O campo dos interesses profissionais é amplo e complexo, circunscrito por diferentes perspectivas teóricas e técnicas, com impacto na construção da carreira dos indivíduos. Nesse contexto, esta pesquisa objetivou caracterizar inclinações e interesses profissionais de estudantes de diferentes níveis de ensino por meio de instrumentos de avaliação psicológica (Teste de Fotos de Profissões, BBT-Br e o Questionário de Busca Autodirigida SDS), bem como avaliar características psicométricas das referidas técnicas de exame na região norte do Brasil. Foram avaliados 729 estudantes provenientes de escolas públicas e particulares da cidade de Manaus (AM), pertencentes ao final do ensino fundamental (9º ano), ensino médio (1ª, 2ª e 3ª séries) e a 3ª série do ensino técnico integrado ao ensino médio, igualmente distribuídos em função do sexo. Os instrumentos (BBT-Br e SDS) foram aplicados no próprio contexto escolar, após as devidas autorizações formais para a pesquisa, incluindo-se também Questionário de Classificação Econômica Brasil para caracterização da amostra. Os resultados, após sistematização padronizada em cada instrumento de avaliação psicológica, foram tratados de modo inicialmente descritivo, para o grupo geral, bem como em função das variáveis: sexo, idade, nível de ensino e origem escolar (pública ou particular). Seguiram-se análises inferenciais dos possíveis efeitos dessas características sócio demográficas sobre as inclinações e os interesses profissionais, bem como a análise de características psicométricas do BBT-Br e SDS. Os resultados do BBT-Br apontaram tendência geral dos interesses dos estudantes (de diferentes níveis de ensino e de ambos os sexos) por atividades que envolvam pensamento abstrato/criativo e senso social/ajuda ao outro (respectivamente representadas pelos radicais G e S). No SDS a tipologia que se destacou, em todos os níveis de ensino, foi o tipo Social (interesses em atividades profissionais de ajuda e ensino) no grupo feminino, e o tipo Empreendedor (liderança, relacionamentos interpessoais e atividades persuasivas) no grupo masculino. Foram detectados padrões específicos na expressão dos interesses de estudantes da região norte em relação aos dados disponíveis do BBT-Br e do SDS para a região sudeste, embora com pontos de convergência motivacional. Sobre as características psicométricas dos instrumentos utilizados, foram identificados bons indicadores de fidedignidade (Alfa de Cronbach para o BBT-Br no sexo feminino foi de 0,84 e, para o sexo masculino, foi de 0,85; no SDS para o sexo feminino foi 0,78 e para o sexo masculino foi 0,79), e também de validade (análise de componentes principais e correlação entre resultados do BBT-Br e do SDS). O estudo contribuiu para a compreensão de características vocacionais e de interesses profissionais de estudantes da cidade de Manaus (AM), comparando-as aos dados disponíveis com estudantes do sudeste do Brasil, demonstrando indicadores positivos de validade das duas versões do BBT-Br e do SDS para uso na região norte. Reflexões foram realizadas, a partir dos achados, no sentido de sugerir processos de orientação vocacional e profissional para os estudantes do 9º ano do ensino fundamental, ensino médio e ensino técnico, para estimular escolhas profissionais que favoreçam a saúde mental e a realização pessoal, em termos científicos e educacionais, de indivíduos dessa região do Brasil. / The field of professional interests is wide and complex, circumscribed by different theoretical and technical perspectives, having impact in the construction of individuals\' career. In this context, this research aimed to describe professional interests and inclinations in students of different levels of education through instruments of psychological assessment (Berufsbilder Test BBT-Br, and Self-Directed Search SDS), as well as assess psychometric characteristics of these measures in the North region of Brazil. The sample was comprised of 729 students from public and private schools in the city of Manaus (AM), that were attending the last year of fundamental school (9th grade), middle school (1st, 2nd and 3rd years) and 3rd year of technical education, equally distributed for gender. Instruments were administered within the school context, after formal authorizations were obtained, along with the Brazil Economic Classification Questionnaire to sample description. The results, after patterned systematization in each instrument, were first analyzed for the description of all sample, on the variables gender, age, school level and school origin (public vs. private school). Following, inferential analysis of possible effects of the sociodemographic profile on professional interests and inclinations were tested, as well as the psychometric properties of BBT-Br and SDS. The results for BBT-Br pointed to a general tendency of students interests (of different educational levels and both sexes) to activities involving abstract/creative reasoning and social sense/help to others (respectively represented by radical G and S). On SDS, the featured typology was the Social type (interest in professional activities of help and teaching), for all school levels, for females group, and Entrepreneur type (leadership, interpersonal relationships, and persuasive activities) for males group. Specific patterns of the expression of interests in the North region emerged in comparison to normative data available of Southeast region, for both tests, with some points of motivational convergence. About psychometric qualities of the measures, good indicators of reliability (Cronbachs alpha of .84 for females and .85 for males on the BBT-Br; and .78 for females and .79 for males on SDS), as well as validity (principal component analysis and correlation between results on BBT-Br and SDS). This study contributes to the comprehension of vocational and professional interests of students in the city of Manaus (AM) when comparing to data available from the Southeast region of Brazil, demonstrating positive indicators of validity for both tests, to its use in the North region context. Some reflections about the data intended to contribute with suggestions about the process of vocational and professional orientation to students of 9th grade of fundamental school, middle school, and technical education, in order to stimulate professional choices that promote mental health and self-fulfillment, scientifically and educationally, for individuals of this region of Brazil.

Orientation In this study the interrelationship between specific personality and interests measures were explored to improve understanding of the respective constructs and their interrelations. A literature study and empirical research was conducted to serve the purpose of this study. Research purpose The purpose of this study was to explore the interrelationship between personality and interests using the measures of the OPQ, the 15FQ+, and the SDS. Motivation for study Much research has been done on the importance of the use of personality and interest questionnaires for career guidance and other purposes. However, a correlation between the SDS and OPQ and between the SDS and 15FQ+ has not been researched. As such, this study was intended to provide valuable insight into the interrelation between the personality and interests as measured by the OPQ, the SDS and the 15FQ+, which should enhance the interpretation of the respective constructs. Research Methodology An exploratory research method was used, as it was a systematic investigation of the relationship among two or more variables. A quantitative strategy of inquiry was used for this study. Main findings A canonical correlation analysis showed moderate to strong interrelationship between personality traits and vocational interest. The interrelation of the OPQ, the SDS and the 15FQ+ are significant. The findings indicated how personality and interests differ and converge for enhancing interpretation purposes. Practical/managerial implications Holland’s theory of vocational interests focuses on the application of the SDS for career purposes, as well as for measuring job fit and job satisfaction. A better understanding of the interrelationship between personality and interests help practitioners to optimize the use of the measures within various contexts. Contributions/value additions The study will enable practitioners to more effectively utilize the personality and interest measures, combined or separately, as the interrelationships are now better known and construct validity is enhanced. Conclusion The objective of this research was successfully achieved, as satisfactory evidence was provided to address the overarching research purpose. / Dissertation (MSc)--University of Pretoria, 2014. / Human Resource Management / MSc / Restricted

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

Combinatorial problems are ubiquitous in our society and solving such problems efficiently is often crucial. One technique for solving combinatorial problems is constraint-based local search. Its compositional nature together with its efficiency on large problem instances have made this technique particularly attractive. In this thesis we contribute to simplifying the solving of combinatorial problems using constraint-based local search. To provide higher-level modelling options, we introduce set variables and set constraints in local search by extending relevant local search concepts. We also propose a general scheme to follow in order to define what we call natural and balanced constraint measures, and accordingly define such measures for over a dozen set constraints. However, defining such measures for a new constraint is time-consuming and error-prone. To relieve the user from this, we provide generic measures for any set constraint modelled in monadic existential second-order logic. We also theoretically relate these measures to our proposed general scheme, and discuss implementation issues such as incremental algorithms and their worst-case complexities. To enable higher-level search algorithms, we introduce constraint-directed neighbourhoods in local search by proposing new constraint primitives for representing such neighbourhoods. Based on a constraint, possibly modelled in monadic existential second-order logic, these primitives return neighbourhoods with moves that are known in advance to achieve a decrease (or preservation, or increase) of the constraint measures, without the need to iterate over any other moves. We also present a framework for constraint-based local search where one can model and solve combinatorial problems with set variables and set constraints, use any set constraint modelled in monadic existential second-order logic, as well as use constraint-directed neighbourhoods. Experimental results on three real-life problems show the usefulness in practice of our theoretical results: our running times are comparable to the current state-of-the-art approaches to solving the considered problems.

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