Saudi Cultural Missions Theses & Dissertations
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Item Restricted Enhancing the quality and standard of research in Saudi Arabian universities: Selection and use of research methods at doctoral level: An investigative study on the use of research into statistical methods(University of glasgow, 2024) Abohiamid, Manal; McMahon, Margeryhis study investigated the selection and use of research methodologies at doctoral level, with particular emphasis on the use of statistical practices in research and with a focus on the Saudi Arabian context. There are misapplications in certain studies when analysing the statistical data, and some of these inaccuracies come from using improper management and suitable statistical methods at the analysis stage, contributing to misleading research conclusions. A key question was ‘How do academics and PhD students from Education departments in a selected university in Saudi Arabia and a university in Scotland/United Kingdom, from different educational backgrounds, view their readiness, selection, and utilization of statistical methods in PhD research?’. The accurate use of Statistics is critical in academic research, Statistics provide a methodical and objective approach to data analysis and interpretation, allowing researchers to make meaningful conclusions and uncover noteworthy patterns. Thus, a study acquires credibility and assures the validity of its findings by using accurate statistics, allowing policymakers and stakeholders to make educated decisions and perform targeted adjustments that enhance every aspect of society. This study examined PhD students' perceptions of their preparedness for statistical analysis, as well as their statistical and mathematical skills. Currently in Saudi Arabia, a programme of development: Vision 2030, is being implemented and so an aim of this study was to show why reforms are needed in Saudi Arabia's education system and why future university students should have sufficient Mathematical understanding to maintain the PhD researcher's basic knowledge base (Mathematics and Statistics). This was accomplished by sending a questionnaire to PhD students in SA and UK and conducting interviews with Statistics lecturers for postgraduate students in Saudi Arabia. The study found there is data analysis problems, such as inaccurate statistical technique application, a lack of pre-existing Mathematical expertise, wrong data processing, and incorrect result analysis. To increase the accuracy of statistical methods employed in PhD research, the study recommends that qualified statisticians, Statistics centres, and quality Mathematics and Statistics material be developed in the KSA. Furthermore, the research showed the significance of developing educational cadres in Statistics, developing the literacy pathway in schools in Mathematics, and making advanced Statistics courses necessary for postgraduate students in order to improve the quality and credibility of research undertaken, and the significance of students having a mathematical foundation in the Kingdom of Saudi Arabia22 0Item Restricted Spatio-temporal modelling of localised health inequalities in Glasgow(University of Glasgow, 2024-04-25) Ismail, Riham; Anderson, Craig; Dean, NemaThe main aim of this thesis is to develop a statistical clustering methodology for disease mapping. Disease mapping studies aim to understand a disease’s spatial pattern and identify areas with low or high disease risk. These studies play an essential role in epidemiology and public health by providing information on how disease exposures differ geographically and assisting in allocating resources for prevention or intervention strategies. Commonly, such studies are based on areal data, which partitions the study region into a set of non-overlapping sub-regions. The standard clustering techniques for grouping data ignore the spatial dependencies between nearby areas in areal data. Therefore, the first model proposed in this thesis incorporates the spatial information within a Poisson finite mixture model for clustering areal data. The disease data are usually available over multiple timepoints, providing a valuable opportunity to carry out examinations of temporal trends and patterns. Thus, the two other methods proposed in this thesis are both forms of spatio-temporal generalised additive mixed model designed to capture trends and variations over both temporal and spatial dimensions. The first of these approaches estimates the disease risk over time and then identifies the high and low-risk clusters of spatio-temporal disease risk data. The final model in this thesis considers the potential clustering structure in spatial data over time and thereafter estimates the disease risk. These models are each used to assess the spatial and temporal trends of COVID-19 cases in the Greater Glasgow and Clyde Health Board areas. A key finding was that areas in different clusters often exhibited similar temporal trends but somewhat different means. The study also clearly identified several waves of COVID-19 cases during the study period, most notably an increase in COVID-19 cases in September 2021, potentially influenced by the UK’s rules in managing the COVID-19 epidemic.19 0Item Restricted Dose Mental Health Have an Impact on The Number of minutes Exercising?(University of Strathclyde, 2023-09-07) Albogamy, Nourah; Stewart, RyanThis study aimed to explore the influence of physical activity on mental health, utilizing the Scottish Health Survey 2021 data to investigate the association between exercise duration and mental well-being, alongside various confounding factors. Employing Poisson regression initially, the research encountered analytical challenges due to violation of model assumptions, prompting the use of Negative Binomial Regression and Zero-Inflated Negative Binomial Regression models for a more accurate assessment. The findings illustrate a significant positive relationship between Moderate to Vigorous Physical Activity (MVPA) and mental health, as measured by the Warwick-Edinburgh Mental Well-being Scale (WEMWBS), highlighting the benefits of increased physical activity. The study further identifies age, gender, alcohol consumption, and education level as important determinants of physical activity frequency, each contributing uniquely to the complex dynamics between physical exercise and mental health. Notably, the analysis reveals age-related declines in exercise, gender disparities favoring males in physical activity levels, intricate interactions between alcohol use and exercise, and an unexpected pattern of higher MVPA among those with lower educational attainment. These insights emphasize the need for targeted, age-appropriate exercise programs and challenge prevailing assumptions about the socio-demographic drivers of physical activity. The paper also suggests avenues for future research, including the exploration of interdependencies among the identified variables, to deepen understanding of the multifaceted relationship between physical activity and mental health.43 0Item Restricted Functional Linear Regression: Linear Hypothesis Testing With Functional Response(Saudi Digital, 2023-06-12) Alsaeed, Rasha; Yu, DengdengHypothesis testing is a crucial aspect of functional data analysis, allowing researchers to make inferential decisions based on samples of functional data. The inherent infinite dimensionality of functional data makes conventional hypothesis testing methods, such as Hotelling’s T2, difficult to apply due to the singularity of the sample covariance matrix. To address this issue, a common practice is to project functional data into a lower-dimensional space before conducting hypothesis tests. However, the choice of projection space can impact the validity and power of the tests, as the projected hypothesis problem may not be equivalent to the original problem. In this thesis, we propose a novel hypothesis testing procedure that establishes an optimal projection space where the original and projected hypothesis problems are equivalent and achieves the best power. The theoretical properties of the proposed test are systematically investigated, and a method for constructing the optimal projection space is provided. We demonstrate in this thesis that classical functional mean testing problems, including one-sample, two-sample, and multisample cases, and predictor significance testing can be reduced to special cases of the proposed method. To assess the performance of our proposed test, we conduct extensive simulations and analyze real data. Our results show the superiority of the proposed projection test for functional linear hypotheses in the function-on-scalar regression linear model. The findings of this thesis contribute to the advancement of hypothesis testing in functional data analysis by addressing the challenges posed by the infinite nature of functional data and providing a novel approach to establish an optimal projection space for improved hypothesis testing performance.23 0