Doctor of Philosophy / Department of Special Education, Counseling and Student Affairs / Doris W. Carroll / This study examined factors relates to student ratings of instruction and student levels of motivation. Data came from archival data of 386,195 classes of faculty and students who completed the Faculty Information Form (FIF), completed by the instructor, and the Student Ratings Diagnostic Form (SRDF) completed by the student from the Individual Development and Educational Assessment (IDEA) Center Student Ratings system. Descriptive statistics, correlation studies, analysis of variance (ANOVA), and pairwise comparisons were used to test the research hypotheses. Despite significant differences among student ratings of instruction and student motivation by course type, discipline, and student type, the amount of unknown variability in student ratings of instruction and student motivation is still very large. The findings from the study provide higher education institutions with information about differences between student ratings of instruction by institution type, course level, discipline, and course type as well as the impact of student motivation on student ratings of instruction.

Ce mémoire traite des diverses agences de notation et de l'importance qu'elles accordent aux fluctuations du nationalisme dans l'émission des cotes de crédit du Québec de 1970 à 2012. Plus spécifiquement, il a pour objectif de traiter de la situation des finances publiques du Québec, de démystifier le rôle de la notation financière et de déterminer si le nationalisme québécois est une variable spécifique prépondérante dans le processus d'évaluation des agences de notation. L'analyse statistique occupe une portion importante de la démonstration. Ce faisant, il est possible d'établir des modèles, voire des préférences méthodologiques, pour chacune des agences de notation à l'étude dans ce document. Les résultats de cette recherche démontrent que le nationalisme québécois n'est pas une variable spécifique prépondérante dans l'évaluation des agences de notation à l'endroit du Québec mais que ce sont plutôt les facteurs institutionnels et fiscaux qui vont primer lors de l'émission des cotes de crédit.

The objective of this research is to present a comprehensive framework for harnessing the flexibility of power systems in the presence of unforeseen events, such as those associated with component outages or renewable energy variability. Increased penetration of variable resources in the power grid, mainly in the form of wind and solar plants, has resulted in variable power flow patterns, increased thermal unit cycling and higher reserve capacity requirements. Furthermore, the variability of renewable energy output has increased the system’s ramping requirements and threatens the system’s voltage control capabilities. However, new sources of flexibility and network control are emerging to address these problems. Specifically, energy storage systems, demand side management, distributed energy resources and flexible transmission operation can participate by providing ramping services and/or voltage control, as well as by alleviating transmission congestion. This research focuses on contributing to modeling and optimization approaches for scheduling the operation of these sources of flexibility in a certain look-ahead horizon, ensuring a state of the art level of modeling accuracy, with full inclusion of voltage control considerations which do not exist in current DC-OPF modeling approaches. Also, by including reactive power flows, the network congestion model proposed is above par compared to the current state-of-the-art for look-ahead dispatch literature. Nevertheless, the model is further expanded by including a thermal model for transmission lines, which allows for the implementation of dynamic line ratings in look-ahead economic dispatch. The benefits from these augmented modeling capabilities are documented and compared with current operating practices. Once an AC-OPF look-ahead optimization problem has been established, and the corresponding components have been modeled, further contributions are made in the area of remedial action schemes. The developed formulations allow for the identification of appropriate corrective actions that will restore feasibility in infeasible cases. Finally, a combination of contingency filtering and contingency analysis approaches is developed, to allow for fast identification and analysis of critical outages in the transmission system. The filtering approach is based on a basic Taylor expansion of network power flow equations as well as a new formulation of margin indices that directly quantify the proximity to constraint violation in the post-outage system state. The analysis approach is based on low-rank modifications of the Jacobian matrix of network equations, to produce good estimates of post-outage operating states and map the effect on the system’s operating constraints. Compared to current state of the art, advances are made both in the speed and the accuracy of the analysis, since the proposed filtering and analysis methods are fully unbalanced. The need for unbalanced security analysis is discussed and justified. Through the contributions made in this research, a roadmap to increase flexibility in power system operations is developed. Namely, an enhanced modeling capability allows for integration of additional sources of flexibility and voltage control and a highly accurate security analysis and remedial actions formulation allows for improved response to unforeseen critical outages and rapid generation changes.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: For many organisations who wish to sell their debt, or investors who are looking to invest in an organisation, company credit ratings are an important surrogate measure for the marketability or risk associated with a particular issue. Credit ratings are issued by a limited number of authorised companies – with the predominant being Standard & Poor’s, Moody’s and Fitch – who have the necessary experience, skills and motive to calculate an objective credit rating. In the wake of some high profile bankruptcies, there has been recent conjecture about the accuracy and reliability of current ratings. Issues relating specifically to the lack of competition in the rating market have been identified as possible causes of the poor timeliness of rating updates. Furthermore, the cost of obtaining (or updating) a rating from one of the predominant agencies has also been identified as a contributing factor. The high costs can lead to a conflict of interest where rating agencies are obliged to issue more favourable ratings to ensure continued patronage. Based on these issues, there is sufficient motive to create more cost effective alternatives to predicting corporate credit ratings. It is not the intention of these alternatives to replace the relevancy of existing rating agencies, but rather to make the information more accessible, increase competition, and hold the agencies more accountable for their ratings through better transparency. The alternative method investigated in this report is the use of a backpropagation artificial neural network to predict corporate credit ratings for companies in the manufacturing sector of the United States of America. Past research has shown that backpropagation neural networks are effective machine learning techniques for predicting credit ratings because no prior subjective or expert knowledge, or assumptions on model structure, are required to create a representative model. For the purposes of this study only public information and data is used to develop a cost effective and accessible model. The basis of the research is the assumption that all information (both quantitive and qualitative) that is required to calculate a credit rating for a company, is contained within financial data from income statements, balance sheets and cash flow statements. The premise of the assumption is that any qualitative or subjective assessment about company creditworthiness will ultimately be reflected through financial performance. The results show that a backpropagation neural network, using 10 input variables on a data set of 153 companies, can classify 75% of the ratings accurately. The results also show that including collinear inputs to the model can affect the classification accuracy and prediction variance of the model. It is also shown that latent projection techniques, such as partial least squares, can be used to reduce the dimensionality of the model without making any assumption about data relevancy. The output of these models, however, does not improve the classification accuracy achieved using selected un-correlated inputs. / AFRIKAANSE OPSOMMING: Vir baie organisasies wat skuldbriewe wil verkoop, of beleggers wat in ʼn onderneming wil belê is ʼn maatskappy kredietgradering ’n belangrike plaasvervangende maatstaf vir die bemarkbaarheid van, of die risiko geassosieer met ʼn betrokke uitgifte. Kredietgraderings word deur ʼn beperkte aantal gekeurde maatskappye uitgereik – met die belangrikste synde Standard & Poor’s, Moody’s en Fitch. Hulle het almal die nodige ervaring, kundigheid en rede om objektiewe kredietgraderings te bereken. In die nadraai van ʼn aantal hoë profiel bankrotskappe was daar onlangs gissings oor die akkuraatheid en betroubaarheid van huidige graderings. Kwessies wat spesifiek verband hou met die gebrek aan kompetisie in die graderingsmark is geïdentifiseer as ‘n moontlike oorsaak vir die swak tydigheid van gradering opdatering. Verder word die koste om ‘n gradering (of opdatering van gradering) van een van die dominante agentskappe te bekom ook geïdentifiseer as ʼn verdere bydraende faktor gesien. Die hoë koste kan tot ‘n belange konflik lei as graderingsagentskappe onder druk kom om gunstige graderings uit te reik om sodoende volhoubare klante te behou. As gevolg van hierdie kwessies is daar voldoende motivering om meer koste doeltreffende alternatiewe vir die skatting van korporatiewe kredietgraderings te ondersoek. Dit is nie die doelwit van hierdie alternatiewe om die toepaslikheid van bestaande graderingsagentskappe te vervang nie, maar eerder om die inligting meer toeganklik te maak, mededinging te verhoog en om die agentskappe meer toerekenbaar vir hul graderings te maak deur beter deursigtigheid. Die alternatiewe manier wat in hierdie verslag ondersoek word, is die gebruik van ‘n kunsmatige neurale netwerk om die kredietgraderings van vervaardigingsmaatskappye in die VSA te skat. Vorige navorsing het getoon dat neurale netwerke doeltreffende masjienleer tegnieke is om kredietgraderings te skat omdat geen voorafkennis of gesaghebbende kundigheid, of aannames oor die modelstruktuur nodig is om ‘n verteenwoordigende model te bou. Vir doeleindes van hierdie navorsingsverslag word slegs openbare inligting en data gebruik om ʼn kostedoeltreffende en toeganklike model te bou. Die grondslag van hierdie navorsing is die aanname dat alle inligting (beide kwantitatief en kwalitatief) wat benodig word om ʼn kredietgradering vir ʼn onderneming te bereken, opgesluit is in die finansiële data in die inkomstestate, balansstate en kontantvloei state. Die aanname is dus dat alle kwalitatiewe of subjektiewe assessering oor ‘n maatskappy se kredietwaardigheid uiteindelik in die finansiële prestasie sal reflekteer. Die resultate toon dat ʼn neurale netwerk met 10 toevoer veranderlikes op ‘n datastel van 153 maatskappye 75% van die graderings akkuraat klassifiseer. Die resultate toon ook dat die insluiting van kollineêre toevoere tot die model die klassifikasie akkuraatheid en die variansie van die skatting kan beïnvloed. Daar word verder getoon dat latente projeksietegnieke, soos parsiële kleinste kwadrate, die dimensies van die model kan verminder sonder om enige aannames oor data toepaslikheid te maak. Die afvoer van hierdie modelle verhoog egter nie die klassifikasie akkuraatheid wat behaal is met die gekose ongekorreleerde toevoere nie. 121 pages.

Thesis (MBA (Business Management))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: NamPower is the current power utility in Namibia and needs to access the debt capital markets over the next few years, in order to be successful to finance its capital expenditure programme of NAD 13.9 billion. NamPower intends to raise the funding from its operations, shareholders equity injection and debt, in the form of bonds and development finance. In order to be successful in its bond issuance programme, NamPower must at least maintain an investment grade credit rating. Credit Rating Agencies play an important role to provide investors with their credit ratings and reports. Many investors base their investment decision making on certain levels of credit ratings. A credit rating is the probability that an issuer or instrument will default on its debt repayment obligation. Depending on the circumstances, investors usually require a minimum of an investment grade rating (AFP, 2009:20). Looking at the current financial crisis investors felt left down by the credit rating agencies, as investors relied on the credit rating reports and the underlying credit rating. Investors literally lost billions in financial crisis of 2007-8/9 as corporate and structured products defaulted on meeting financial obligations. As a result of the defaults and financial crisis the credit rating agencies have been criticised for inadequate disclosure and potential conflicts of interest. Many critics argue that credit rating agencies are not asking inquisitive questioning and probing into issues when doing credit reviews. Evidence was not that conclusive, but big corporate failures like Enron and WorldCom are examples of the credit rating agencies’ failures. Furthermore, credit rating agencies are not particular about creating predictions of future developments, but the last crisis has shown that credit rating agencies were fairly successful with corporate or issuer ratings as default has been fairly limited to the higher credit rating categories. Evidence provided in the research supports that investors still rely on credit ratings more so for corporate, institutions and fixed income products, but are very insure about structured products, due to recent market failures. Therefore it is still of critical importance for NamPower to maintain its investment grade credit rating. NamPower has maintained and even improved on its local national scale credit rating. Investors are still risk adverse since the financial crisis but as economic conditions improve investors should be coming back to emerging markets. To bring back the investors to invest in the emerging markets will require a certain appetite returning to the investor, but surely there will be a premium or funding will be more costly in future and not in demand as previously. As for NamPower, the opinion is therefore that although smaller in size, it poses as an attractive investment opportunity for investors as there is shortage in investment grade assets in Sub-Sahara Africa to fill the portfolio gaps and give diversification.

published_or_final_version / Economics and Finance / Master / Master of Economics

published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

Although traditional researchers exploring perfectionism frequently cast the construct in a negative light, a steady stream of recent studies have demonstrated that perfectionistic beliefs can yield both positive and negative outcomes. Despite this progression in the research, perfectionism remains an understudied phenomenon among youth, especially as it relates to the ways in which these individuals are perceived by others. The current study builds on the previous literature by exploring adolescent perfectionism across a variety of psychological and psychoeducational dimensions. Moreover, a unique addition to the literature offered by this study was the inclusion of peer-reports along with self-reported measures in hopes of gaining a fuller understanding of the psychosocial characteristics of perfectionistic youth. The incorporation of peer reports also allowed a novel approach to the study of perfectionism by exploring this construct through the lens of their adolescent colleagues. Self and peer reported data was drawn from a sample of 816 ninth grade students representing three separate high schools. MANOVA results revealed a number of differences between perfectionistic subtypes across both self and peer-reported data. More specifically, adaptive perfectionists rated themselves as having less anxiety and depression as compared to their maladaptive and non-perfectionistic counterparts. Adaptive perfectionists were also reported to have stronger interpersonal relationships and greater social connectivity than their peers. Moreover, both adaptive and maladaptive perfectionists reported significantly higher GPAs than non-perfectionists. Peer informant data indicated that adaptive perfectionists were rated as having the highest academic expectations followed by maladaptive perfectionists and then non-perfectionists. Contrary to expectations, no significant differences were found between cluster groupings on peer reported social withdrawnness. Findings suggest that adaptive perfectionism is associated with a range of positive psychological, psychoeducational and psychosocial outcomes. Conversely, maladaptive perfectionism appears to be related to several behaviors which may impede healthy adolescent functioning. Implications regarding the improved assessment of perfectionism and intervention strategies aimed at both students and professionals working within the school domain are discussed.

本研究以2001至2006年台灣上市、上櫃公司為研究對象，探討信用評等與資本結構的關係。參考Kisgen (2006)，以融資順位理論和靜態抵換理論為基礎，本研究探討：(1) 面臨信用評等調等之公司，是否會減少其負債水準，以避免(促使)信用評等調降(調升)，(2) 面臨信用評等調等之公司，是否會背離融資順位理論及靜態抵換理論，而減少長期債務水準。 分析信用評等調等與負債水準關聯性之實證結果顯示，信用評等為影響資本結構之重要因素。企業會因信用評等面臨調等，而減少其負債。此外，企業利用資本結構的改變以避免信用評等調降的動機較促使信用評等調升之動機強，而此種現象又以規模較大之公司較為顯著。 分析信用評等調等對資本結構理論之影響之實證結果顯示，在納入信用評等變數後，面臨信用評等調等之公司有較高傾向背離融資順位理論和靜態抵換理論，進一步減少其長期債務之水準。 / Based on a sample of listed companies in Taiwan over the period of 2001 to 2006, this research investigates the relationship between credit ratings and capital structure. Refers to Kisgen (2006), and result on the Pecking order theory and the Static trade-off theory, this research examines:(1) whether firms near a credit ratings upgrade or downgrade would issue less debt relative to equity. (2) whether firms near a credit ratings upgrade or downgrade would issue less long-term debts and thus depart from the Pecking order theory and the Static trade-off theory. The findings reveals that credit ratings is an important factor to determination of capital structure. The results show that firms near a credit ratings upgrade or downgrade would issue less debt relative to equity. The findings also indicates that firms are more inclined to avoid the downgrade of their credit ratings than to instigate the upgrade of their credit ratings. Such phenomena is more obviously for larger firms. In addition, this research also finds that firms near a credit rating upgrade or downgrade would issue less long-term debts and thus depart from the Pecking order theory and the Static trade-off theory, after taking their credit ratings into consideration.

No description available.