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Learner engagement, or the involvement of the student's cognitive and emotional energy to accomplish a learning task, has been called "the holy grail of learning" (Sinatra, Heddy, & Lombardi, 2015, p. 1) because of its correlations to academic achievement, persistence, and satisfaction. In the 21st century, learning will be increasingly "blended," combining face-to-face with computer-mediated instruction. Research is already exploring learner engagement in blended contexts, but no theoretical framework guides inquiry or practice. Developing models and measures of the factors that facilitate learner engagement is important to the advancement of the domain. This multiple-article format dissertation addresses the theoretical gap in research on learner engagement in blended settings. The first article reviews the existing literature on learner engagement, delineates a set of constructs most relevant to the contexts of blended learning, and proposes a theoretical framework for learner engagement in blended settings. The second article operationalizes and tests the proposed model of blended learning engagement using exploratory and confirmatory factor analysis. It creates and evaluates an end-of-course self-report measure of cognitive and emotional engagement. The unique factor structure of online and face-to-face indicators of learner engagement is clearly demonstrated in the results of this study.

The present study tested the interactive model of affect, motivation, and engagement (Linnenbrink, 2007) in mathematics education with a nationally representative sample. Self-efficacy, self-concept, and anxiety were indicators of pleasant and unpleasant affect. Intrinsic and extrinsic motivation were indicators of mastery and performance approach. Persistence and cognitive activation were indicators of behavioral and cognitive engagement. The 2012 Programme for International Student Assessment (PISA) supplied a sample of 4,978 students from the United States for structural equation modeling. The results indicated that PISA data overall supported the interactive model. Specifically, PISA data completely supported the specification of the relationship between motivation and affect, largely supported the specification of the relationship between affect and engagement, but failed to support the specification of the relationship between motivation and engagement. Finally, PISA data largely supported the specification of the mediation effects of affect on the relationship between motivation and engagement.

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.