Students who fail to achieve in school are frequently retained in grade to remediate their lack of satisfactory progress. In this study, elementary school principals, kindergarten, and first grade teachers were interviewed to explore their perceptions of the decision making processes used in recommending retention. The belief systems which underlie their reliance on retention as a remedial option were also examined. Three research questions were addressed: 1. What is the relationship between the written retention policy of a selected school district and the actual decision making process used by its schools? 2. What are the influences by district socio-economic level which impact the decision making process used in student retention? 3. What are the perceptions across district socioeconomic level of teachers and principals regarding the use of retention as an intervention for students? Some additional questions related to the three research questions were also explored in the study. The primary method of data collection consisted of interviews with nine participants. In addition, principals, kindergarten, and first grade teachers from 12 schools, representing three socio-economic levels, were surveyed. Data were integrated to develop a more complete narrative of retention practice as perceived by these practitioners. The results of this study indicate several factors influence retention decision making and practice: 1. expectations of other teachers 2. pressure of curriculum standards 3. the availability of alternatives 4. the perceived needs of students 5. the belief systems of teachers 6. knowledge of retention research. Recommendations are presented for encouraging practice more aligned with current research and to assist district policy makers in developing alternatives for retention. The research suggests that future study be conducted to further explore teacher belief systems underlying retention practice.

Thesis (LLM. (Development and management law)) --University of Limpopo, 2010 / This study probes the topical issue of prosecutorial independence in post 1994 South Africa in order to begin to determine how the new democratic constitutional dispensation has and should have affected the independence of our prosecutors. It also explores, albeit introductorily, the intersection of prosecutorial and judicial independence by suggesting that the much vaunted judicial independence in South Africa can prove mythical if prosecutorial independence is not vigorously and unflinchingly championed. The study also looks into what role accountability plays both as a pro and a con for prosecutorial independence within the parameters of the rule of law. Furthermore a comparative analysis of some fellow Commonwealth of Nations jurisprudences is embarked upon with a view to see what lessons can be learned and which prosecutorial approach tutorials are worth bunking. With a critical approach which is historical, contemporary and contextual, the study goes on to marry South African legal instruments, prosecutorial policies and other relevant literary insights to contemporary intersections ,interactions and frictions between law and politics in South Africa. The study seeks to begin to suggest a rule of law based but reasonably accountable prosecutorial approach for this country.

Medical choices can be life or death, and thus improving the accuracy of diagnostic decisions within a time constrained environment has a large potential for positive change. To that end, an adaptation of Dual Process Theory was developed to create a theoretical framework for medical decision making. In order to effectively measure this framework, a possible electroencephalographical link was investigated. During a complex medical diagnostic task, 52 participants were asked to diagnose what liver condition simulated patients had based on procedurally generated biometric data Feedback was provided during a learning phase until the pattern was learned. During the experimental phase, possible ranges for the biometric data were extended, allowing for increased diagnostic difficulty in some trials, thereby producing conflict for the participants. This difference between the control (Type 1) trials and the high conflict (Type 2) trials was measured using electroencephalography. It was predicted that an elevation in midfrontal theta power would be observed in high-conflict trials, which would provide a neurological correlate for Type 2 processing. This hypothesis was not verified, although several modifications to the experimental design were provided to inform future investigations. It is likely that an improved paradigm would be able to distinguish between the two processes, providing vital neurofeedback that could inform future medical students and emphasize effective learning to improve diagnostic outcomes. / Graduate

The theme of this dissertation is to develop robust statistical approaches for the high-dimensional observational data. The development of technology makes data sets more accessible than any other time in history. Abundant data leads to numerous appealing findings and at the same time, requires more thoughtful efforts. We are encountered many obstacles when dealing with high-dimensional data. Heterogeneity and complex interaction structure rule out the traditional mean regression method and expect a novel approach to circumvent the complexity and obtain significant conclusions. Missing data mechanism in high-dimensional data is complicated and is hard to manage with existing methods. This dissertation contains three parts to tackle these obstacles: (1) a tree-based method integrated with the domain knowledge to improve prediction accuracy; (2) a tree-based method with linear splits to accommodate the large-scale and highly correlated data set; (3) an integrative analysis method to reduce the dimension and impute the block-wise missing data simultaneously. In the first part of the dissertation, we propose a tree-based method called conditional quantile random forest (CQRF) to improve the screening and intervention of the onset of mentor disorder incorporating with rich and comprehensive electronic medical records (EMR). Our research is motivated by the REactions to Acute Care and Hospitalization (REACH) study, which is an ongoing prospective observational cohort study of the patient with symptoms of a suspected acute coronary syndrome (ACS). We aim to develop a robust and effective statistical prediction method. The proposed approach fully takes the population heterogeneity into account. We partition the sample space guided by quantile regression over the entire quantile process. The proposed CQRF can provide a more comprehensive and accurate prediction. We also provide theoretical justification for the estimate quantile process. In the second part of the dissertation, we apply the proposed CQRF to REACH data set. The predictive analysis derived by the proposed approach shows that for both entire samples and high-risk group, the proposed CQRF provides more accurate predictions compared with other existing and widely used methods. The variable importance scores give a promising result based on the proposed CQRF that the proposed importance scores identify two variables which have been proved to be critical features by the qualitative study. We also apply the proposed CQRF to Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study data set. We show that the proposed approach improves the personalized medicine recommendation compared with existing treatment recommendation method. We also conduct two simulation studies based on the two real data sets. Both simulation studies validate the consistent property of the estimated quantile process. In the second part, we also extend the proposed CQRF with univariate splits to linear splits to accommodate a large number of highly correlated variables. Gene-environment interaction is a widely concerned topic since the traits of complex disease is always difficult to understand, and we are eager to find interventions tailored to individual genetic variations. The proposed approach is applied to a Breast Cancer Family Registry (BCFR) study data set with body mass index (BMI) as the response variable, several nutrition intake factors, and genotype variables. We aim to figure out what kind of genetic variations affect the heterogeneous effect of the environmental factors on BMI. We devise a criterion which measures the relationship between the response variable and gene variants conditioning on the environmental factor to determine the optimal linear combination split. The variable importance score is also calculated by summing up the criterion across all splits in the random forest. We show in the results that top-ranked genes prioritized by the proposed importance scores make the effect of the environmental factors on BMI differently. In the third part, we introduce an integrative analysis approach called generalized integrative principal component analysis (GIPCA). The heterogeneous data types and the presence of block-wise missing data pose significant challenges to the integration of multi-source data and further statistical analyses. There is not literature can easily accommodate data of multiple types with block-wise missing structure. The proposed GIPCA is a low-rank method which conducts the dimension reduction and imputation of block-wise missing data simultaneously to data with multiple types. Both simulation study and real data analysis show that the proposed approach achieves good missing data imputation accuracy and identifies some meaningful signals.

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No description available.

This thesis explores the role and relationships of employee participation in decision-making (PDM) within the enterprise bargaining context. The advent of the enterprise bargaining to facilitate labour market restructuring has led to dramatic changes within Australian industrial relations, supposedly offering employees the opportunity to participate in changes to work practices, conditions of employment and rewards in return for employer gains in productivity (Niland, 1993). Productivity improvements have been achieved, but some researchers claim this has been at employees expense and that job satisfaction and affective commitment are declining as working hours increase, work intensifies, and job security diminishes. Employee PDM influencing more positive outcomes, such as improved productivity, satisfaction and commitment is appealing, but largely untested.Research data was gathered from the public, private and local government sectors to form two separate studies to test a model of PDM developed from the literature. The first Study analysed cross-sectional data to test the influence of PDM in relation to working conditions, work practices and rewards and outcomes of job satisfaction and affective commitment, while the second Study examined these relationships on an independent longitudinal matched sample. Analysis was conducted using Structural Equation Modelling with the EQS statistical package.Findings from both studies supported that higher levels of PDM correlate with higher levels of job satisfaction and affective commitment and Autonomy is the only significant mediator in the relationship PDM and affective commitment. Employees also perceived that increased task variety correlated with higher levels of PDM. Lower levels of PDM correlated with lower autonomy and perceptions of performance effectiveness. Although positive attitudes to PDM positively influence ++ / satisfaction and affective commitment outcomes, lowered perceived performance effectiveness and rewards compromise the gains achieved. These findings support the crucial role of employee participation in decision-making and sound a warning to practitioners in that increased demands for performance should not extend to role overload that reduces effectiveness, and must be matched with equitable rewards.

Existing studies provide a range of insights into the causes of womens low retirement incomes and emphasise the effect of low life-time incomes on womens access to economic resources in later life. Despite these insights, however, there is relatively little research on the roles played by motivations, social institutions and decision-making processes in determining womens capacity to save for retirement. In order to address some of these gaps in our understanding, this study aimed to broaden the range of theoretical approaches applied to economic studies of womens retirement savings strategies. Based on methodological perspectives informed by critical realism and feminist epistemology, the study utilised grounded research methods to collect and analyse qualitative data relevant to womens financial decisions and retirement plans. The data collection and analysis process are conceptually organised and integrated to propose a theoretical contribution that emphasises the links between social structures and specific decision-making processes that systematically contribute to low retirement savings for women. The studys findings are discussed with reference to existing economic literature that has not previously been utilised in studies of womens retirement incomes. The conclusions from this study suggest that there are significant features of womens decision-making contexts that contribute to ongoing under-saving to support women in later life.

Survey data are often subject to a number of measurement errors. The measurement error associated with a multinomial variable is called a misclassification error. In this dissertation we study such errors when the outcome is binary. It is known that ignoring such misclassification errors may affect the parameter estimates, see for example Hausman, Abrevaya and Scott-Morton (1998). However, previous studies showed that robust estimation of the parameters is achievable if we take misclassification into account. There are many attempts to do so in the literature and the major problem in implementing them is to avoid poor or fragile identifiability of the misclassification probabilities. Generally we restrict these parameters by imposing prior information on them. Such prior constraints on the parameters are simple to impose within a Bayesian framework. Hence we consider a Bayesian logistic regression model that takes into account the misclassification of the dependent variable. A very convenient way to implement such a Bayesian analysis is to estimate the hierarchical model using the WinBUGS software package developed by the MRC biostatistics group, Institute of Public Health, at Cambridge University. WinGUGS allows us to estimate the posterior distributions of all the parameters using relatively little programming and once the program is written it is trivial to change the link function, for example from logit to probit. If we wish to have more control over the sampling scheme or to deal with more complex models, then we propose a data augmentation approach using the Metropolis-Hastings algorithm within a Gibbs sampling framework. The sampling scheme can be made more efficient by using a one-step Newton-Raphson algorithm to form the Metropolis-Hastings proposal. Results from empirically analyzing real data and from the simulation studies suggest that if suitable priors are specified for the misclassification parameters and the regression parameters, then logistic regression allowing for misclassification results in better estimators than the estimators that do not take misclassification into account.

This thesis describes the development of a decision support tool for unplanned maintenance of aircraft at ramp time during airport operations. Ramp time is the time between an aircraft arrival and its next departure. Clearance of an aircraft for flight is controlled by aviation regulations. Therefore decisions regarding maintenance are taken by engineers who have to comply with the regulations that are governed outside the organizational structure of the airline. Unplanned maintenance also often disrupts the normal operational scheduling and leads to significant costs. Therefore, the decision support tool must include the relevant aviation regulations, be capable of rescheduling to minimise disruption and be able to optimise solutions based on cost. In this project an aircraft schedule is used to demonstrate the procedures. An assumed fleet of six airplanes fly between three cities. Consultation with aviation experts ensured the size of the fleet and operations are realistic. A regulation database was developed based on the Master Minimum Equipment List (MMEL) for the aircraft, and a computer programme was developed to provide different options that comply with the regulations and take into account scheduling disruption and costs. In certain cases the regulations allow an aircraft to fly with some components inoperable so long as backup systems can perform the tasks. It is possible then to postpone the maintenance until the aircraft arrives at a properly equipped airport, or until a longer scheduled stopover reduces the disruption to operations. To address the engineering aspects of the project, maintenance of a single component that appears in the MMEL for the chosen aircraft is considered. To plan maintenance following a failure, the cause of the failure needs to be identified. Only then can the resources and time required to repair the defect be defined. The programme validation has confirmed it is able to balance different aspects of decisions related to unplanned aircraft ramp maintenance. Although the programme is based on an assumed fleet operation, the structure of the programme will allow it to be applied to other fleet and route configurations.