In today’s information age, technological advances in virtually every industry allow organizations, both big and small, to create and store more data than ever before. Though data are highly abundant, they are still often underutilized resources with regard to improving organizational performance. The popularity and intrigue around big data specifically has opened up new opportunities to study how organizations embrace evidence and use it to improve their business. Generally, the focus of big data has mainly been on specific technologies, techniques, or its use in everyday life; however, what has been critically missing from the conversation is the consideration of culture and climate to support effective data use in organizations. Currently, many organizations want to develop a data-embracing climate or create changes to make their existing climates more data-informed. The purpose of this project was to develop a scale to assess the current state of data usage in organizations, which can be used to help organizations measure how well they manage, share, and use data to make informed decisions. I defined the phenomena of a data-embracing climate based on reviewing a broad range of business, computer science, and industrial-organizational psychology literature. Using this definition, I developed a scale to measure this newly defined construct by first conducting an exploratory factor analysis, then an item retranslation task, and finally a confirmatory factor analysis. This research provides support for the reliability and validity of the Albrecht Data-Embracing Climate Scale (ADEC); however, the future of this new area of research could benefit by replicating the results of this study and gaining support for the new construct. Implications for science and practice are discussed. I sought to make a valuable contribution to the field of I-O psychology and to make a useful instrument for researchers and practitioners in multiple and diverse fields. I hope others will benefit from this scale to measure how organizations use evidence from data to make informed decisions and gain a competitive advantage beyond intuition alone. Do not cite without express permission from the author.

ABSTRACT Background: Alcohol use disorders (AUD) affect health and wellbeing, and have broad societal costs (Bouchery, Harwood, Sacks, Simon, & Brewer, 2011; Rehm et al., 2009; Sudhinaraset, Wigglesworth, Takeuchi, & Tsuker, 2016). While treatments have existed for decades, they are limited in success and expensive to administer. As such, understanding which factors best predict who will benefit most from treatment remains a laudable goal. Prior attempts to predict factors associated with positive treatment outcome are limited by methodology including statistical methods that lead to poor predictive power in new samples. This study aims to use a data-driven approach to clarify the predictors of AUD treatment success (Objective 1) accompanied by a theory-driven analysis assessing the mediation of treatment outcomes through psychological distress (Objective 2). Methods: One hundred forty-five patients seeking treatment for alcohol use problems at the Day One Intensive Outpatient Treatment Program (part of UVM Medical Center) between June 2011 and June 2012 were examined. Variables were extracted through chart review and were categorized using the Bronfenbrenner Ecological Model. First, 20% of the sample was set-aside for model testing, and the remaining 80% was used in an Elastic Net Regularized linear regression, with 10-fold cross validation. Models were tested on the set-aside sample to yield estimates of out-of-sample prediction and repeated models were compared to ensure generalizability. Next, a theoretical model was tested examining a model of psychological distress mediating the relationship between individual predictors and treatment outcome. Results: The models developed from the Elastic Net Regularization approach demonstrated consistency in model strength (mean=0.32, standard deviation=0.03) with models ranging from 14 to 31 included variables. Across the models, 15 variables occurred in >75% of the models, and an additional 7 variables were included in 25% - 75% of the models. Some of the strongest predictors included treatment non-compliance (β=-0.92), ASI Alcohol Composite (β=0.63), treatment dosage (β =-0.36), and readiness to change (β=-0.95). The results of the theory-driven mediation analysis demonstrated several strong direct predictors of outcome frequency of alcohol use, including readiness to change (β=-0.59), initial frequency of alcohol use (β=0.27), and access to a primary care physician (β=-2.20). The theoretical model found that none of the mediation pathways (testing psychological variables) were significantly different from the direct models. Conclusions: This study used both data-driven and theory-driven methods to examine factors affecting treatment of AUDs. The application of data-driven methods provided several predictors of outcome that can guide treatment efforts within Day One IOP treatment, as well as generalized to other abstinence-based treatment settings. For example, focusing on treatment attendance and using motivational interviewing to enhance readiness to change are methods supported by this study. Demographic variables that have been shown to predict treatment outcome in small studies, without cross-validation were not identified by the elastic net regression (e.g., age and gender). It is suspected that this is due to model overfitting in prior studies supporting the importance of using generalizable statistical methods to understand predictors of treatment outcome. This notion is supported by the results of the theory-driven model, which did not yield a strong model of treatment success. Taken together, the results support the use of strong analytic techniques which will guide theory in the future.

There is little evidence concerning the impact of professional learning communities (PLCs) at juvenile correctional facilities. This qualitative case study explored the implementation of a PLC at a juvenile correctional facility school that housed students 10 to 19 years of age in southeastern United States. The purpose of this study was to understand the perceptions of teachers and paraprofessionals about how the PLC supported their work as they designed, constructed, and delivered instruction at the correctional facility. The social interactions among engaged educators through collaboration, collective inquiry, reflections, and communication derived from constructivist learning theory. Qualitative methodology included document review and structured face-to-face interviews with 4 teachers and 3 paraprofessionals. Following an inductive model, educators' perceptions were analyzed using an open coding process to derive categories, themes, and meaning. Five themes emerged: professional learning growth and benefits, teacher learning in PLCs, attitude adjustment of the culture, collaboration and sharing, and active engagement of paraprofessionals in PLCs. This study provided 5 recommendations: use allotted time, prioritize concerns, keep an open communication, discuss student-centered questions, and ensure supportive relationships. The findings indicated that the PLC supported teachers and paraprofessionals with strategies and accommodations to promote student achievement. This study has the potential to strengthen teacher collaboration and instruction to empower incarcerated students to succeed academically and become productive citizens.

Mandates have been issued for educators to collaborate and improve student achievement, requiring a change in instructional practices through teacher talk. Teachers have struggled to make the transitional conversion from team planning to observed changes in instructional practices with evidence of improvement. The purpose of this qualitative study was to examine how teachers collaborated while following the Ohio Improvement Process. The purpose was then to make data-driven changes regarding instructional practices in the continuous improvement cycle. The conceptual framework was constructed from the teachers' dialogic stances towards talk of instruction, along with the intellectual and emotional attitudes teachers have about making changes. The guiding research question examined the ways teachers have been influenced by each other to make changes in instructional practices. The case study design observed a sample of 10 teachers from two teacher-based teams, with five of those teachers being interviewed. Observational data were examined for dialogic stance toward talk of instructional practices, whereas interview data were analyzed looking for evidence of the cognitive restructuring. Statements were categorized as motivations and influences. The analysis revealed that the teachers are changing their thinking through motivations and influences from collaboration. Literature has supported the findings that teachers could benefit from a gradual implementation process leading to the continuous improvement cycle. By developing a policy recommendation paper with a focus on teacher learning, positive social change may include preparing and empowering teachers for the changes that occur through collaboration.

Administrators at a Southeastern elementary school eliminated single-sex instructional grouping in 5th-grade classes without a proper analysis of all available data and later reflected upon whether this instructional model should be revived. Because data-based decisions may positively improve teaching and learning for all stakeholders, the purpose of this qualitative case study was to explore all available data leading to this decision, inform stakeholders about the decision-making processes in the local school, and provide data to inform future decisions. Conceptually framed with Mandinach's data-driven decision making (DDDM) model, the guiding question for the study focused on perceptions of teacher, administrator, and leadership team member about the DDDM process related to single-sex instructional grouping in the local venue. The data were collected using 8 interviews with administrators, teachers, and school leadership team members involved in the instructional decision. Data from interview were transcribed, analyzed, and coded for emergent themes, types of data and decisions, decision making processes, and stakeholder perceptions. The findings showed a gap in DDDM practice and affirmed the value of data for informed decision making. The findings guided recommendations for a professional development series created to increase data literacy and DDDM best practices. Improving DDDM for teaching and learning may promote positive social change by developing educational stakeholder skill sets for all decision-making as well as providing targeted, data-driven instruction for learners whether in multi- or single-sex instructional grouping.

The primary objective of this research is to model and optimize wastewater treatment process in a wastewater treatment plant (WWTP). As the treatment process is complex, its operations pose challenges. Traditional physics-based and mathematical- models have limitations in predicting the behavior of the wastewater process and optimization of its operations. Automated control and information technology enables continuous collection of data. The collected data contains process information allowing to predict and optimize the process. Although the data offered by the WWTP is plentiful, it has not been fully used to extract meaningful information to improve performance of the plant. A data-driven approach is promising in identifying useful patterns and models using algorithms versed in statistics and computational intelligence. Successful data-mining applications have been reported in business, manufacturing, science, and engineering. The focus of this research is to model and optimize the wastewater treatment process and ultimately improve efficiency of WWTPs. To maintain the effluent quality, the influent flow rate, the influent pollutants including the total suspended solids (TSS) and CBOD, are predicted in short-term and long-term to provide information to efficiently operate the treatment process. To reduce energy consumption and improve energy efficiency, the process of biogas production, activated sludge process and pumping station are modeled and optimized with evolutionary computation algorithms. Modeling and optimization of wastewater treatment processes faces three major challenges. The first one is related to the data. As wastewater treatment includes physical, chemical, and biological processes, and instruments collecting large volumes of data. Many variables in the dataset are strongly coupled. The data is noisy, uncertain, and incomplete. Therefore, several preprocessing algorithms should be used to preprocess the data, reduce its dimensionality, and determine import variables. The second challenge is in the temporal nature of the process. Different data-mining algorithms are used to obtain accurate models. The last challenge is the optimization of the process models. As the models are usually highly nonlinear and dynamic, novel evolutionary computational algorithms are used. This research addresses these three challenges. The major contribution of this research is in modeling and optimizing the wastewater treatment process with a data-driven approach. The process model built is then optimized with evolutionary computational algorithms to find the optimal solutions for improving process efficiency and reducing energy consumption.

The test of homogeneity for intraclass correlation coefficients has been one of the active topics in statistical research. Several chi-square tests have been proposed to test the homogeneity of intraclass correlations in the past few decades. The big concern for them is that these methods are seriously biased when sample sizes are not large. In this thesis, data driven approaches are proposed to testing the homogeneity of intraclass correlation coefficients of several populations. Through simulation study, data driven methods have been proved to be less biased and accurate than some commonly used chi-square tests.

Applied Research in System Analysis (ARiSA) is a company specialized in the development of the customer-specific quality models and applied research work. In order to improve the quality of the projects and to reduce maintenance costs, ARiSA developed Quality Monitor (QM) – a web application for quality analysis. QM application has been originally developed as a basic program to enable customers to evaluate the quality of the sources. Therefore, the business logic of the application was simplified and certain limitations were imposed on it, which in its turn leads to a number of issues related to user experience, performance and architecture design. These aspects are important for both application as a product, and for its future promotion. Moreover, this is important for customers, as end users. Main application issues, which were added to the maintenance list are: manual data upload, insufficient server resources to handle long-running and resource consuming operations, no background processing and status reporting, simplistic presentation of analysis results and known usability issues, weak integration between analysis back-ends and front-end. ­­­­­­­­­­­In order to address known issues and to make improvements of the existing limitations, a maintenance phase of QM application is initiated. First of all, it is intended to stabilize current version and improve user experience. It also needed for refactoring and implementation of more efficient data uploads processing in the background. In addition, extended functionality of QM would fulfill customer needs and transform application from the project into a product. Extended functionality includes: automated data upload from different build processes, new data visualizations, and improvement of the current functionality according to customer comments. Maintenance phase of QM application has been successfully completed and master thesis goals are met. Current version is more stable and more responsive from user experience perspective. Data processing is more efficient, and now it is implemented as background analysis with automatic data import. User interface has been updated with visualizations for client-side interaction and progress reporting. The solution has been evaluated and tested in close cooperation with QM application customers. This thesis describes requirements analysis, technology stack with choice rationale and implementation to show maintenance results.

Objectives: The aim of the two exploratory studies presented here, was to investigate expert-novice cognitive performance in the field of dietetic counseling. More specifically, the purpose was to characterize the knowledge used and the cognitive reasoning strategies of expert, intermediate and novice dietitians during their assessment of clinical vignettes of simulated dyslipidemia cases. Background: Since no studies have been conducted on the expert-novice differences in knowledge utilization and reasoning in the field of dietetics, literature from various domains looking at expert-novice decision-making was used to guide the studies presented here. Previous expert-novice research in aspects of health such as counseling and diagnostic reasoning among physicians and nurses has found differences between in the way experts extract and apply knowledge during reasoning. In addition, various studies illustrate an intermediate effect, where generalist performance is somewhat poorer than that of experts and novices. Methods: The verbal protocols of expert (n=4), generalist (n=4), and novice (n=4) dietitians were analyzed, using propositional analysis. Semantic networks were generated, and used to compare reasoning processes to a reference model developed from an existing Dyslipidemia care map by Brauer et al, (2007, 2009). Detailed analysis was conducted on individual networks in an effort to obtain better understanding of cue utilization, concept usage, and overall cohesiveness during reasoning. Results: The results of the first study indicate no statistical differences in reasoning between novices, generalist and experts with regards to recalls and inferences. Interesting findings in the study also suggest that discussions of the terms “dietary fat” and “cholesterol” by individuals in each level of expertise had qualitative differences. This may be reflective of the information provided in the case scenearios to each participating dietitian. Furthermore, contrary to previous studies in expert-novice reasoning, an intermediate effect was not evident. The results of the second study show a statistical difference in data driven (forward) reasoning between experts and novices. There was no statistical difference in hypothesis driven (backward) reasoning between groups. The reasoning networks of experts appear to reveal more concise explanations of important aspects related to dyslipidemia counseling. Reasoning patterns of the expert dietitians appear more coherent, although there was no statistical difference in the length or number of reasoning chains between groups. With previous research focusing on diagnostic reasoning rather than counseling, this finding may be a result of the nature of the underlying task. Conclusion: The studies presented here serve as a basis for future expert-novice research in the field of dietetics. The exploration of individual verbal protocols to identify characteristics of dietitians of various levels of expertise, can provide insight into the way knowledge is used and applied during diet counseling. Subsequent research can focus on randomized sample selection, with case scenarios as a constant, in order to obtain results that can be generalized to the greater dietitian population.

The Swedish health care sector must improve productivity in order to deal with anincreased demand from an aging population with limited resources. In the traditiondriven health care sector, transitioning towards becoming more data-driven has beenidentified as a potential solution. This explorative qualitative case study explores howindividual employees perceive this development at two Swedish hospitals. The resultscomplement theory by presenting propositions that explains drivers and barriers ofthe transition, but also the outcomes of it as perceived by the employees. The studyprimarily concludes that (1) a lack of trust in data and a tradition to base decisions ongut feelings in conjunction with low IT competence make hospital culture a majorobstacle for the transition, and that (2) it is important to understand the employees’perceived outcomes of becoming data-driven as it affects their support of thetransition. The results provide a platform for future research to build on and arevaluable for practitioners as they seek to utilize the drivers and mitigate the barriers.