This study compared 3 approaches for handling a fourth level of nesting structure when analyzing data from a cluster-randomized trial (CRT). CRTs can include 3 levels of nesting: repeated measures, individual, and cluster levels. However, above the cluster level, there may sometimes be an additional potentially important fourth level of nesting (e.g., schools, districts, etc., depending on the design) that is typically ignored in CRT data analysis. The current study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting it using a design-based approach on parameter and standard error (SE) estimates. Several fixed effect and random effect variance parameters and SEs were biased across all 3 models. In the 4-level model, most SE biases decreased as the number of level 3 clusters increased and as the number of level 4 clusters decreased. Also, random effect variance biases decreased as the number of level 3 clusters increased. In the 3-level and complex models, SEs became more biased as the weight level 4 carried increased (i.e., larger intraclass correlation, more clusters at that level). The current results suggest that if a meaningful fourth level of nesting exists, future researchers should account for it using design-based approach; the model-based approach is not recommended. If the fourth level is not practically important, researchers may ignore it altogether.

No description available.

Outcomes Based Education (OBE), has been, since its inception, fraught with problems. OBE in its very nature is complex. To fully embrace this method and ensure its success, schools must be able to make the necessary paradigm shift. This can only be achieved when schools receive relevant and empowering training, support and development. In other words, organisational development must be the key words. The aim of this study is to explore the impact of organisational development interventions on the implementation of OBE. The case study method was employed where it was realised that schools that received organisational development interventions, together with Outcomes Based Education, were able to implement this method with greater understanding, skill, and confidence. The investigation recommends an organisational development design that could be used instead of the cascade model, and provides suggestions on what can be done to ensure a more successful implementation process. / Educational Studies / M. Ed (Education Management)

No presente trabalho propomos uma estrutura bayesiana, através de um esquema de dados aumentados, para analisar modelos longitudinais com grupos mútiplos (MLGMTRI) na Teoria da Resposta ao Item (TRI). Tal estrutura consiste na tríade : modelagem, métodos de estimação e métodos de diagnóstico para a classe de MLGMTRI. Na parte de modelagem, explorou-se as estruturas multivariada e multinível, com o intuito de representar a hierarquia existente em dados longitudinais com grupos múltiplos. Esta abordagem permite considerar várias classes de submodelos como: modelos de grupos múltiplos e modelos longitudinais de um único grupo. Estudamos alguns aspectos positivos e negativos de cada uma das supracitadas abordagens. A modelagem multivariada permite representar de forma direta estruturas de dependência, além de possibilitar que várias delas sejam facilmente incorporadas no processo de estimação. Isso permite considerar, por exemplo, uma matriz não estruturada e assim, obter indícios da forma mais apropriada para a estrutura de dependência. Por outro lado, a modelagem multinível propicia uma interpretação mais direta, obtenção de condicionais completas univariadas, fácil inclusão de informações adicionais, incorporação de fontes de dependência intra e entre unidades amostrais, dentre outras. Com relação aos métodos de estimação, desenvolvemos um procedimento baseado nas simulações de Monte Carlo via cadeias de Markov (MCMC). Mostramos que as distribuições condicionais completas possuem forma analítica conhecida e, além disso, são fáceis de se amostrar. Tal abordagem, apesar de demandar grande esforço computacional, contorna diversos problemas encontrados em outros procedimentos como: limitação no número de grupos envolvidos, quantidade de condições de avaliação, escolha de estruturas de dependência, assimetria dos traços latentes, imputação de dados, dentre outras. Além disso, através da metodologia MCMC, desenvolvemos uma estrutura de seleção de matrizes de covariâncias, através de um esquema de Monte Carlo via Cadeias de Markov de Saltos Reversíveis (RJMCMC). Estudos de simulação indicam que o modelo, o método de estimação e o método de seleção produzem resultados bastante satisfatórios. Também, robustez à escolha de prioris e valores iniciais foi observada. Os métodos de estimação desenvolvidos podem ser estendidos para diversas situações de interesse de um modo bem direto. Algumas das técnicas de diagnóstico estudadas permitem avaliar a qualidade do ajuste do modelo de um modo global. Outras medidas fornecem indícios de violação de suposições específicas, como ausência de normalidade para os traços latentes. Tal metodologia fornece meios concretos de se avaliar a qualidade do instrumento de medida (prova, questionário etc). Finalmente, a análise de um conjunto de dados real, utilizando-se alguns dos modelos abordados no presente trabalho, ilustra o potencial da tríade desenvolvida além de indicar um ganho na utilização dos modelos longitudinais da TRI na análise de ensaios educacionais com medidas repetidas em deterimento a suposição de independência. / In this work we proposed a bayesian framework, by using an augmented data scheme, to analyze longitudinal multiple groups models (LMGMIRT) in the Item Response Theory (IRT). Such framework consists in the following set : modelling, estimation methods and diagnostic tools to the LMGMIRT. Concerning the modelling, we exploited multivariate and multilevel structures in order to represent the hierarchical nature of the longitudinal multiple groupos model. This approach allows to consider several submodels such that: multiple groups and longitudinal one group models. We studied some positive and negative aspects of both above mentioned approches. The multivariate modelling allows to represent, in a straightforward way, many dependence structures. Furthermore it possibilities that many of them can be easily considered in the estimation process. This allows, for example, to consider an unstructured covariance matrix and, then, it allows to obtain information about the most appropritate dependece structure. On the other hand, the multilevel modelling permits to obtain: more straightforward interpretations of the model, the construction of univariate full conditional distributions, an easy way to include auxiliary information, the incorporation of within and between subjects (groups) sources of variability, among others. Concerning the estimation methods, we developed a procedure based on Monte Carlo Markov Chain (MCMC) simulation. We showed that the full conditional distributions are known and easy to sample from. Even though such approach demands a considerable amount of time it circumvents many problems such that: limitation in the number of groups that can be considered, the limitation in the number of instants of observation, the choice of covariance matrices, latent trait asymmetry, data imputation, among others. Furthermore, within the MCMC metodology, we developed a procedure to select covariance matrices, by using the so called Reversible Jump MCMC (RJMCMC). Simulation studies show that the model, the estimation method and the model selection procedure produce reasonable results. Also, the studies indicate that the developed metodology presents robustness concerning prior choice and different initial values choice. It is possible to extent the developed estimation methods to other situations in a straightforward way. Some diagnostics techniques that were studied allow to assess the model fit, in a global sense. Others techniques give directions toward the departing from some specific assumptions as the latent trait normality. Such methodology also provides ways to assess the quality of the test or questionaire used to measure the latent traits. Finally, by analyzing a real data set, using some of the models that were developed, it was possible to verify the potential of the methodology considered in this work. Furthermore, the results of this analysis indicate advantages in using longitudinal IRT models to model educational repeated measurement data instead of to assume independence.

Objetivos: Identificar a distribuição do índice de massa corporal (IMC), da circunferência da cintura (Ccintura), da razão cintura-quadril (RCQ) e do índice de conicidade (IC), segundo variáveis demográficas, socioeconômicas, relacionadas à saúde e comportamentais na população de 30 anos e mais, residente no município de Ribeirão Preto - SP, em 2006; avaliar o efeito modulador das regiões geográficas do município sobre os fatores associados aos indicadores antropométricos e estimar a contribuição do nível agregado para esses desfechos. Métodos: Estudo epidemiológico transversal, de base populacional, com amostragem em três estágios de sorteio. Pesos amostrais foram calculados para a recomposição do total de elegíveis e correção da taxa de não-resposta, em cada setor censitário, originando amostra ponderada de 2.197 participantes. Médias e intervalos de confiança (95%) dos indicadores antropométricos foram calculados, segundo sexo, nas categorias das variáveis independentes. Análise de variância, com um critério de classificação foi utilizada para a comparação das médias dos desfechos nas categorias das variáveis independentes. Testes de tendência linear foram aplicados para variáveis com mais de duas categorias. O nível de significância adotado foi ?=5%. Para a identificação de fatores associados aos indicadores antropométricos foram construídos modelos lineares multinível de efeitos fixos com dois níveis. Os 81 setores censitários sorteados foram agrupados em quatro regiões geográficas do município (nível agregado). A análise multinível seguiu modelo conceitual hierarquizado para avaliação do efeito direto de variáveis de nível individual e a contribuição do nível agregado sobre os desfechos (rho). Medidas de efeito (?) foram estimadas por pontos e por intervalos com 95% de confiança para as variáveis independentes. As medidas de associação foram calculadas, inicialmente, em modelos parciais que incluíram as variáveis de cada bloco hierárquico, ajustadas para as de blocos precedentes. Nos modelos finais, permaneceram as variáveis que mantiveram significância estatística (p < 0,05), após ajustamento simultâneo para os blocos precedentes. Para cada um dos desfechos considerados, calcularam-se médias ajustadas nas categorias das variáveis independentes, segundo regiões geográficas do município. Diferenças das médias ajustadas entre os extremos das categorias das variáveis independentes foram calculadas para avaliar o efeito modulador das regiões geográficas. Todas as estimativas calculadas levaram em consideração o efeito de desenho amostral. Resultados: Em relação ao IMC e em ambos os sexos, médias de maior magnitude foram detectadas entre os que referiram antecedentes familiares de excesso de peso, história pessoal de obesidade, utilizaram medicamentos nos últimos 15 dias e seguiram dieta alimentar para perda de peso. No sexo feminino, médias mais elevadas de IMC foram detectadas entre as mais velhas, com menor escolaridade, maior tempo de residência no município e que referiram antecedentes familiares de acidente vascular cerebral (AVC). O elenco de fatores associados ao IMC foi constituído por: sexo (?= -0,814; IC95% : -1,514 - -0,113), idade (?= 0,050; IC95% : 0,014 - 0,086), escolaridade (?= -0,101; IC95% : -0,206 - -0,005) , antecedentes familiares de excesso de peso (?= 1,214; IC95% : 0,468 - 1,961), história pessoal de obesidade (?= 6,422; IC95%: 5,724 - 7,119) e consumo de lipídios (?= 0,029; IC95% : 0,008 - 0,051), considerando-se a contribuição de 11% do nível agregado. As menores diferenças entre as médias ajustadas do IMC foram detectadas, principalmente, nos extremos das categorias das variáveis: sexo, faixas etárias, escolaridade e consumo de lipídios nas regiões Norte e Oeste do município. Médias de maior magnitude para os três indicadores de obesidade central (Ccintura, RCQ e IC), foram observadas entre os participantes mais velhos, com menor escolaridade, classificados nos estratos mais baixos de renda, com maior tempo de residência no município, com antecedentes familiares de excesso de peso, história pessoal de obesidade e entre aqueles classificados como \"pré-obesos\" e \"obesos\". O elenco de fatores associados à Ccintura foi constituído por: sexo (?= -8,686; IC95% : -9,439 - -7,932), idade (?= 0,169; IC95% : 0,122 - 0,216), antecedentes familiares de AVC (?= 0,689; IC95% : 0,051 - 1,327), história pessoal de obesidade (?= 2,363; IC95% : 0,320 - 4,410), IMC (?= 1,689; IC95% : 1,437 - 1,941), nº de medicamentos (?= 0,259; IC95% : 0,078 - 0,440), tempo de tabagismo (?= 0,035; IC95% : 0,008 - 0,061) e energia total da dieta (?= 0,084; IC95% : 0,012 - 0,157), considerando-se a contribuição de 12,4% do nível agregado. As menores diferenças entre as médias ajustadas da Ccintura foram detectadas nos extremos das categorias das variáveis: antecedentes familiares de AVC, tempo de tabagismo e energia total da dieta nas regiões Norte e Oeste. Em relação à RCQ, permaneceram associadas as variáveis: sexo (?= -0,099; IC95% : -0,107 - -0,090), idade (?= 0,002; IC95% : 0,001 - 0,003), IMC (?= 0,004; IC95% : 0,003 - 0,005), nº de medicamentos (?= 0,004; IC95% : 0,001 - 0,007), tempo de tabagismo (?= 0,0006; IC95% : 0,0004 - 0,0009), dependência de álcool (?= 0,013; IC95% : 0,003 - 0,022), dieta para perder peso (?= 0,017; IC95% : 0,004 - 0,030) e consumo de carboidratos (?= 0,0001; IC95% : 0,0001 - 0,0002). A contribuição do nível agregado foi equivalente a 12%. As menores diferenças entre as médias ajustadas da RCQ foram detectadas nos extremos das categorias das variáveis: nº de medicamentos e consumo de carboidratos na região Oeste. O elenco de fatores associados ao IC foi composto por: sexo (?= -0,068; IC95% : -0,076 - -0,056), idade (?= 0,003; IC95% : 0,002 - 0,004), antecedentes familiares de AVC (?= 0,008; IC95% : 0,001 - 0,016), IMC (?= 0,005; IC95% : 0,004 - 0,006), acesso a serviços de saúde (?= -0,011; IC95% : -0,020 - -0,002), nº de medicamentos (?= 0,004; IC95% : 0,002 - 0,007), tempo de tabagismo (?= 0,0004; IC95% : 0,0001 - 0,0007), dependência de álcool (?= 0,011; IC95% : 0,001 - 0,020), dieta para perder peso (?= 0,015; IC95% : 0,002 - 0,030) e consumo de ácidos graxos monoinsaturados (?= -0,0005; IC95% : -0,0010 - -0,0001), com contribuição do nível agregado equivalente a 14,1%. As menores diferenças entre as médias ajustadas do IC foram detectadas nos extremos das categorias das variáveis: acesso a serviços de saúde e dependência de álcool, na região Norte. Conclusões: No município de Ribeirão Preto, as regiões Norte e Oeste foram identificadas como regiões obesogênicas. Medidas de promoção de hábitos saudáveis, bem como de prevenção da obesidade, no município, não devem ser direcionadas apenas para mudanças de hábitos individuais, visto que o impacto de tais intervenções pode ser minimizado ou refreado pelo efeito de contexto destas regiões. / Objectives: To identify the distribution of the body mass index (BMI), waist circumference (waistC), waist-to-hip ratio (WHR) and conicity index (CI), according to demographic, socioeconomic, health-related and behavioral variables, in the population aged 30 years and older, living in Ribeirão Preto - SP, Brazil, in 2006; to assess shaping effect of geographical regions on associated factors related to anthropometric indexes as well as to estimate the contribution of the ecological level to the outcomes. Methods: Population-based cross-sectional epidemiological study with multistage sampling. Sampling weights were calculated to compose the total number of eligible participants and correct for the non-response rate, in each census tract, resulting in a weighted sample of 2,197 participants. Means and confidence intervals (95%) were calculated for the anthropometric indexes, according to gender, in the categories of the independent variables. To compare the mean outcomes in the categories of the independent variables, ANOVA (one criterion) was used. Linear trend tests were applied for variables with more than two categories. Significance level was 5%. To identify anthropometric indexes correlates, two-level fixed-effects multilevel linear models were applied. The 81 census tracts drawn were grouped in four geographical regions of the city (ecological level). Multilevel analysis followed a hierarchical conceptual model to assess the direct effect of individual variables and the contribution of the ecological level to the outcomes (rho). Point and confidence intervals (95%) for the effect measures (?) were calculated for independent variables. Firstly, these effect measures were calculated in partial models that included the variables of each hierarchical block, adjusted for the preceding blocks\' variables. In the final models, statistically significant variables (p< 0.05) were kept, after adjusting for the preceding blocks. For the outcomes, the adjusted means were calculated in the categories of the independent variables, according to geographical regions. Adjusted means differences between extreme categories of independent variables were calculated to assess the shaping effect of the geographical regions. All estimates took into account the design effect. Results: Higher mean of BMI were detected, in both gender, among those who reported familial antecedents of overweight, personal history of obesity, took medicines in the last 15 days, and adopted a weight loss diet. Among women, higher mean of BMI were found among the older, those with lower education level, who had lived in the city for longer time and reported familial antecedents of stroke (STR). The BMI correlates were: gender (?= -0.814; CI95% : -1.514 - -0.113), age (?= 0.050; CI95% : 0.014 - 0.086), education (?= -0.101; CI95% : -0.206 - -0.005), familial antecedents of overweight (?= 1.214; CI95% : 0.468 - 1.961), personal antecedents of overweight (?= 6.422; CI95% : 5.724 - 7.119) and total fat consumption (?= 0.029; CI95% : 0.008 - 0.051), considering the 11% contribution of the ecological level. The smallest adjusted means differences of BMI were mainly detected at the extreme categories of the following variables: gender, age strata, education and total fat consumption in the North and West regions. Higher means for the three central obesity indexes (waistC, WHR and CI) were observed among the older, with a lower education level, classified in the lowest income groups, who had lived in the city for longer time, with familial antecedents of overweight, personal antecedents of obesity, and among those classified as \"pre-obese\" and \"obese\". The waistC correlated included: gender (?= -8.686; CI95% : -9.439 - -7.932), age (?= 0.169; CI95% : 0.122 - 0.216), familial antecedents of STR (?= 0.689; CI95% : 0.051 - 1.327), personal antecedents of obesity (?= 2.363; CI95% : 0.320 - 4.410), BMI (?= 1.689; CI95% : 1.437 - 1.941), nº of medicines taken (?= 0.259; CI95% : 0.078 - 0.440), smoking time (?= 0.035; CI95% : 0.008 - 0.061), and total energy of diet (?= 0.084; CI95% : 0.012 - 0.157), considering the 12.4% contribution of the ecological level. The smallest adjusted means differences of waistC were detected at the extreme categories of the following variables: familial antecedents of STR, smoking time, and total energy of diet in the North and West. The correlates of WHR were: gender (?= -.099; CI95% : -0.107 - -0.090), age (?= 0.002; CI95%: 0.001 - 0.003), BMI (?= 0.004; CI95% : 0.003 - 0.005), nº of medicines taken (?= 0.004; CI95% : 0.001 - 0.007), smoking time (?= 0.0006; CI95% : 0.0004 - 0.0009), alcohol addiction (?= 0.013; CI95% : 0.003 - 0.022), weight loss diet (?= 0.017; CI95% : 0.004 - 0.030) and carbohydrate consumption (?= 0.0001; CI95% : 0.0001 - 0.0002). The ecological level contributed with 12%. The smallest adjusted means differences of WHR were detected at the extreme categories of the variables: nº of medicines taken and carbohydrate consumption in the West of the city. The CI correlates included: gender (?= -0.068; CI95% : -0.076 - -0.056), age (?= 0.003; CI95% : 0.002 - 0.004), familial antecedents of STR (?= 0.008; CI95% : 0.001 - 0.016), BMI (?= 0.005; CI95% : 0.004 - 0.006), access to health services (?= -0.011; CI95% : -0.020 - -0.002), nº of medicines taken (?= 0.004; CI95% : 0.002 - 0.007), smoking time (?= 0.0004; CI95% : 0.0001 - 0.0007), alcohol addiction (?= 0.011; CI95% : 0.001 - 0.020), weight loss diet (?= 0.015; CI95% : 0.002 - 0.030) and MUFA´s consumption (?= -0.0005; CI95% : -0.0010 - -0.0001), with 14.1% of contribution from the ecological level. The smallest adjusted means differences of CI were detected at the extreme categories of the variables: access to health services and alcohol addiction, in the North region. Conclusions: In the Ribeirão Preto city, the North and West regions were identified as obesogenic areas. Thus to promoting health behaviors and preventing the obesity in the city it should be taken into account that interventions might be applied not only for changing individual behaviors, since the impact of those interventions may be minimized or constrained by the contextual effect imposed by those regions.

No presente trabalho propomos uma estrutura bayesiana, através de um esquema de dados aumentados, para analisar modelos longitudinais com grupos mútiplos (MLGMTRI) na Teoria da Resposta ao Item (TRI). Tal estrutura consiste na tríade : modelagem, métodos de estimação e métodos de diagnóstico para a classe de MLGMTRI. Na parte de modelagem, explorou-se as estruturas multivariada e multinível, com o intuito de representar a hierarquia existente em dados longitudinais com grupos múltiplos. Esta abordagem permite considerar várias classes de submodelos como: modelos de grupos múltiplos e modelos longitudinais de um único grupo. Estudamos alguns aspectos positivos e negativos de cada uma das supracitadas abordagens. A modelagem multivariada permite representar de forma direta estruturas de dependência, além de possibilitar que várias delas sejam facilmente incorporadas no processo de estimação. Isso permite considerar, por exemplo, uma matriz não estruturada e assim, obter indícios da forma mais apropriada para a estrutura de dependência. Por outro lado, a modelagem multinível propicia uma interpretação mais direta, obtenção de condicionais completas univariadas, fácil inclusão de informações adicionais, incorporação de fontes de dependência intra e entre unidades amostrais, dentre outras. Com relação aos métodos de estimação, desenvolvemos um procedimento baseado nas simulações de Monte Carlo via cadeias de Markov (MCMC). Mostramos que as distribuições condicionais completas possuem forma analítica conhecida e, além disso, são fáceis de se amostrar. Tal abordagem, apesar de demandar grande esforço computacional, contorna diversos problemas encontrados em outros procedimentos como: limitação no número de grupos envolvidos, quantidade de condições de avaliação, escolha de estruturas de dependência, assimetria dos traços latentes, imputação de dados, dentre outras. Além disso, através da metodologia MCMC, desenvolvemos uma estrutura de seleção de matrizes de covariâncias, através de um esquema de Monte Carlo via Cadeias de Markov de Saltos Reversíveis (RJMCMC). Estudos de simulação indicam que o modelo, o método de estimação e o método de seleção produzem resultados bastante satisfatórios. Também, robustez à escolha de prioris e valores iniciais foi observada. Os métodos de estimação desenvolvidos podem ser estendidos para diversas situações de interesse de um modo bem direto. Algumas das técnicas de diagnóstico estudadas permitem avaliar a qualidade do ajuste do modelo de um modo global. Outras medidas fornecem indícios de violação de suposições específicas, como ausência de normalidade para os traços latentes. Tal metodologia fornece meios concretos de se avaliar a qualidade do instrumento de medida (prova, questionário etc). Finalmente, a análise de um conjunto de dados real, utilizando-se alguns dos modelos abordados no presente trabalho, ilustra o potencial da tríade desenvolvida além de indicar um ganho na utilização dos modelos longitudinais da TRI na análise de ensaios educacionais com medidas repetidas em deterimento a suposição de independência. / In this work we proposed a bayesian framework, by using an augmented data scheme, to analyze longitudinal multiple groups models (LMGMIRT) in the Item Response Theory (IRT). Such framework consists in the following set : modelling, estimation methods and diagnostic tools to the LMGMIRT. Concerning the modelling, we exploited multivariate and multilevel structures in order to represent the hierarchical nature of the longitudinal multiple groupos model. This approach allows to consider several submodels such that: multiple groups and longitudinal one group models. We studied some positive and negative aspects of both above mentioned approches. The multivariate modelling allows to represent, in a straightforward way, many dependence structures. Furthermore it possibilities that many of them can be easily considered in the estimation process. This allows, for example, to consider an unstructured covariance matrix and, then, it allows to obtain information about the most appropritate dependece structure. On the other hand, the multilevel modelling permits to obtain: more straightforward interpretations of the model, the construction of univariate full conditional distributions, an easy way to include auxiliary information, the incorporation of within and between subjects (groups) sources of variability, among others. Concerning the estimation methods, we developed a procedure based on Monte Carlo Markov Chain (MCMC) simulation. We showed that the full conditional distributions are known and easy to sample from. Even though such approach demands a considerable amount of time it circumvents many problems such that: limitation in the number of groups that can be considered, the limitation in the number of instants of observation, the choice of covariance matrices, latent trait asymmetry, data imputation, among others. Furthermore, within the MCMC metodology, we developed a procedure to select covariance matrices, by using the so called Reversible Jump MCMC (RJMCMC). Simulation studies show that the model, the estimation method and the model selection procedure produce reasonable results. Also, the studies indicate that the developed metodology presents robustness concerning prior choice and different initial values choice. It is possible to extent the developed estimation methods to other situations in a straightforward way. Some diagnostics techniques that were studied allow to assess the model fit, in a global sense. Others techniques give directions toward the departing from some specific assumptions as the latent trait normality. Such methodology also provides ways to assess the quality of the test or questionaire used to measure the latent traits. Finally, by analyzing a real data set, using some of the models that were developed, it was possible to verify the potential of the methodology considered in this work. Furthermore, the results of this analysis indicate advantages in using longitudinal IRT models to model educational repeated measurement data instead of to assume independence.

The overall aim of this thesis is to contribute to an understanding of the pathways through which structural and intermediary determinants influence child health in Colombia and how they operate through the context where children live in a more comprehensive way than has been determined to date. Colombia has made significant progress in child health in the last few decades and it is currently on track to meet the Millennium Development Goals (MDG). Nearly 90% of the goals on global malnutrition, infant mortality rate and under-five mortality rate have been achieved. However, despite the progress, national averages remain masking huge territorial disparities. While some regions present figures similar to those of a developed country, others report indicators similar to those of a very poor African country. Some municipalities, for example, record no stunted children, whereas in others, the prevalence of chronic malnutrition is greater than 50%. In this context, empirical research that enhances our understanding of socioeconomic determinants of child health and guide policy-making is crucial in order to reduce place-based health inequities in Colombia. This thesis is based on three research articles. The data used in these studies are drawn from the 2010 Colombian Demographic and Health Survey (DHS). The DHS is a large survey programme designed to collect high-quality nationally representative data on population, health and nutrition for developing countries. The DHS are widely recognised as the most important source of information for the analysis of health inequities in the developing world. Firstly, intermediary determinants of early childhood health have been analysed through the construction of a composite index (Chapter 3). The intermediary determinants are the most immediate mechanisms through which socioeconomic position influence child health inequities. Therefore, their identification should contribute to the drafting of intervention policies at this level, given the importance of these factors in programmes aimed at improving maternal and child care. The index allowed us to identify key immediate determinants of child health and their relative importance among Colombian departments (administrative subdivisions). The index was constructed using a more sophisticated methodological approach than that commonly used in the literature, termed polychoric principal component analysis (PCA). A hierarchical cluster analysis was also carried out in order to identify how departments cluster based on the health of their children rather than their geographic proximity. The results showed that the largest differences in intermediary determinants of child health are associated with health care before and during delivery. Furthermore, the departments that perform relatively better in the most immediate determinants of child health are located in the centre of the country. In contrast, those departments that perform worse are located in the peripheral region. This region has a per capita gross domestic product (GDP) well below the national average, little state presence, a hostile environment and a large proportion of the ethnic minorities. Our index provides very useful information in terms of public policy since it facilitates measuring, visualizing and monitoring of child health indicators, and may, therefore, help identify potential intervention strategies for improving the well-being of Colombian children. Secondly, this thesis examines the effect of individual, family and community socioeconomic conditions on different indices representing intermediary determinants of child health, using a coherent conceptual framework (Chapter 4). Using a weighted multilevel approach, the results indicate that whilst community socioeconomic context can exert a greater influence on factors linked directly to health, in the case of psychosocial factors and parent’s behaviours, the family context can be more important. In addition, the results indicate that a significant percentage of the variability in the overall index of intermediary determinants of child health is explained by the community context, even after controlling for individual, family and community characteristics. This study provides evidence that community socioeconomic context is a key component for improving child health in Colombia. However, the role played by context may vary according to the category of the intermediary determinants of child health analysed, highlighting the importance of distinguishing between community and family intervention programmes. Thirdly, the influence of education of other women in the community and family socioeconomic characteristics on child nutrition outcomes, as well as their interactions, was investigated (Chapter 5). The contextual effects of education on child health were studied using weighted multilevel models. This study takes into account important methodological issues such as sample weights and second level endogeneity in multilevel modelling, which have not been addressed in the empirical literature and can lead to biases in the estimates. Different ways through which community education can substitute for the effect of family characteristics on child nutrition were found, suggesting that child care programmes should focus not only on individuals but should also target the broader context of communities. In particular, those communities with less educated mothers and with low female autonomy are those that could benefit more from intervention policies that focus on encouraging female education. To conclude, previous studies on the social determinants of child health in Colombia are limited. Most of them have covered the issue from the perspective of the individual and little attention has been paid to the effect of context. In this vein, understanding the structural and intermediary determinants of child health inequities, as well as the role played by community socioeconomic context, is essential for the design, monitoring and tracking of public child care policies in Colombia. / El objetivo de esta tesis es contribuir a un entendimiento de los mecanismos a través de los cuales los determinantes estructurales e intermediarios de la salud influencian la salud de los niños en Colombia y como estos operan a través del contexto donde los niños viven, de una manera más completa que la que se ha hecho hasta ahora. Esta tesis se base en tres artículos de investigación. Los datos usados en estos estudios son de la “Demographic and Health Survey” (DHS) para Colombia en el año 2010. En primer lugar, los determinantes intermediarios de salud infantil se analizan a través de un indicador compuesto. El indicador permite identificar determinantes intermediarios claves de salud infantil y su importancia relativa entre departamentos colombianos. En segundo lugar, esta tesis examina el efecto de las condiciones socioeconómicas de la comunidad, la familia y los individuos sobre diferentes índices que representan determinantes intermediarios de salud infantil. Por último, se analiza la influencia de la educación de otras mujeres en la comunidad y de características socioeconómicas de las familias sobre la nutrición infantil, así como sus interacciones. Se utilizan diferentes enfoques metodológicos. Para la construcción de los indicadores compuestos se utiliza un análisis de componentes principales usando correlaciones policóricas, con el fin de analizar cómo se agrupan los departamentos de acuerdo a la salud de sus niños se hace un análisis de cluster jerarquico. Finalmente, con el fin de medir efectos contextuales se utilizan modelos multinivel de regresión lineal ponderados. Los resultados de esta tesis demuestran que el contexto de la comunidad es un componente clave en la determinación de determinantes intermediarios de salud infantil, así como de indicadores de nutrición de los niños, tanto en el mediano como en el largo plazo en Colombia. Por lo tanto, es necesario que los gobiernos municipales y departamentales involucren no sólo a los individuos, sino que también tengan en cuenta el contexto de las comunidades para el desarrollo, ejecución, monitoreo y evaluación de las políticas de cuidado infantil.

La tesi è una raccolta di tre saggi sulla soddisfazione sul posto di lavoro. Il primo capitolo utilizza il dataset panel BHPS per analizzare le differenze di soddisfazione lavorativa dei lavoratori impiegati nel settore nonprofit rispetto agli altri settori. Il secondo considera l'effetto del salario -sia quello del lavoratore che di un gruppo rilevante di riferimento- e degli schemi incentivanti di retribuzione sulla soddisfazione lavorativa. L'analisi si basa sul round 5 dello European Working Conditions Survey (EWCS). Il terzo capitolo analizza e critica la relazione da tempo condivisa in letteratura che i membri di organizzazioni sindacali riportano minore soddisfazione lavorativa rispetto ai non iscritti, mediante l'analisi dei dati dello European Social Survey (ESS),round 3 e 5. / The thesis provides a detailed examination of job satisfaction through three distinct essays. The first chapter uses UK household panel data (the BHPS) to consider differences in job satisfaction in the non-profit sector, compared with other sectors. The second looks at the effect of earnings – that of the respondent and of relevant other people – and of payment systems on job satisfaction. This is based on data from round 5 of the European Working Conditions Survey (EWCS). The third essay seeks to examine and to challenge the long-established finding that union members tend to have lower job satisfaction than non-members, and this is addressed with two rounds of data from the European Social Survey (ESS).

Outcomes Based Education (OBE), has been, since its inception, fraught with problems. OBE in its very nature is complex. To fully embrace this method and ensure its success, schools must be able to make the necessary paradigm shift. This can only be achieved when schools receive relevant and empowering training, support and development. In other words, organisational development must be the key words. The aim of this study is to explore the impact of organisational development interventions on the implementation of OBE. The case study method was employed where it was realised that schools that received organisational development interventions, together with Outcomes Based Education, were able to implement this method with greater understanding, skill, and confidence. The investigation recommends an organisational development design that could be used instead of the cascade model, and provides suggestions on what can be done to ensure a more successful implementation process. / Educational Studies / M. Ed (Education Management)

Made available in DSpace on 2010-04-20T20:51:44Z (GMT). No. of bitstreams: 3 166826.pdf.jpg: 12022 bytes, checksum: 4510aed66898db91f0e043e1b18f6047 (MD5) 166826.pdf: 634031 bytes, checksum: 16a587efdb46955921482c8c5d25bdb9 (MD5) 166826.pdf.txt: 227960 bytes, checksum: 224b048d7483d5c29522eb5b546900d0 (MD5) Previous issue date: 2007-03-14T00:00:00Z / A importância relativa de diversas origens da heterogeneidade do desempenho das firmas tem sido discutida em termos teóricos e testada empiricamente, com ênfase nos efeitos da indústria, da corporação, dos recursos idiossincráticos da firma e do ano. Este trabalho estudou a influência do país sobre o desempenho das firmas. Efeitos significantes e significativos do país e da interação indústria-país foram identificados. A amostra analisada incluiu mais de 80.000 observações e 11.000 firmas em 37 países e 256 indústrias em um período de 10 anos. Um modelo linear hierárquico com quatro níveis permitiu identificar que os efeitos país, indústria e indústria-país têm aproximadamente a mesma importância relativa e, em conjunto, quase a mesma magnitude do efeito firma. A relevância destes efeitos foi consistente entre as diferentes divisões do SIC (Standard Industrial Classification) assim como se manteve estável ao longo do tempo. Um modelo para explicação da variância do desempenho entre países foi proposto e estimado, em caráter exploratório, na análise da relação entre competitividade das nações e rentabilidade das firmas. / The relative importance of different sources of firms’ performance heterogeneity has been widely discussed as well as tested empirically. Industry, corporation, firm and year effects have been the main focus of these studies. This work analzyed another source of performance heterogeneity: country effect. Empirical analysis was based on a Compustal Global sample which included over 80,000 observations and 11,000 firms in 37 countries, 256 industries and a 10 year time span. A cross classified 4- level hierarchical linear model allowed the estimation of country and country-industry interaction effects. These were significant and as relevant as industry effect. Altogether, the effects related to the external environment (country, industry and country-industry) were almost as important as firm effect. Variance composition was consistent across SIC Divisions and remained stable along time. A model adequate to explain performance variance between countries was proposed and estimated – as a methodological illustration – to analyze the relationship between competitiveness of nations and firm performance.