In this thesis, I have assessed and modelled invasion of alien plant species in the forest of the southeastern United States. There are over 380 recognized invasive plants in southeastern forests and grasslands with 53 ranked as high-to-medium risk to natural communities. I have focused on ten of these: Chinese lespedeza, tall fescue, Japanese honeysuckle, Chinese privet, autumn olive, princesstree, silktree, chinaberry, tree of heaven, tallowtree. Assessing them at differing scales, locally (Chapter 2 and 3), eco-regionally (Chapter 4 and 5) and regionally (Chapters 6 and 7), using field based measurements integrated with remotely sensed and digital datasets, and applying both parametric and non-parametric modelling approaches. Data from field based measurements as well as digitally available sources was evaluated, bringing together freely available data with time consuming, intensively collected data. Once models were developed application to assessing long term impacts was done by integrating potential climate change scenarios. At the local level Chinese lespedeza and Japanese Honeysuckle were the most prevalent, with models at the local level dominated by remotely sensed variables. At an eco-regional level Japanese honeysuckle was the most prevalent with models primarily dominated by environmental variables. At a regional level, where only trees were assessed, potential distributions of the invasive species ranged from 12 to 33 percent of the southeastern forests under current conditions with this dramatically increasing for chinaberry and tallowtree under most climate change scenarios, up as high as 66 percent of southeastern forest sites. In this thesis information on anthropogenic factors added some value to the models, however it was rarely dominant. Roads and land use (proportion of forest or distance to forest) were the most useful anthropogenic variables. In all models evaluated, only six times did any one anthropogenic variable represent more than 25 percent of the models, four of these were at the local scale. At the regional and eco-regional level, roads had a greater than 25 percent contribution to the silktree models, at a local level, distance to forest and distance roads contributed more than 25 percent to three of the species evaluated, sawtooth oak, Japanese honeysuckle and privet. Human activities have the most influence on invasion progression through dispersal (movement and introduction rate) and disturbance of the landscape (increased resource availability). Anthropogenic variables such as roads are likely to be a mechanism of spread, thus the more a model is driven by anthropogenic variables, the more likely the invasive plant is to be in the early stages of invasion process. Thus our results suggest that many of these species have moved through the first stages of invasion. Environmental characteristics play an important role in determining a site’s vulnerability to invasion. At an eco-region and regional scale, environmental characteristics dominated (>50%) all but one model (silktree at the regional scale). At the eco-region level elevation was the dominant variable, and at a regional level minimum temperature was the dominant variable. These have some correlation, with higher elevation often relating to lower temperatures, particularly at a smaller scale. This confirms the validity of matching the climate ranges of native species with the range of potential invasion, and the approach of integrating elevation, latitude and longitude to estimate potential distribution. It also suggests that climate change will influence the distribution and that variation in climate should be integrated into models. Two different modelling approaches, logistic regression and maximum entropy, were used throughout my thesis, and applied to the same data. Agreement between different modelling types adds strength to conclusions, while disagreement can assist in asking further questions. The inclusion in the models of similar variables with the same direction of relationships gives confidence to any inference about the importance of these variables. The geographical agreement between models adds confidence to the probability of occurrence in the area. Alternatively using the same model but different datasets can give you similar information. Overall for all models created by both logistic regression and MaxEnt, the logistic regression had slightly better omission rates and the MaxEnt model had better AUC’s. Logistic regression models also often predicted larger geographical areas of occurrences when the threshold of maximum sensitivity plus specificity was used, thus the lower omission rates is related to the less stringent model that predicts a larger area. The selection of appropriate data to answer the question was shown to be fundamental in Chapter 7. When data were used outside of the area of interest it generalized the models and increased the potential for invasion significantly. There was more value in the intensive surveyed data but this was less dramatic than in using the defined areas of interest to select the data for models.

Multicollinearity can be present in the propensity score model when estimating average treatment effects (ATEs). In this thesis, logistic ridge regression (LRR) and principal components logistic regression (PCLR) are evaluated as an alternative to ML estimation of the propensity score model. ATE estimators based on weighting (IPW), matching and stratification are assessed in a Monte Carlo simulation study to evaluate LRR and PCLR. Further, an empirical example of using LRR and PCLR on real data under multicollinearity is provided. Results from the simulation study reveal that under multicollinearity and in small samples, the use of LRR reduces bias in the matching estimator, compared to ML. In large samples PCLR yields lowest bias, and typically was found to have the lowest MSE in all estimators. PCLR matched ML in bias under IPW estimation and in some cases had lower bias. The stratification estimator was heavily biased compared to matching and IPW but both bias and MSE improved as PCLR was applied, and for some cases under LRR. The specification with PCLR in the empirical example was usually most sensitive as a strongly correlated covariate was included in the propensity score model.

Smoking rates among senior students have been related to smoking initiation in younger students. Opportunities to select smoking friends may be one explanation, however our understanding of this process has been limited by cross-sectional designs. <br ><br /> The purpose of this longitudinal study was to determine whether senior student smoking rates a) predict smoking initiation in younger elementary school students, controlling for individual exposure to family and friends who smoke and b) are related to the selection of smoking friends, increasing risk of smoking initiation as a result. <br ><br /> This study involved secondary data analysis of 2798 students from 84 Ontario elementary schools involved with the Third Waterloo Smoking Prevention Project (WSPP3). Grade 8 students completed a questionnaire at baseline to obtain the percentage of senior students who smoke in each school. Students in grade 6 completed a similar questionnaire at baseline, and were surveyed again in grades 7 and 8. Multilevel regression analyses were used to examine school and individual characteristics simultaneously. <br ><br /> Each 5% increase in the senior student smoking rate at a school increased the risk that a non-smoking grade 6 student would try smoking more than once by grade 8 (OR 1. 05) and that a non-smoking grade 6 student with no smoking friends would gain a smoking friend by grade 7 (OR 1. 10). Students who remained non-smokers in grade 7 but gained a smoking friend were more likely to try smoking more than once by grade 8 (OR 4. 31). <br ><br /> In schools where a high proportion of senior students smoked, younger students were more likely to initiate smoking, and gain a smoking friend. Anti-smoking policies and interventions may be more urgently required in these schools to lower senior student smoking rates and reduce initiation among younger students. Tailoring the intensity and content of programs to match the needs of schools is one way to potentially maximize effectiveness.

Information technology is constantly changing, and if academic users are to make best use of these resources, they must sustain efficient information-seeking behaviour. This study explores the information-seeking behaviour of graduate students at Kuwait University, and investigates the factors influencing that behaviour. The population also includes faculty members engaged in teaching and supervising graduate students, and academic librarians. Adopting Wilson's information-seeking model (1999) as the theoretical framework, the study identifies factors influencing graduate students' information behaviour and formulates hypotheses that illustrate the relationship between the different variables. The use of this model provides useful insights into determinants of the information-seeking behaviour patterns of students in a multidisciplinary graduate context. The research uses a mixed method approach, comprising questionnaire survey, focus groups and semi-structured interviews. Application of the Critical Incident Technique method provided in-depth data about the patterns of information-seeking behaviour of both graduate students and faculty members. Logistic regression revealed that significant factors related to library awareness, information literacy, organisational and environmental issues, source characteristics, and demographics act as determinants of the patterns of students' information-seeking behaviour. Uneasiness on the part of graduate students towards using the library and consulting its personnel reflects a broader negative perception of the role of the library in shaping students' information-searching patterns. The clearest finding that emerged from the analysis of the students' information literacy dimension was that the majority of graduate students still face difficulties in finding the appropriate information resources, particularly when using resources that need advanced search strategies. Both quantitative and qualitative analyses revealed a heavy reliance on the information resources that require least effort (search engines, Internet websites, and personal contacts). Further, results revealed that graduate students are overwhelmed by an information overload, which leads them to become anxious about finding the appropriate information resources. Based on the results of the research, recommendations are made to further explore the information-seeking behaviour patterns of graduate students in order to enhance their information literacy skills. Improving information-seeking behaviour and enhancing the information literacy of students require interventions on various fronts: faculty members, academic librarians, the university administration, and graduate students themselves.

This thesis examines the electoral performance of minor parties in English local government from 1973 to 2008, a period that has seen a sharp increase in the numbers of candidates from such parties. Beginning with an overall assessment of the extent to which candidates from minor parties have contested local elections and the level of success in being elected, the thesis then explores the spatial distribution of minor party candidates, the types of people that become candidates and, considering the relative lack of electoral success, their motivations for standing. Traditional studies of party systems frequently exclude parties that do not win a relatively large share of votes and seats or are incapable of forming part of a subsequent government or administration. However broader definitions of what constitutes a party allow that small political parties can influence policy and the behaviour of mainstream parties simply because they are present in an electoral contest. Using the concept of presence, this thesis demonstrates that there has been an unprecedented increase in contestation by minor parties over the past 10 years that has not been proportionally matched by vote share and electoral success. Examination of patterns of contestation reveals that the growth in minor party contestation is uneven across England but is not apparently related to the different electoral systems used by local authorities. Furthermore, it becomes clearer that minor party contestation appears to be primarily a function of temporal local factors and is not necessarily influenced by electoral history, thus making it difficult to predict beforehand where and when such parties may begin to contest local elections and the relative level of electoral support they might subsequently enjoy. Having examined at a general level the electoral nature of minor party activity the thesis offers a new typology, an analytical framework within which to locate the various types of minor parties that feature in modern English local government. Using individual level data we examine whether it is possible to distinguish between candidates based on their party’s location within this typology, thus testing whether the recent increase in minor party activity is due to the rise of a new political class. Initial survey observations subsequently inform the development of a multinomial logistic regression model that seeks to identify similarities and differences between candidates standing for the range of parties currently contesting English local elections. This modelling suggests that candidates from across the range of minor parties are rather similar to each other and, moreover, similar to candidates from major parties. There does not, therefore, appear to be any association between the rise in the frequency of minor party candidates and the existence of a new political class of candidates that are different to those already located within the party political mainstream. Consequently, the value of the typology as a heuristic for establishing a better understanding of minor party activity is brought into question and further research into the phenomenon of minor party contestation in English local government is recommended.

This paper is exploring return migration in Sweden by implementing logistic regression technics on the cohort born 1978. In order to evaluate how socio-economic and geographical characteristics influence individuals propensities to re-circulate to the municipality of origin. Previous studies have indicated that socio-economic status is a selective trait that can either push or pull return migrants, depending on the setting. The theory of urban hierarchies was also applied to investigate if people were more likely to move back to certain region types. The calculations showed that 22 % of the movers had returned to their place of origin, with regional variations ranging from 18-30 %. The regression result revealed that a high socio-economic status decreased the likelihood of returning, while growing up in metropolitan city and having strong social capital in the place of origin increased the propensity. The findings were further supporting that movers have higher incomes than stayers, while return migrants gained less on their re-location in relation to all movers. I argue that these varying likelihoods depend on structural socio-economic divisions, which are pulling human capital to the metropolitan regions and causing a brain drain in the periphery. These population trends are replicating themselves over time and it is assumed that these processes are to enforce the regional disparities in the future.

Approved for public release, distribution is unlimited / This thesis analyzes the effects of commissioning sources on the retention and promotion of U S Army officers The data in this thesis were taken from the Active Duty Military Master File, with separate data sets for cohorts commissioned from 1981 through 2001 We describe three logistic regression models: Retention to the Grade of O-4, Promotion to O-4, and Promotion to O-5 We conclude that Academy graduates have the lowest retention rates, whereas OCS graduates have the highest retention rates Among male officers, retention rates are higher for ROTC graduates than for those with Direct Appointments; among female officers retention rates are higher for Direct Appointments than ROTC graduates The Promotion to O-4 Model indicates that the effect of commissioning source is different within gender, race and marital status groups The results of the promotion to O-5 model contrasts with those of the O-4 models Academy graduates are more likely to be promoted to Lieutenant Colonel than those from other sources, followed by ROTC graduates and then Direct Appointments The effects of the Army's reduction in force ("drawdown") between 1989 and 1996 are not accounted for in this thesis as they cannot be modeled with the data at hand. / First Lieutenant, Turkish Army

How does body armor and posture affect Soldier marksmanship? The Interceptor Body Armor (IBA) has significantly improved Soldier combat survivability, but in what ways does it change rifleman lethality? Moreover, can we model these effects so as to develop better tactics and operational plans? This study quantifies the effects of Soldier equipment on lethality through multi-factor logistic regression using data from range experiments with the 1st Brigade, 1st Infantry Division (Mechanized), at Fort Riley, Kansas. The designed experiment of this study estimates the probability of a qualified US rifleman hitting a human target. It uses the rifleman's equipment, posture, Military Occupational Specialty (MOS), and experience along with the target's distance, time exposure and silhouette presentation as input factors. The resulting family of mathematical models provides a Probability of Hit prediction tailored to a shooter-target scenario. The study shows that for targets closer than 150 meters, Soldiers shot better while wearing body armor than they did without. Body armor had a negative effect for targets farther than 200 meters, and this could significantly impact the employment of the Squad Designated Marksman. The study also shows that the kneeling posture is an effective technique and recommends standardized training on this method of firing.

Students transitioning into college from public school require more than just academic readiness; they also need the personal attributes that allow them to successfully transition into a new community (Braxton, Doyle, Hartley III, Hirschy, Jones, & McLendon, 2014; Nora, 2002; Nora, 2004; Tinto, 1975). Rural students have a different educational experience than their peers at schools in suburban and urban locations (DeYoung & Howley, 1990; Gjelten, 1982). Additionally, the resources, culture, and educational opportunities at rural schools also vary among different types of rural communities. Although some studies have examined the influence of rural students' academic achievement on college access and success, little research has analyzed the relationship between students of different types of rural communities and their persistence in post-secondary education. This study examined the likelihood for college-going students from three different types of rural communities to successfully transition into and persist at a four-year residential college. Multilevel logistic modeling was used to analyze the likelihood for students to persist in college for up to two academic years based on whether they were from rural tourist communities, college communities, and other rural communities. The analysis controlled for a variety of student and high school factors. Findings revealed that student factors related to poverty and academic readiness have the greatest effects, while the type of rural community has no significant influence on college persistence.

The postsecondary enrollment of student veterans has increased with the troop draw down in Iraq and Afghanistan as well as the generous amendments made to the Post 9/11 GI Bill. Acquired disabilities remain a reality for this population as they transition into the civilian world; consequently, previous literature cites the role of disabilities amongst student veterans. Also, prior research often aggregates these two groups without a thorough understanding of the ways in which they differ. This study compared student veterans with disabilities to student veterans without disabilities in order to understand the enrollment and demographic factors on which they differed, if any. Using a secondary data analysis of the 2007-2008 National Postsecondary Student Aid Survey, univariate tests of significance, a logistic regression, and a discriminant function analysis examined the relationship between disability status and seven predictor variables: age, gender, GPA, major, risk index, degree program type, and whether or not a student was exclusively a distance learner. These seven variables as a whole were not significant predictors of disability status; however, the models provided valuable insight into the similarities and characteristics shared within this population. Univariate tests of significance revealed that students with disabilities had a significantly lower mean GPA, were more often male, tended to favor certain academic majors over others, more often enrolled in bachelor’s degree versus associate and certificate programs, and had a lower risk of attrition based on their index of risk. Major, degree program type, and risk index proved to be the most significant predictors of disability status in LR and DFA. A student veteran’s age and whether they were a distance learner had no significant bearing on disability status indicating that student veterans enroll in distance learning or campus-based programs without influence from an orthopedic or mobility impairment, the most common type of disability amongst student veterans. This study offers a full description of student veterans with disabilities including the specific types of disabilities with which this population enters higher education.