The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.

The thesis deals with optimization of project management in a custom web development company. The theoretical part is devoted to the description of traditional and agile approaches of project management. The analytical part describes the current state of project management. The main part of the thesis consists of optimization of project management by agile SCRUM method, case study implementation and evaluation of benefits and costs of introduced change.

Prototyper är avgörande för att lyckas med designprocessen. Ett återkommande hinder är att översätta fysiska prototyper till digitala. Genom att utveckla ett nytt verktyg och förbättra den allmänna designmetodiken, kan nya idéer och resultat blomstra som kan få stor betydelse för framtidens design. Resultatet är en noggrant framtagen uppsättning av geometriska former som tillsammans med en app kallad Eascan möjliggör för användaren att kombinera fysiska och digitala prototyper effektivt.

Appealing due to its universality, cross-validation is an ubiquitous tool for model tuning and selection. At its core, cross-validation proposes to split the data (potentially several times), and alternatively use some of the data for fitting a model and the rest for testing the model. This produces a reliable estimate of the risk, although many questions remain concerning how best to compare such estimates across different models. Despite its widespread use, many theoretical problems remain unanswered for cross-validation, particularly in high-dimensional regimes where bias issues are non-negligible. We first provide an asymptotic analysis of the cross-validated risk in relation to the train-test split risk for a large class of estimators under stability conditions. This asymptotic analysis is expressed in the form of a central limit theorem, and allows us to characterize the speed-up of the cross-validation procedure for general parametric M-estimators. In particular, we show that when the loss used for fitting differs from that used for evaluation, k-fold cross-validation may offer a reduction in variance less (or greater) than k. We then turn our attention to the high-dimensional regime (where the number of parameters is comparable to the number of observations). In such a regime, k-fold cross-validation presents asymptotic bias, and hence increasing the number of folds is of interest. We study the extreme case of leave-one-out cross-validation, and show that, for generalized linear models under smoothness conditions, it is a consistent estimate of the risk at the optimal rate. Given the large computational requirements of leave-one-out cross-validation, we finally consider the problem of obtaining a fast approximate version of the leave-one-out cross-validation (ALO) estimator. We propose a general strategy for deriving formulas for such ALO estimators for penalized generalized linear models, and apply it to many common estimators such as the LASSO, SVM, nuclear norm minimization. The performance of such approximations are evaluated on simulated and real datasets.

Scholars have called for contextual based explorations of factors impacting implementation of integrated health care (IHC), yet IHC researchers agree that a paucity of research remains. (Kirschbaum, Rask, Brennan, Phelan, & Fortner, 2012; Miller, Kessler, Peek, & Kallenberg, 2011). Even less is known about the process of behavioral and physical health care integration in student health centers on university campuses (Alschuler et al., 2008). The purpose of this study was to address specific gaps in the literature of contextualized processes of integration (Kwan & Nease, 2013; Xyrichis & Lowton, 2008) by examining clinician, support staff, and administrator experiences with the process of integration at a student health center and to identify how values, principles, and attitudes impact this process. The results of a qualitative analysis of nine focus groups show the complexity associated with the process of integration as well as the factors impacting implementation at a university health center. Grounded theory methodology was used to analyze the data in three steps: (a) open-coding, (b) axial coding, and (c) selective coding (Corbin & Strauss, 2015). The analysis revealed three axial level categories comprised of 16 open-coding level categories and subcategories. A grounded theory model was developed and depicts how the various phenomena revealed at the axial level were interrelated in the early stages of the Student Health Center’s integration efforts.The conclusions of this study revealed that this organization’s integration was characterized by a collection of interacting Individual-Level, Organization-Level, and Communication variables, including barriers and facilitators of integration, which impact the process of integration. At the center of the theoretical structure was a co-constructed base of knowledge and attitudes from which staff approached and understood integrationa Shared Construct Representing Integration. However, since staff were at the beginning of the process of integration, the form of this construct had not yet come into focus. Therefore, the model is depicted as a “snapshot” in time. These conclusions have many implications for Administrators of student health centers who are considering integration. Prior to embarking on the integration process, interested parties are encouraged to seriously consider the many variables, processes, and strategies identified in this study.

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Sampling in onarm production environments presents challenges that must be considered when doing hazard analysis. The sensitivity and specificity of the test used and the sample types chosen will have an impact on the food safety outcome and food safety decisions made during the interpretation of results. In this work, broiler houses were sampled for the presence of Salmonella spp. using two different sampling strategies and four different microbiological isolation procedures. The study was undertaken after complications arose during a field study evaluating the role darkling beetles play in the transmission of foodborne pathogens. It was determined that, based on this work, incorporating a secondary enrichment procedure into the isolation protocol significantly increased the isolation rate from the various sample types, including drag swabs and litter samples. It was also determined that when attempting to characterize the Salmonella-status of a particular broiler house, no one sampling strategy is superior. The results of this study demonstrate that both drag swabs and litter samples need to be utilized to accurately determine if the pathogen is present in a flock. Not only did the secondary enrichment procedure have a higher isolation frequency than the other three methods compared, it also highlighted the discrepancies of the other methods. Two commonly used isolation procedures, tetrathionate and Rappaport-Vassiliadis, were found to disagree on a significant number of samples analyzed. While the isolation frequencies for these procedures were not found to be statistically different, the analysis for agreement, kappa, did indicate that the procedures did not identify the same samples as positive. Overall, the secondary enrichment procedure identified all the samples positive that were also found to be positive by either of the other methods used. Since the secondary enrichment method is a modified version of the traditional delayed secondary enrichment procedure, which requires five additional day of incubation, this study also compared these two procedures. It was determined that the secondary enrichment protocol was as effective for isolating Salmonella from broiler house samples as the delayed secondary enrichment procedure. The secondary enrichment procedure, did however, provide for a quicker turn around for results.

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No description available.