abstract: The process of combining data is one in which information from disjoint datasets sharing at least a number of common variables is merged. This process is commonly referred to as data fusion, with the main objective of creating a new dataset permitting more flexible analyses than the separate analysis of each individual dataset. Many data fusion methods have been proposed in the literature, although most utilize the frequentist framework. This dissertation investigates a new approach called Bayesian Synthesis in which information obtained from one dataset acts as priors for the next analysis. This process continues sequentially until a single posterior distribution is created using all available data. These informative augmented data-dependent priors provide an extra source of information that may aid in the accuracy of estimation. To examine the performance of the proposed Bayesian Synthesis approach, first, results of simulated data with known population values under a variety of conditions were examined. Next, these results were compared to those from the traditional maximum likelihood approach to data fusion, as well as the data fusion approach analyzed via Bayes. The assessment of parameter recovery based on the proposed Bayesian Synthesis approach was evaluated using four criteria to reflect measures of raw bias, relative bias, accuracy, and efficiency. Subsequently, empirical analyses with real data were conducted. For this purpose, the fusion of real data from five longitudinal studies of mathematics ability varying in their assessment of ability and in the timing of measurement occasions was used. Results from the Bayesian Synthesis and data fusion approaches with combined data using Bayesian and maximum likelihood estimation methods were reported. The results illustrate that Bayesian Synthesis with data driven priors is a highly effective approach, provided that the sample sizes for the fused data are large enough to provide unbiased estimates. Bayesian Synthesis provides another beneficial approach to data fusion that can effectively be used to enhance the validity of conclusions obtained from the merging of data from different studies. / Dissertation/Thesis / Doctoral Dissertation Psychology 2017

Context: The amount of software in solutions provided in various domains is continuously growing. These solutions are a mix of hardware and software solutions, often referred to as software-intensive systems. Companies seek to improve the software development process to avoid delays or cost overruns related to the software development.   Objective: The overall goal of this thesis is to improve the software development/building process to provide timely, high quality and cost efficient solutions. The objective is to select the origin of the components (in-house, outsource, components off-the-shelf (COTS) or open source software (OSS)) that facilitates the improvement. The system can be built of components from one origin or a combination of two or more (or even all) origins. Selecting a proper origin for a component is important to get the most out of a component and to optimize the development.  Method: It is necessary to investigate the component origins to make decisions to select among different origins. We conducted a case study to explore the existing challenges in software development.  The next step was to identify factors that influence the choice to select among different component origins through a systematic literature review using a snowballing (SB) strategy and a database (DB) search. Furthermore, a Bayesian synthesis process is proposed to integrate the evidence from literature into practice.   Results: The results of this thesis indicate that the context of software-intensive systems such as domain regulations hinder the software development improvement. In addition to in-house development, alternative component origins (outsourcing, COTS, and OSS) are being used for software development. Several factors such as time, cost and license implications influence the selection of component origins. Solutions have been proposed to support the decision-making. However, these solutions consider only a subset of factors identified in the literature.    Conclusions: Each component origin has some advantages and disadvantages. Depending on the scenario, one component origin is more suitable than the others. It is important to investigate the different scenarios and suitability of the component origins, which is recognized as future work of this thesis. In addition, the future work is aimed at providing models to support the decision-making process.