<p dir="ltr">Families experiencing homelessness and housing instability (FEH/HI) face myriad challenges, placing their children at risk for adverse outcomes. Research typically adopts a deficit-based approach to meet immediate needs, but this may limit understanding of the children’s experiences. Recognizing children’s strengths is important for improving their health, development, and support. Resilience and family resilience are concepts that draw on a strengths-based approach. However, there is a need for more research to identify the strengths, such as resilience, among families and their children who are not stably housed. The present research seeks to address the gaps in the literature by examining the resilience perceptions and experiences of adolescents in FEH/HI, as much of the existing research focuses on the adult perspectives, within a family resilience framework through multiple studies. Study 1 (Chapter 2) systematically reviewed existing research on resilience and family resilience within FEH/HI. An analysis of 27 studies identified resilience-related factors across individual, interpersonal, and community domains. Study 2 (Chapter 3) integrated a strengths- and deficit-based approach to explore the challenges and strengths of children in FEH/HI from the perspectives of parents/caregivers and service providers. Semi-structured interviews with 17 parents/caregivers and 15 service providers identified challenges and strengths at the individual, interpersonal, and system levels. Study 3 (Chapter 4) investigated how adolescents within FEH/HI experience and make meaning of family resilience in their day-to-day lives using photo-elicitation (PE) and Froma Walsh’s family resilience framework. Four adolescents participated and took photographs depicting family resilience within their families. Together, findings from these studies provide insights into the strengths and resilience within FEH/HI. Moreover, they emphasize the need for strengths-based approaches in research and practice to support the health, development, and wellbeing of children and adolescents in FEH/HI.</p>

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

Purpose: Social determinants of health (SDoH) and rurality have both been shown to contribute to severe maternal morbidity (SMM). Appalachian communities often embody this compounded risk, but regional SMM is under-explored. This study’s purpose is to explore SMM in rural areas of Appalachian states. Aims: There are two specific aims. 1) Describe the prevalence of rural SMM in Appalachian states. 2) Investigate the relationship between SMM and SDoH, specifically income, education, and care access. Methods: An IRB exempt, descriptive correlational study was conducted. Birth data (2018-2022) were extracted from the CDC WONDER database for 12 Appalachian states. Demographic, income, and education data were obtained from the U.S. Census. Access was measured by March of Dimes (MoD) maternity care categorizations. Descriptive statistics and Pearson’s correlations were conducted in IBM SPSS. Results: Rural SMM rates correlated with poverty (r =.803, p Conclusions: This study describes rural SMM in Appalachian states, showing correlation with poverty, education, and maternity care access. Limited access to care is correlated with a higher SMM burden for rural areas. This points to the need for further exploration into rural SMM, and the interplay of SDoH and geography in relation to maternal health.

Many beginner educators leave the profession within the first three years following their appointment. The security of educators, and especially that of the beginner educator is threatened from many angles. Beginner educators experience this threat very intensely and this weakened feeling of security they experience, has important implications for their role as educators. There are, however, many legal determinants that protect educator security and oppose each threat directly. This research falls within the field of Education Law and the security of beginner-educators is studied from this angle. The study focuses on the protection of security as offered by law determinants and the real experience of threats by beginner educators. In the research, law determinants such as the Constitution, education law, labour law and case law, as protectors of security, were studied. Using a qualitative study the experience and perceptions of a selection of participants was analysed. The participants, from different types of schools in a certain geographical area, were identified. During the analysis two aspects emerged that influence security. The first aspect is the beginner educator‟s lack of knowledge of the mechanisms that protect security and the second aspect was beginner educators‟ real experience of threats. Beginner educators‟ lack of security is the result of their lack of knowledge of the mechanisms that protect security. The findings of this research propose that more emphasis is placed on the legal aspects concerning the protection of security of educators during their training and that beginner educators are empowered to face threats of security and overcome it. / MEd (Education Law), North-West University, Potchefstroom Campus, 2015

Many beginner educators leave the profession within the first three years following their appointment. The security of educators, and especially that of the beginner educator is threatened from many angles. Beginner educators experience this threat very intensely and this weakened feeling of security they experience, has important implications for their role as educators. There are, however, many legal determinants that protect educator security and oppose each threat directly. This research falls within the field of Education Law and the security of beginner-educators is studied from this angle. The study focuses on the protection of security as offered by law determinants and the real experience of threats by beginner educators. In the research, law determinants such as the Constitution, education law, labour law and case law, as protectors of security, were studied. Using a qualitative study the experience and perceptions of a selection of participants was analysed. The participants, from different types of schools in a certain geographical area, were identified. During the analysis two aspects emerged that influence security. The first aspect is the beginner educator‟s lack of knowledge of the mechanisms that protect security and the second aspect was beginner educators‟ real experience of threats. Beginner educators‟ lack of security is the result of their lack of knowledge of the mechanisms that protect security. The findings of this research propose that more emphasis is placed on the legal aspects concerning the protection of security of educators during their training and that beginner educators are empowered to face threats of security and overcome it. / MEd (Education Law), North-West University, Potchefstroom Campus, 2015

The purpose of this research was to investigate the relationship between Education Acts and learner dropout at public schools within the South-Central education region of Botswana. Policy-related dropout Theories of Social Class and the hidden curriculum of work, as well as the Inclusive Education Policy were selected as suitable framework-settings for investigating the research problem. A comprehensive review of the literature revealed that the strategies used to implement the Basic Education Act, the Examinations Act, and Policies on Inclusive Education in Botswana diverge from their set stipulations and the current international trends in this regard. The research design used was a mixed-methods approach. Mixed paradigms of the positivists’ and the constructivists’ beliefs were used to conduct a concurrent empirical investigation. The reliability coefficient of the questionnaire instrument (non-demographic variables 1-26) was .985 (close to 1). All the measuring tools were pilot-tested. The sampling technique was stratified for the questionnaire, and was comprehensive for the qualitative instruments. Ethical issues were observed during the course of the study. The scores on the questionnaire showed that 68% of the 75 teacher respondents perceived that the improper implementation of the above-named Acts contributed to learner drop-out. The content analysis transcripts further indicated that 66% of the 28 interviewees linked learner drop-out to the improper implementation of the Acts. Additionally, 84% of the Biology teachers linked the teaching strategies being used to policy decisions. The interpretation of this study has to take note of the limitation discussed in the report. The conclusion drawn from the foregoing research findings is that the teaching-learning process in the public schools is defective in relation to its relevance to the learners, and the education goal. The study therefore recommended dropout tracking strategies by means of a greater synchrony between all the departments of the Ministry of Education Skills and Development (MOESD) as well as further comprehensive research to improve education practice towards curbing learner dropout. / Educational Studies / D. Ed. (Comparative Education)

Farmers generally produce multiple crops while selectively adopting modern technologies to meet various needs. The main aim of this study is, therefore, to identify the range of socio-economic factors influencing the adoption of modern agricultural technology in multiple food crops and the corresponding impacts on productivity and food availability at the farm-level in South-eastern Nigeria. In this study, three major food crops (i.e., rice, yam and cassava) and two elements of modern technologies (i.e., HYV seeds and inorganic fertilizers) are considered. The hypotheses of the study are that inverse farm size – technology adoption, size – productivity, size- profitability and size – food availability relationships exist in Nigerian agriculture. The research is based on an in-depth farm-survey of 400 farmers from two states (251 from Ebonyi and 149 from Anambra states) of South-eastern Nigeria. Data has also been derived from surveys and interviews of ADP Program Managers and NGOs. A range of qualitative and quantitative methods including inferential statistics, bivariate probit model and regression analysis were used in order to achieve the specific objectives and test hypotheses. The results show that sample respondents are dominated by small scale farmers (81% of total) owning land less than 1 ha. The average farm size is small estimated at 1.27 ha. Farmers grow multiple crops instead of a single crop, i.e., 68% of the surveyed farmers grew at least two food crops. The level of modern technology adoption is low and mixed and farmers selectively adopt components of technologies as expected and use far less than recommended dose of fertilizers in crops. Only 29% of farmers adopted both HYV seeds and fertilizers as a package. The study clearly demonstrates that inverse farm size – technology adoption, farm size – productivity, and farm size – food availability relationships exist in agriculture in this region of Nigeria; but not inverse farm size – profitability. The bivariate probit model diagnostic reveals that the decision to adopt modern technologies are significantly correlated, implying that univariate analysis of such decisions are biased, thereby, justifying use of the bivariate approach. Overall, the most dominant determinants are the positive influence of farming experience and the negative influence of remoteness of extension services on modern technology adoption. The per capita per day level of mean food produced is 12322.74 calories from one ha of land and food available for consumption is 4693.34 calories which is higher than the daily requirement of 2000 calories. Yam is produced mainly for sale while cassava is produced for consumption. Regression analysis shows that farm size and share of cassava in the total crop portfolio significantly increases food availability. A host of constraints are affecting Nigerian agriculture, which includes lack of extension agents, credit facilities, farm inputs, irrigation, and value addition and corruption, lack of support for ADP staff and ineffective government policies. Policy implications include investment in extension credit services and other infrastructure (e.g., irrigation, ADP staff), training of small farmers in business skills, promotion of modern technology, as a package as well as special projects targeted for cassava (e.g., Cassava Plus project) in order to boost modern technology adoption in food crops, as well as improving productivity, profitability and food availability at the farm-level in Nigeria.

Thesis (MBA (Business Management))--University of Stellenbosch, 2007. / ENGLISH SUMMARY: With diminishing sources of capital over the past two decades, developing countries have increasingly regarded the flow of foreign direct investment (FDI) as their main source of capital for development. In response to this, countries have also liberalised their policies, making their investment climate friendlier to FDI. This has been accompanied by increased competition amongst such countries to attract FDI, resulting in higher investment incentive packages offered by host governments to potential investors. This study aims to analyse the significance of host country incentives in attracting FDI, and consider whether or not these generous incentives benefit only the foreign investors, without any positive spillovers and linkages being created within the domestic economy, as this is usually given as the strongest motivation for offering these generous incentives. The research has used case studies of three diverse countries to compare and contrast their approach to incentive policies: • Lesotho, where no incentives are offered specifically to foreign investors • Namibia, with its export processing zones (EPZ) and • South Africa, which offers industry-specific incentives. The analysis is undertaken on aggregate FDI inflows to these three countries for the period 1998 to 2004. These are then compared to other selected countries from Africa. A further analysis of relative performance of FDI to gross fixed capital formation and GDP has also been undertaken for the same period. A separate analysis of the flow of FDI to Namibia four years before and after the introduction of the EPZ regime is also undertaken, and the results are compared with those of Lesotho and South Africa during the same period. It can be concluded that fiscal incentives have not had a significant impact on aggregate FDI inflow into Namibia, but that industry specific incentives such as those used in South Africa have had a much better impact. The results also show that there has been little evidence that FDI has created positive spillovers and linkages in these economies and therefore that the use of generous incentives may have benefited foreign investors more and accrued costs for the host governments. The study has also shown that, despite the absence of essential determinants of FDI in countries such as Angola i.e. adequate infrastructure, economic stability and good governance, FDI in Africa has been mainly resource seeking; concentrated on resource and in particular petroleum rich countries such as Nigeria, Angola and Equatorial Guinea. This form of FDI creates little or no linkages with the rest of the economy and therefore contributes which means that little contribution is being made to the broader development of the economy of the continent.

As the globalisation process accelerates there is a growing need for individual countries to understand the bases for effective performance in international trade. Because it makes up such a large share of world trade, it is especially important to understand what determines effectiveness in exporting. Despite much empirical research, especially over recent decades, the state of knowledge on this topic remains fragmented, unclear and unsatisfactory. The motivation for the present study was therefore twofold: dissatisfaction with the present state of knowledge in this vital area and the importance to the UK economy of improving its export performance in a world of increasing competition. Its aim was to contribute to the resolution of both. In addition to finding what appeared to be quite serious methodological problems in a group of earlier studies, our review of the literature indicated that the best prospects for identifying the determinants of effective exporting were to be found, not at national or sectoral level but at that of the individual firm. Accordingly, an empirical survey research project was developed. To minimise unquantifiable inter-sectoral variability, it was focused on a single sector of industry. For a range of reasons, including the limited amount of information available about its current export activity and prospects, the UK knitted apparel industry was chosen. Special care having been taken to assemble the fullest possible sampling frame and to develop a suitable instrument (which included an export performance model), a mail survey in the form of a stratified random sample of exporting UK manufacturers of knitted apparel was carried through from late 2000. Persistent follow-up by mail and telephone generated a response rate of 70 per cent, comprising close to half of the sampling frame, that was representative of all company size bands, levels of exporting and products. The overall quality of the responses was good; tests of non-response did not find any indications of non-response bias. Data analysis, designed to test thoroughly our 10 export-determinants hypotheses, relied primarily on Pearsonian correlation at the bivariate level then sequentially on Multiple Regression Analysis, Canonical Correlation Analysis and Partial Least Squares. A perhaps slightly novel aspect of the research was that it was not solely cross-sectional in format; a longitudinal element was provided by drawing on the researcher's earlier surveys ; and a panel element by following-up, in 2007, the main 2000 field survey. Where possible, these data were drawn upon in the analysis and interpretation. There did not appear to be any conflict between the three multivariate techniques employed and indeed their findings were not dissimilar. The outcome of the data analysis was to uphold, to varying degrees, most of our hypotheses about the determinants of effective or successful exporting. Those that did not find support were three: firm size, product adaptation, and price determination method. Most strongly supported as determinants were promotional intensity, serving many markets and visits to trade fairs/exhibitions; others which were statistically significant, included management commitment, special staff skills and the use of Commission Agents. While the conclusions must remain a bit tentative they are encouraging.

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates. (authors' abstract) / Series: Department of Economics Working Paper Series