial constraints, and risk are found to be significant factors in the decision to export and interest in foreign marketing.