Substance abuse is a serious issue in both modern and traditional societies. Besides health complications such as depression, cancer and HIV, social complications such as loss of concentration, loss of job, and legal problems are among the numerous hazards substance use disorder imposes on societies. Understanding the causes of substance abuse and preventing its negative effects continues to be the focus of much research. Substance use behaviors, symptoms and signs are usually measured in form of ordinal data, which are often modeled under threshold models in Structural Equation Modeling (SEM). In this dissertation, we have developed a general nonlinear optimizer for the software package OpenMx, which is a SEM package in widespread use in the fields of psychology and genetics. The optimizer solves nonlinearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. We have tested the performance of our optimizer on ordinal data and compared the results with two other optimizers (implementing SQP algorithm) available in the OpenMx package. While all three optimizers reach the same minimum, our new optimizer is faster than the other two. We then applied OpenMx with our optimization engine to a very large population-based drug abuse dataset, collected in Sweden from over one million pairs, to investigate the effects of genetic and environmental factors on liability to drug use. Finally, we investigated the reasons behind better performance of our optimizer by profiling all three optimizers as well as analyzing their memory consumption. We found that objective function evaluation is the most expensive task for all three optimizers, and that our optimizer needs fewer number of calls to this function to find the minimum. In terms of memory consumption, the optimizers use the same amount of memory.

Structural Equation Modeling (SEM) is a framework of statistical methods that allows us to represent complex relationships between variables. SEM is widely used in economics, genetics and the behavioral sciences (e.g. psychology, psychobiology, sociology and medicine). Model complexity is defined as a model’s ability to fit different data patterns and it plays an important role in model selection when applying SEM. As in linear regression, the number of free model parameters is typically used in traditional SEM model fit indices as a measure of the model complexity. However, only using number of free model parameters to indicate SEM model complexity is crude since other contributing factors, such as the type of constraint or functional form are ignored. To solve this problem, a special technique, Confirmatory Tetrad Analysis (CTA) is examined. A tetrad refers to the difference in the products of certain covariances (or correlations) among four random variables. A structural equation model often implies that some tetrads should be zero. These model implied zero tetrads are called vanishing tetrads. In CTA, the goodness of fit can be determined by testing the null hypothesis that the model implied vanishing tetrads are equal to zero. CTA can be helpful to improve model selection because different functional forms may affect the model implied vanishing tetrad number (t), and models not nested according to the traditional likelihood ratio test may be nested in terms of tetrads. In this dissertation, an R package was created to perform CTA, a two-step method was developed to determine SEM model complexity using simulated data, and it is demonstrated how the number of vanishing tetrads can be helpful to indicate SEM model complexity in some situations.

Many highway maintenance agencies are facing an increased pressure to utilize their limited resources while still achieving the optimum winter highway maintenance outcome. Also there is a tendency to privatize maintenance operation, in order to improve the road user's satisfaction by bringing more competition to winter maintenance operations. Given this context the purpose of this research is to develop an effective performance measurement system that can evaluate how well agencies have conducted winter maintenance activities to meet the road user's expectations of safety and mobility. Though there have been performance measurement studies conducted in the winter maintenance area, few of them are comprehensive enough to evaluate winter maintenance outcomes, while at the same time taking storm severity, road system characteristics, and maintenance effort together into consideration. To address this deficiency, several particular challenges must be considered: first, how to evaluate the storm severity for individual storms; second, how to evaluate maintenance outcomes using a series of quantitative measures; and third, what are the appropriate targets that maintenance outcomes can be compared with, considering outcomes are sensitive to maintenance input, weather severity, road classifications, and traffic specifications. To address these questions: A storm severity index is developed; studies on effects of weather were quantitatively synthesized by meta-analysis; effects of weather and maintenance on road surface conditions are estimated by MLR; SEM (Structural Equation Modeling) is applied to estimate the direct and indirect effects of maintenance on mobility and Multiple Classification Analysis (MCA) was applied to estimate the contribution of winter maintenance to safety. The final result of this research is an applicable winter maintenance performance measurement system. It informs maintenance agencies where they excel at and where improvements are needed for the specified goals. Further, the developed road surface condition prediction model can be used as a predictive tool to allow agencies to conduct "what if" experiments that will lead to optimization of maintenance practice over time. The relative magnitudes of the effects of different maintenance methods on mobility and safety that is estimated by the models will enable agencies to assign priorities, and to compare maintenance outcomes based on the input resources.

Young's schema theory offers a theoretical approach that relates negative childhood experiences, temperament, and early maladaptive schema, to the experience of negative affect and/or depression in adulthood. However, despite the widespread use of schema therapy in clinical practice, little research has explored the pathways theorized by Young. This study explored the pathways posited by Young and colleagues looking at the direct and indirect relationships among negative childhood experience, temperament, early maladaptive schema, and the experience of negative affect in adulthood. Self-report data were collected from 365 undergraduate students. Results demonstrated consistent and robust direct relationships between temperament and negative affect, as well as indirect relationships between temperament and/or NCE, schema, and the outcome of negative affect. Results, though mixed, reveal strengths of the schema therapy model and provide suggestions for future research.

This research seeks an answer to the following question: what is the relationship between the structure of researchers' communication network and the structure of their collaborative output networks (e.g. co-authored publications, joint grant proposals, and joint patent applications), and the impact of these structures on their citation performance and the volume of collaborative research outputs? Three complementary studies are performed to answer this main question as discussed below. 1. Study I: A frequently used output to measure scientific (or research) collaboration is co-authorship in scholarly publications. Less frequently used are joint grant proposals and patents. Many scholars believe that co-authorship as the sole measure of research collaboration is insufficient because collaboration between researchers might not result in co-authorship. Collaborations involve informal communication (i.e., conversational exchange) between researchers. Using self-reports from 100 tenured/tenure-track faculty in the College of Engineering at the University of South Florida, researchers' networks are constructed from their communication relations and collaborations in three areas: joint publications, joint grant proposals, and joint patents. The data collection: 1) provides a rich data set of both researchers' in-progress and completed collaborative outputs, 2) yields a rating from the researchers on the importance of a tie to them 3) obtains multiple types of ties between researchers allowing for the comparison of their multiple networks. Exponential Random Graph Model (ERGM) results show that the more communication researchers have the more likely they produce collaborative outputs. Furthermore, the impact of four demographic attributes: gender, race, department affiliation, and spatial proximity on collaborative output relations is tested. The results indicate that grant proposals are submitted with mixed gender teams in the college of engineering. Besides, the same race researchers are more likely to publish together. The demographics do not have an additional leverage on joint patents. 2. Study II: Previous research shows that researchers' social network metrics obtained from a collaborative output network (e.g., joint publications or co-authorship network) impact their performance determined by g-index. This study uses a richer dataset to show that a scholar's performance should be considered with respect to position in multiple networks. Previous research using only the network of researchers' joint publications shows that a researcher's distinct connections to other researchers (i.e., degree centrality), a researcher's number of repeated collaborative outputs (i.e., average tie strength), and a researchers' redundant connections to a group of researchers who are themselves well-connected (i.e., efficiency coefficient) has a positive impact on the researchers' performance, while a researcher's tendency to connect with other researchers who are themselves well-connected (i.e., eigenvector centrality) had a negative impact on the researchers' performance. The findings of this study are similar except that eigenvector centrality has a positive impact on the performance of scholars. Moreover, the results demonstrate that a researcher's tendency towards dense local neighborhoods (as measured by the local clustering coefficient) and the researchers' demographic attributes such as gender should also be considered when investigating the impact of the social network metrics on the performance of researchers. 3. Study III: This study investigates to what extent researchers' interactions in the early stage of their collaborative network activities impact the number of collaborative outputs produced (e.g., joint publications, joint grant proposals, and joint patents). Path models using the Partial Least Squares (PLS) method are run to test the extent to which researchers' individual innovativeness, as determined by the specific indicators obtained from their interactions in the early stage of their collaborative network activities, impacts the number of collaborative outputs they produced taking into account the tie strength of a researcher to other conversational partners (TS). Within a college of engineering, it is found that researchers' individual innovativeness positively impacts the volume of their collaborative outputs. It is observed that TS positively impacts researchers' individual innovativeness, whereas TS negatively impacts researchers' volume of collaborative outputs. Furthermore, TS negatively impacts the relationship between researchers' individual innovativeness and the volume of their collaborative outputs, which is consistent with `Strength of Weak Ties' Theory. The results of this study contribute to the literature regarding the transformation of tacit knowledge into explicit knowledge in a university context.

The National Institutes of Health recommends the development of conceptual models to increase rigor and improve evaluation in research. Validated models are essential to guide conceptualizations of phenomena, selection of variables and development of testable hypotheses. Structural equation modeling (SEM) is a methodology useful in model testing due to its ability to account for measurement error and test latent variables. The purpose of this study was to test a model of The Geriatric Cancer Experience in End of Life as adapted from Emanuel and Emanuel's framework for a good death using SEM. It was hypothesized that the model was a five-factor structure composed of clinical status, physical, psychological, spiritual and quality of life domains and that quality of life is dependent on the other factors. The sample was comprised of 403 hospice homecare patients. Fifty six percent were male, 97% were white with a mean age of 77.7. Testing of the model used AMOS statistical software. The initial five-factor model was rejected when fit indices showed mis-specification. A three-factor model with quality of life as an outcome variable showed that 67% of the variability in quality of life is explained by the person's symptom experience and spiritual experience. As the number of symptoms and the associated severity and distress increase, the person's quality of life significantly decreases (ß -0.8). As the spiritual experience increases (the expressed need for inspiration, spiritual activities, and religion) the person's quality of life significantly increases (ß 0.2). This is significant to nursing because the model provides a useful guide for understanding the relationships between symptoms, spiritual needs, and quality of life in end of life geriatric cancer patients and suggests variables and hypotheses for research. This study provides evidence for a strong need for symptom assessment and spiritual assessment, development of plans of care inclusive of symptom control and spiritual care, and implementation and evaluation of those plans utilizing quality of life as an indicator for the outcome of care provided by nurses.

Violence and aggression continue to be both public health and economic concerns. The field of violence prediction has undergone a series of changes in an attempt to best assess risk including using unstructured clinical judgment, actuarial measures, and structured professional judgment. Although prediction has become more accurate with improved measures, a new generation has recently emerged with an emphasis on understanding violence, as opposed to merely predicting it, to shift the focus towards violence prevention. In addition to the creation of measures, researchers have sought to identify specific risk factors for aggression and violence including static and dynamic risk factors. Despite research demonstrating associations between neuropsychological and social-cognitive factors, violence risk measures continue to omit these variables. The current study developed a multi-faceted, theoretical model of aggression including social-cognitive, neuropsychological, personality, and psychiatric factors. A community, male sample (N = 1,192) collected through Amazon's MTurk responded to a series of self-report measures and neuropsychological tasks. Utilizing structural equation modeling (SEM), I created a model predicting aggression. Several important paths were significant including from entity theory to aggression, mediated by hostile attribution bias, schizotypy to aggression, mediated by both hostile attribution bias and disinhibition, substance use to aggression mediated by disinhibition, and psychopathy to aggression directly. This model provides a framework for future research that focuses on process factors of violence and aggression.

In today’s technological environment, organizational capabilities for managing change are regarded as important for business survival and growth. In particular, dynamic organizational capabilities have attracted considerable research interest over the past decade. Recently several studies have suggested that dynamic capabilities may be associated with a concept termed organizational fitness. What is not clear in this emerging research stream is whether firms with superior organizational fitness are more likely to prosper than unfit firms. In addition, relatively little attention has been directed toward creating a systemic model of dynamic capabilities that explains organizational fitness. The nature of fitness has been intensively debated in the biological sciences over a period of several decades. A confusing variety of fitness definitions have emerged from this literature. The lack of an agreed definition of fitness has resulted in several streams of research on organizational fitness. As a result of this fragmentation, there has been little progress toward answering the question of how to measure organizational fitness. The fragmentation in organizational fitness literature is problematic, because research into the relationship between organizational fitness and firm performance is not well-advanced. In this study, organizational fitness is defined in terms of organizational capability to produce variation. By defining fitness in this way, the tautological criticisms leveled against existing concepts of fitness are avoided. The definition of fitness proposed here accommodates both an evolutionary learning perspective and a perspective of strategic management, and thus reflects an integrative approach to the concept. A notable feature of the literature exploring organizational fitness is that it has been focused on large corporations. However, a growing body of literature suggests that SMEs are different from large firms and need to be examined in their own right. SMEs are important contributors to business in most countries throughout the world. This study addresses that perceived gap in the literature and asks: What relationship, if any, is there between organizational fitness and business performance for SMEs? Theory is developed and tested here by means of a large sample of SMEs in New Zealand. Two distinct aspects of organizational fitness are identified for SMEs. First, survival fitness is associated with generic combinative capabilities. Second, growth fitness is associated with knowledge assimilation and transformation. SME growth fitness and survival fitness are each found to be positively related to business performance under a variety of contexts. Further, an increase of growth fitness and survival fitness provides a means of alleviating selection pressures for SMEs. That is, dynamic capabilities of knowledge assimilation and integration are found to be positively associated with SME business performance. In contrast to studies that advocate SME development of context-dependent capabilities, the findings of this study suggest an alternative perspective: variable selection pressures can be influenced by SMEs with a high level of survival and growth fitness.

Bronfenbrenners Process-Person-Context-Time model was used to examine the relationships among the process of negative parenting, the person characteristics of child temperament and early aggressive behaviour and the contexts of family income (in)adequacy and maternal depression from infancy to school entry and their effects on the outcome of aggressive behaviour in boys at school entry. The sample included 361 boys in two-parent families who participated in the Canadian National Longitudinal Survey of Children and Youth (NLSCY). Structural equation modeling was used with a repeated measures longitudinal design. The model explained 43% of the variance in boys aggressive behaviour at school age. The results indicated that, by preschool age, boys and mothers behaviours are well established, and that process, person, and context variables all influence the persistence of boys aggressive behaviour. The strength of the effects of these variables increased with their proximity to the developing child and decreased over time. By school age, concurrent effects were not significant. The addition of the contextual variables resulted in ill-fitting models. Modification indices suggested the ill fit was localized in modeling the persistence of maternal depression, and not in the relationship between maternal depression and the other variables in the model. Modification indices also suggested there may be reciprocal effects between boys aggressive behaviour and both negative parenting and maternal depression, but this was not tested. Future research using a cross-lagged panel design could clarify these relationships. This study contributes to a growing body of research on the development of aggressive behaviour in children and underscores the importance of examining the contribution of the multiple levels of process, person, context, and time to the development of aggressive behaviour. Findings of this study provide evidence that the effects of proximal processes and proximal contexts on the development of boys aggressive behaviour are strongest in infancy and toddlerhood, and their consequences extend through to school entry. Initiating prevention and intervention efforts in early childhood that provide parents-to-be and parents of young children with practical direction in ways to engage in positive and responsive interactions with their children would do more to reduce the development of aggressive behaviour in children than would later interventions aimed at changing entrenched behaviours in both parents and children.

Adjustment, particularly in adulthood, is a vague concept discussed among researchers. Most often researchers only consider lack of involvement in problem behavior as criteria for positive adjustment. Furthermore, it is unclear what factors influence the likelihood of adjustment and the influence of race on these factors is unknown. The current study proposed a composite of male adult adjustment that considers what the Wellness Model terms the “wholeness” of an individual. In addition, adolescent predictors of adult adjustment and the influence of race on factors influencing adjustment were examined in a longitudinal sample of 481 males. Results revealed 4 profiles of adjustment: 1 profile that included individuals who were overall adjusted, 2 profiles that included individuals who were moderately adjusted, and 1 profile of individuals who were maladjusted. The majority of the sample was identified as adjusted in that they were financially responsible, did not have psychological problems, engaged in little to no acts of delinquency, and acknowledged at least adequate social support. Note, however, that these individuals did engage in some substance use. The smallest profile of individuals was those who were maladjusted in that they engaged in excessive delinquency, used both drugs and drank alcohol heavily, and lacked a positive support system. However these individuals were also absent of psychological problems and were financially responsible. Findings also uncovered predictors of adjustment, such that high levels of depression, physical punishment, and poor relationships with peers were associated with only moderate levels of adjustment regardless of race. Furthermore, racial differences in predictors of adjustment were found. Anxiety and parent/child communication were associated with only moderate adjustment for African American but not Caucasian males. In contrast, mother’s arrest and peer delinquency were associated with only moderate adjustment for Caucasian American but not African American males. Recommendations for prevention and intervention strategies are discussed.