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Determining the quality of natural language applications is one of the most important aspects of technology development. There has, however, been very little work done on establishing how well the methods and measures represent the quality of the technology and how reliable the evaluation results presented in most research are. This study presents a new stepwise evaluation reliability methodology that provides a step-by-step framework for creating predictive models of evaluation metric reliability that take into account inherent evaluation variables. These models can then be used to predict how reliable a particular evaluation will be prior to doing an evaluation, based on the variables that are present in the evaluation data. This allows evaluators to predict the reliability of the evaluation prior to doing the evaluation and adjusting the evaluation data to ensure reliable results. Furthermore, this permits researchers to compare results when the same evaluation data is not available. The new methodology is firstly applied to a well-defined technology, namely spelling checkers, with a detailed discussion of the evaluation techniques and statistical procedures required to accurately model an evaluation. The spelling checker evaluations are investigated in more detail to show how individual variables affect the evaluation results. Finally, a predictive regression model for each of the spelling checker evaluations is created and validated to verify the accuracy of its predictive capability. After performing the in-depth analysis and application of the stepwise evaluation reliability methodology on spelling checkers, the methodology is applied to two more technologies, namely part of speech tagging and named entity recognition. These validation procedures are applied across multiple languages, specifically Dutch, English, Spanish and Iberian Portuguese. Performing these additional evaluations shows that the methodology is applicable to a broader set of technologies across multiple languages. / Thesis (PhD (Linguistics and Literary Theory))--North-West University, Potchefstroom Campus, 2013.

Determining the quality of natural language applications is one of the most important aspects of technology development. There has, however, been very little work done on establishing how well the methods and measures represent the quality of the technology and how reliable the evaluation results presented in most research are. This study presents a new stepwise evaluation reliability methodology that provides a step-by-step framework for creating predictive models of evaluation metric reliability that take into account inherent evaluation variables. These models can then be used to predict how reliable a particular evaluation will be prior to doing an evaluation, based on the variables that are present in the evaluation data. This allows evaluators to predict the reliability of the evaluation prior to doing the evaluation and adjusting the evaluation data to ensure reliable results. Furthermore, this permits researchers to compare results when the same evaluation data is not available. The new methodology is firstly applied to a well-defined technology, namely spelling checkers, with a detailed discussion of the evaluation techniques and statistical procedures required to accurately model an evaluation. The spelling checker evaluations are investigated in more detail to show how individual variables affect the evaluation results. Finally, a predictive regression model for each of the spelling checker evaluations is created and validated to verify the accuracy of its predictive capability. After performing the in-depth analysis and application of the stepwise evaluation reliability methodology on spelling checkers, the methodology is applied to two more technologies, namely part of speech tagging and named entity recognition. These validation procedures are applied across multiple languages, specifically Dutch, English, Spanish and Iberian Portuguese. Performing these additional evaluations shows that the methodology is applicable to a broader set of technologies across multiple languages. / Thesis (PhD (Linguistics and Literary Theory))--North-West University, Potchefstroom Campus, 2013.

Innovativeness is one of the fundamental instruments of growth strategies that provide companies with a competitive edge. Only a few recent studies have examined marketing innovation and the factors that might encourage its adoption. This study investigates the factors that motivate marketing innovation and examines whether the occurrence of marketing innovation varies across industry sectors. This study uses data from surveys and a nationwide census conducted by Statistics Canada. They include: the Survey of Innovation and Business Strategies (SIBS) 2009, the Survey of Innovation and Business Strategies (SIBS) 2012, the Business Registry (BR) and the General Index of Financial Information (GIFI). Multilevel (random-intercept) logistic regression modelling is employed. The results show that if a firm has a strategic focus on new marketing practices, maintains marketing within its enterprise, acquires or expands marketing capacity, has competitor and customer orientations, and adopts advanced technology then it is more likely to carry out marketing innovation. However, breadth of long-term strategic objectives and competitive intensity do not have significant impacts on marketing innovation. In addition, product innovation and organizational innovation occur simultaneously with marketing innovation, but process innovation may not. Lastly, the occurrence of marketing innovation is found to vary across industry sectors. The theoretical and empirical implications of the results are discussed within this study.

Fasciolosis, caused by the trematode parasite Fasciola hepatica, is a multi-host parasitic disease affecting many countries worldwide. It is a well-recognized clinically and economically important disease of food producing animals such as cattle and sheep. In the UK, the incidence and distribution of fasciolosis has been increasing in the last decade while the timing of acute disease is becoming more variable and the season suitable for parasite development outside the mammalian host has been extended. Meanwhile control is proving increasingly difficult due to changing weather conditions, increased animal movements and developing anthelmintic resistance. Forecasting models have been around for a long time to aid health planning related to fasciolosis control, but studies identifying management related risk factors are limited. Moreover, the lack of information on the accuracy of meat inspection and available liver fluke diagnostic tests hinders effective monitoring of disease prevalence and treatment. So far, the evaluation of tests available for the diagnosis of the infection in cattle has mainly been carried out using gold standard approaches or under experimental settings, the limitations of which are well known. In cattle, the infection mainly manifests as a sub-clinical disease, resulting in indirect production losses, which are difficult to estimate. The lack of obvious clinical signs results in these losses commonly being attributed to other causes such as poor weather conditions or bad quality forage. This further undermines establishment of appropriate control strategies, as it is difficult to convince farmers to treat without demonstrating clear economic losses of sub-clinical disease. This project explores the value of slaughterhouse data in understanding the changing epidemiology of fasciolosis, identifying sustainable control measures and estimating the effect of infection on production parameters using data collected at one of the largest cattle and sheep abattoirs in Scotland. Data used in this study include; a) abattoir data routinely collected during 2013 and 2014, b) data collected during 3 periods of abattoir based sampling, c) data collected through administration of a management questionnaire and d) climatic and environmental data from various online sources. A Bayesian extension of the Hui Walter no gold standard model was used to estimate the diagnostic sensitivity and specificity of five diagnostic tests for fasciolosis in cattle, which were applied on 619 samples collected from the abattoir during three sampling periods; summer 2013, winter 2014 and autumn 2014. The results provided novel information on the performance of these tests in a naturally infected cattle population at different times of the year. Meat inspection was estimated to have a sensitivity of 0.68 (95% BCI 0.61-0.75) and a specificity of 0.88 (95% BCI 0.85-0.91). Accurate estimates of sensitivity and specificity will allow for routine abattoir liver inspection to be used as a tool for monitoring the epidemiology of F. hepatica as well as evaluating herd health planning. Linear regression modelling was used to estimate the delay in reaching slaughter weight in beef cattle infected with F. hepatica, accounting for other important factors such as weight, age, sex, breed and farm as a random effect. The model estimated that cattle classified as having fluke based on routine liver inspection had on average 10 (95% CI 9-12) days greater slaughter age, assuming an average carcass weight of 345 kg. Furthermore, estimates from a second model indicated that the increase in age at slaughter was more severe for higher fibrosis scores. More precisely, the increase in slaughter age was 34 (95% CI 11-57) days for fibrosis score of 1, 93 (95% CI 57-128) days for fibrosis score 2 and 78 (95% CI 30-125) days for fibrosis score 3. Similarly, in a third model comparing different burden categories with animals with no fluke burden, there was a 31 (95% CI 7-56) days increase in slaughter age for animals with 1 to 10 parasites and 77 (95% CI 32-124) days increase in animals with more than 10 parasites found in their livers. Lastly, a multi-variable mixed effects logistic regression model was built to estimate the association between climate, environmental, management and animal specific factors and the risk of an animal being infected by F. hepatica. Multiple imputation methodology was employed to deal with missing data arising from skipped questions in the questionnaire. Results of the regression model confirmed the importance of temperature, rainfall and cattle movements in increasing the risk for fasciolosis, while it indicated that the presence of deer can increase the risk of infection and that male cattle have a reduced risk of infection. Overall, this project has used slaughterhouse data to fill important knowledge gaps regarding F. hepatica infection in cattle. It has provided valuable information on the accuracy of routine abattoir meat inspection, as well as other diagnostic tests. It has also provided estimates of the effect of infection on the time cattle take to reach slaughter weight at different levels of infection and identified relevant risk factors related to the infection. In conclusion, knowledge of the effect of infection on slaughter age, as well as regional risk factors for F. hepatica infection, along with an improved use of abattoir inspection results in the evaluation of treatment strategies, can provide farmers and veterinarians with better incentives and tools to improve their herd health strategies and in the longer term help reduce the incidence of liver fluke in cattle.

This thesis contributes practical and conceptual tools for discovering and understanding the variation of quantitative patterns in social and political survey data. It uses regression modelling as an exploratory method with a focus on deep rather than wide model specifications, i.e. on interaction terms rather than control variables. Our main research question is how can we learn from survey data with an exploratory approach of regression modelling. We also seek to answer two more specific questions, what sort of quantitative variations should an exploratory approach seek to model, and how do we deal with statistical uncertainty within an exploratory approach. Our work shows how to use regression modelling for exploratory purposes by interpreting the results descriptively, and connecting these summaries to theory through an act of interpretation. Using data from the Partirep electoral survey of the 2014 elections in Belgium, we illustrate how the emphasis on group variations and interactions has both empirical and theoretical value. We propose to summarise the results of exploratory modelling in a notebook containing a series of increasingly disaggregated prediction graphs. These notebooks help researchers to increase their domain numeracy, i.e. develop a quantitative understanding of the patterns in the data. Regarding statistical uncertainty, we mitigate the risks of modelling sampling noise by using standard errors of binned averages as precision hints that serve as an indication of excessive disaggregation. We also lay out the path for regularising the estimates of the final results with Bayesian models by exploring methods of including the sampling weights in these models. / Doctorat en Sciences politiques et sociales / info:eu-repo/semantics/nonPublished

Thesis (DPhil (Environmental Health))--Cape Peninsula University of Technology, 2016. / Informal caregivers play an important role in the well-being of dependent members in a household. The burdens of these caregivers are multiple and pervasive and may contribute to mental health epidemiology as a result of worry, grief, anxiety and stress. The literature review revealed that studies in caregiving and its various facets began from a pragmatically applied interest rather than from theoretical and intellectual curiosity. The majority of the research on caregiver burden involves meta-analysis of qualitative studies with little quantitative research. Also, many of these studies are concentrated on caregivers of chronically ill patients, such as those suffering from dementia, Alzheimer’s disease, and heart disease, without much attention to care burden resulting from caring for those who are not necessarily ill. Similar studies by other researchers did not consider the impact of the physical health of the care recipients or the environmental factors that are critical in the study of female caregiver burden in low-income settings. In addition, existing studies did not adequately evaluate the many potential factors that may vary and influence the lives of the caregivers, especially in a single, comprehensive model. This study attempted to provide a more complete picture of these relationships in low-income and culturally diverse settings. The study population consisted of black/African and coloured populations living in subsidised or low-cost housing settlements. In each of the two different cultural communities, 100 black/African and 100 coloured female caregivers were selected through a systematic random sampling procedure. In addition, data were also collected from caregivers in Tamale, Ghana to assess differences between the socio-demographic profiles of the caregivers in Cape Town, South Africa and Tamale, Ghana. The theoretical paradigm used in this study is the Stress Process Model by Pearlin et al., (1990). The study instrument assessed caregiver burden with both objective and subjective measures through the use of a fully structured questionnaire. The information that was collected according to the constructs of the Stress Process Model included personal and role strains and incorporated the physical health of the care recipients and environmental factors such as kitchen and toilet hygiene. Inclusion criteria for the respondents in both Cape Town and Tamale were the principal female caregivers who were present, willing, and able to give informed consent. The Statistical Package for Social Sciences (SPSS Version 22) was used for the analyses. The Chi-square test was used to assess the relationships between environmental health, the socio-demographics of the female caregivers and the health status of the care recipients. The hierarchical regression analysis in the form of a General Linear Model was used to model caregiving burden. iv The results showed that the majority of the female caregivers were in the age group 40 – 49 years and in both Cape Town and Tamale, a large proportion was in the low-income group. Also, the majority of the informal caregivers in the two samples were in care tasks as a full-time job, providing more than 40 hours of care per week. Regarding the length of time in caregiving, a large proportion of the caregivers in both Cape Town and Tamale had been in the care role for more than three years preceding the survey, and almost all the caregivers in the Cape Town sample (98.4%) did not use any form of caregiving programme to ameliorate the negative effects of caregiving. Further, the results showed statistically significant relationships between the socio-demographic characteristics of female caregivers (age, education, population group and income status) and the diarrhoea status of the care recipients. Also, a significant relationship was shown between environmental health variables of the home (kitchen hygiene and toilet hygiene) and the physical health of the care recipients. The major predictors of female caregiver burden in the samples were the physical health of the care recipients and access to social grants. On the basis of the analyses, it was recommended that the government should recognise the importance of the physical health of the care recipients and increase the amounts of social grants to the caregivers since this could improve the circumstances of both the caregivers and the care recipients. In addition, this could aid in improving the standard of living of caregivers in these households. Future research in similar settings should disaggregate the data to compare the burden of caring for caregivers of physically strong care recipients with physically ill care recipients.

The banking industry in India has undergone many phases in its history; evolving from a regulated, decentralised system in the early 1800’s, to a regulated, centralised system during British rule, to a nationalised system following India’s independence, and finally a combination of a nationalised and private system adopting global standards as it currently stands. This study has two main aims. Firstly, it will assess the relationship between the firm-specific determinants of capital structure, based on the prevailing literature, and the capital structure of public and private sector banks in India. Secondly, it will determine whether there is a difference in the firm-specific factors that contribute to the determination of the capital structure of public sector banks and private sector banks. This study adopts quantitative methods, similar to previous studies on the relationship between capital structure and its firm-specific determinants. The dependent variable, being total leverage, is regressed against multiple independent variables, being profitability, growth, firm size and credit risk (hereinafter referred to as “risk” unless otherwise indicated) in a multivariate linear regression model. This study adds to the current literature by applying the same firm-specific independent variables to the case of private and public sector banks and then to evaluate and compare the similarities and differences between the regression outputs. The results show that for private sector banks, all independent variables are statistically significant in explaining total leverage, where all the independent variables conform to the current literature on capital structure – profitability (-), firm size (-), growth (+) and credit risk (-). Conversely, for public sector banks, all independent variables were considered to be statistically significant, except for credit risk – profitability (-), firm size (+) and growth (+). These results imply that credit risk is not an important determination in a nationalised banks’ capital structure; thus, providing evidence for the moral hazard theory of public sector banks.

The objectives of the research study are to review existing subnational credit rating methodologies and their applicability in the South African context, to develop the quantitative parts of credit rating methodologies for two provincial departments (Department of Health and Department of Education) that best predict future payment behaviour, to test the appropriateness of the proposed methodologies and to construct the datasets needed. The literature study includes background information regarding the uniqueness of South Africa’s provinces and credit rating methodologies in general. This is followed by information on subnational credit rating methodologies, including a review of existing subnational credit rating methodologies and an assessment of the applicability of the information provided in the South African context. Lastly, the applicable laws and regulations within the South African regulatory framework are provided. The knowledge gained from the literature study is applied to the data that have been collected to predict the two departments’ future payment behaviour. Linear regression modelling is used to identify the factors that best predict future payment behaviour and to assign weights to the identified factors in a scientific manner. The resulting payment behaviour models can be viewed as the quantitative part of the credit ratings. This is followed by a discussion on further investigations to improve the models. The developed models (both the simple and the advanced models) are tested with regard to prediction accuracies using RAG (Red, Amber or Green) statuses. This is followed by recommendations regarding future model usage that conclude that the department-specific models outperform the generic models in terms of prediction accuracies. / PhD (Risk analysis), North-West University, Potchefstroom Campus, 2015

The objectives of the research study are to review existing subnational credit rating methodologies and their applicability in the South African context, to develop the quantitative parts of credit rating methodologies for two provincial departments (Department of Health and Department of Education) that best predict future payment behaviour, to test the appropriateness of the proposed methodologies and to construct the datasets needed. The literature study includes background information regarding the uniqueness of South Africa’s provinces and credit rating methodologies in general. This is followed by information on subnational credit rating methodologies, including a review of existing subnational credit rating methodologies and an assessment of the applicability of the information provided in the South African context. Lastly, the applicable laws and regulations within the South African regulatory framework are provided. The knowledge gained from the literature study is applied to the data that have been collected to predict the two departments’ future payment behaviour. Linear regression modelling is used to identify the factors that best predict future payment behaviour and to assign weights to the identified factors in a scientific manner. The resulting payment behaviour models can be viewed as the quantitative part of the credit ratings. This is followed by a discussion on further investigations to improve the models. The developed models (both the simple and the advanced models) are tested with regard to prediction accuracies using RAG (Red, Amber or Green) statuses. This is followed by recommendations regarding future model usage that conclude that the department-specific models outperform the generic models in terms of prediction accuracies. / PhD (Risk analysis), North-West University, Potchefstroom Campus, 2015