Early childhood education and family involvement have been shown to provide a positive impact on students' academic achievement regardless of socioeconomic circumstances and background. They have been regarded as two of the most important protective factors in maximizing outcomes for children at risk, especially those from low-income backgrounds. The overall objective of this study was to examine how family involvement changes over time, whether it predicted cognitive outcomes for preschool populations, and potential variables that mediate the relationship between family involvement and outcomes. Data from the Head Start Impact Study conducted through the Department of Health and Human Services (DHHS) were used to answer the research questions. Exploratory and Confirmatory analysis revealed three dimensions of home involvement based on survey items: involvement related to literacy, numeracy, and family resources. Growth curve models suggested increased levels of involvement as children progressed from preschool to first grade. Higher levels of involvement for Literacy for observed for children in Head Start during the first data collection follow-up. The types of involvement were significantly related to cognitive scores as measured by the PPVT-III and WJ-III Achievement. Parenting styles acted as a mediator between involvement and cognitive outcomes. Implications for policy and practice related to transition services are discussed. / School Psychology

Alguns aspectos devem ser considerados nos trabalhos com frutíferas como o ponto adequado de colheita e estratégias de conservação, sendo fundamental a análise do crescimento e desenvolvimento dos frutos. Vários tipos de modelos estatísticos podem descrever os processos de crescimento envolvidos no sistema de produção vegetal, sendo os modelos não lineares considerados mais adequados para esse tipo de estudo. Para que os resultados obtidos no ajuste do modelo sejam válidos é necessário verificar a qualidade de ajuste por meio de uma análise dos resíduos. A distribuição dos resíduos ordinários para os modelos de regressão não linear, é matematicamente trabalhosa e os critérios de diagnóstico são falhos, principalmente em pequenas amostras. Diante de tal fato, Cook e Tsai (1985) definiram o resíduo projetado. O comportamento dos resíduos projetados é melhor do que o dos resíduos ordinários, pois suas propriedades são mais próximas das correspondentes ao resíduo ordinário da regressão normal linear. O ganho é substancial se o teste para a medida de não linearidade for significativo. Por meio dos ajustes dos modelos logístico e de Gompertz aos dados de comprimento de peras foi realizada a análise dos resíduos ordinários e dos resíduos projetados, a fim de evidenciar suas vantagens. Os resíduos projetados se mostraram viáveis para diagnóstico dos modelos e descartaram possíveis tendências apresentadas pelos resíduos normalizados. / Some aspects must be considered in the works with fruit tree such as the appropriate point of harvest and conservation strategies, being fundamental the analysis of growth and fruit developments. Several types of statistical models can describe the growth processes involved in plant production system and the non-linear models are considered more suitable for this type of study. For the results obtained from the model fit to be valid is necessary to check the quality of fit through a residual analysis. The distribution of the ordinary residuals in the non-linear regression models is mathematically complicated and the diagnostic criteria are not precise, mainly in small samples. To overcome this fact, Cook and Tsai (1985) defined the projected residual. The behaviour of the projected residuals is better than the ordinary residuals since their properties are closer to those corresponding to the ordinary residuals for the normal linear regression. The gain is substantial if the test for non-linearity is significant. Through the fit of the logistic and Gompertz models to the pears length data, it was performed an analysis of the ordinary and projected residuals in order to demonstrate their advantages. The projected residuals showed more feasible for the diagnostic of the models and ruled out possible trends presented by normalized residuals.

According to previous research, internalizing and externalizing problems tend to be comorbid or co-occur at different ages in development (Angold, Costello, & Erkanli, 1999). The question that this dissertation addresses is how and why internalizing and externalizing problems, two disorders that represent separate forms of psychopathology, co-occur in children. This is an important question for the developmental psychopathology perspective because an appreciation of the concept of co-occurrence is essential for explaining the development and taxonomy of internalizing and externalizing psychopathology, and for understanding the etiology and course of these symptoms (Achenbach, 1990). Attempts to explain co-occurrence have proposed that co-occurring psychopathology might represent distinct, meaningful syndromes (Angold & Costello, 1992; O’Connor et al., 1998), and in support of this idea, evidence of the existence of pure and co-occurring internalizing and externalizing problems has been found (Keiley et al., 2003). However, no previous study has identified heterogeneous developmental patterns of pure or combined internalizing and externalizing problems within a dynamic framework by taking trajectories of change into account. This dissertation uses data from the NICHD study of Early Child Care to explore the co-occurrence between internalizing and externalizing problems from age 2 to 12 with the use of Latent Class Growth Analysis. The sample included 1232 children (52% male). Different groups of children exhibiting low/normative, pure internalizing, pure externalizing, and co-occurring internalizing and externalizing problems across the 10 year period were identified. The higher risk groups deviated from the low/normative group in terms of antecedents, SES risk, medical risk, difficult temperament, and home environment. Moreover, children who exhibited pure moderate externalizing problems, and children who exhibited chronic externalizing problems, with and without co-occurring internalizing problems, engaged in more risky behaviors and were more likely to have friends who also engaged in risky behaviors. Furthermore, the pure chronic externalizing group and the groups scoring high on internalizing problems, with and without co-occurring externalizing problems, were more asocial with peers. Finally, children exhibiting chronic co-occurring externalizing and internalizing problems were more excluded by peers in comparison to the rest of the sample’s population.

Understanding the development of adolescent sexual risk behavior is complicated by the co-occurrence of sexual risk with substance use and delinquency, conceptualized as “problem behavior syndrome,” with common causes and influences underlying all three problem behaviors (Jessor & Jessor, 1977). Explaining the development of sexual risk becomes even more complex given the changing patterns of adaptation and maladaptation over the course of adolescence (Sroufe & Rutter, 1984). Research also suggests that multiple pathways may forecast adolescent engagement in sexual risk behavior, underscoring the ideas of equifinality and multifinality in developmental psychopathology (Cicchetti & Rogosh, 1996). To understand the diverse nature of sexual risk taking, researchers must identify these pathways and disentangle co-occurring problem behaviors from sexual risk. Revealing the course of sexual risk taking and the early risk and protective processes through which problem behavior develops allows researchers to identify the developmental periods that would be most amenable to intervention efforts (Rolf et al., 1990). Using data from the National Longitudinal Survey of Youth (NLSY79), this study aimed to disentangle problem behavior syndrome by identifying the unique developmental pathways of adolescent sexual risk, alcohol use and delinquency. This study also investigated how early adolescent processes of risk and protection were associated with the growth of these risk behaviors during adolescence. Using a developmental psychopathology and resilience framework, risk trajectories were measured with adolescents aged 15 to 24, and antecedents were measured with early adolescents ages 10 to 14 (N= 1778). Using Latent Class Growth Analyses (LCGA), joint trajectory analyses revealed five distinct adolescent risk taking groups: high sex and alcohol, moderate problem behavior, problem behavior, alcohol-only, and alcohol and delinquency experimentation. Early adolescent externalizing problems were particularly important in understanding adolescent risk group membership. The co-occurrence between sexual risk and alcohol use, the diversity of problem behavior syndrome, and potential intervention and prevention efforts are discussed.

There is significant interest by the food industry in applying milder processing conditions. A major area of research within predictive modelling has been the search for models which accurately predict the effect of combining multiple processes or hurdles. For a mild process, which has temperature as the major microbial injury step, the effect of the other combined hurdles in inhibiting growth of the injured organisms must be understood. The latter means that the inoculum size dependency of the time to growth must also be fully understood. This essentially links injury steps with the potential for growth. Herein, we have been developing the use of optical density (O.D) for obtaining growth rates and lag times using multiple inocula rather than using the traditional methods which use one single inoculum. All analyses were performed in the Bioscreen analyser which measures O.D. The time to detection (TTD) was defined as the time needed for each inoculum to reach an O.D=0.2 and O.D was related to microbial numbers with simple calibration curves. Several primary models were used to predict growth curves from O.D data and it was shown that the classic logistic, the Baranyi and the 3-phase linear model (3-PLM) were the most capable primary models of those examined while the modified Gompertz and modified logistic could not reproduce TTD data. Using the Malthusian approximation of the logistic model the effect of mild temperature shifts was studied. The data obtained showed that for mild temperature shifts, growth rates quickly changed to the new environment without the induction of lags. The growth of Listeria monocytogenes, Salmonella Typhimurium and Escherichia coli was studied at 30⁰C and/or 37⁰C, in different NaCl concentrations, pH and their combinations. The classical 3-parameter logistic with lag model was rearranged to provide the theoretical foundation for the observed TTD and accurate growth rates and lag times could be estimated. As the conditions became more unfavourable, the lag time increased while the growth rate decreased. Also, the growth rate was found to be independent from the inoculum size; the inoculum size affected only the TTD. The Minimum Inhibitory Concentration (MICNaCl and MICpH) was calculated using the Lambert and Pearson model (LPM) and also the Growth/No Growth (G/NG) interface was determined using combinations of NaCl and pH. These data were transformed in rate to detection (RTD) and fitted with a response surface model (RSM) which was subsequently compared with the Extended LPM (ELPM). The LPM and the ELPM could analyse results from individual and combined inhibitors, respectively. Following a mild thermal process a lag due to thermal injury was also induced, the magnitude of which was dependent on the organism and environmental conditions; the observed distribution of the lags appeared, in general, to follow the Log-normal distribution. After the lag period due to injury, growth recommenced at the rate dictated by the growth environment present. Traditional growth curves were constructed and compared with the data obtained from the Bioscreen under the same conditions. From the results obtained, it can be suggested that the increased lag times and growth rates obtained from the traditional plate counts compared with the values obtained from the Bioscreen microbiological analyser, might be an artifact of the plating method or may be due to the use of the modified Gompertz to study the growth. In conclusion, O.D can be used to accurately determine growth parameters, to give a better understanding and quantify the G/NG interface and to examine a wealth of phenomena such as fluctuating temperatures and mild thermal treatments. The comparison between the traditional growth curves against the data obtained from the Bioscreen showed that the TTD method is a rapid, more accurate and cheaper method than the traditional plate count method which in combination with the models developed herein can offer new possibilities both to the research and the food industry.

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This research examines the viability of a need-based approach that models the age-trajectories of healthcare utilization. We propose a fundamentally different way of treating age in modeling healthcare use. Rather than treating age as a need indicator, we refocus modeling efforts to predicting the age-trajectories of healthcare use. Using inpatient hospital utilization data from the Discharge Abstract Database, first, we model the age-trajectories of the rate of hospital use employing a common functional form. Second, we assess variation in these age-trajectories using growth curve modeling. Third, we explain variation in these age-trajectories using census variables. Our analysis shows that the regional variation in the age-trajectories of the rate of inpatient hospital use is sufficient to justify this method, and could be partially explained using census variables. This indicates that modeling age-trajectories of healthcare use is advantageous, and the current need-based approach may benefit from this new modeling strategy.

Veterinarians often perform controlled experiments in which they inoculate animals with infectious diseases. They then monitor the transmission process in infected animals. The aim of such experiments can be to assess vaccine effects. The fitting of individual-level models (ILMs) to the infectious disease data, typically achieved by means of Markov Chain Monte Carlo (MCMC) methods, can be computationally burdensome. Here, we want to see if a vaccination effect can be identified using simpler regression-type models rather than the complex infectious disease models. We examine the use of various logistic growth curve models, via a series of simulated experiments in which the underlying true model is a mechanistic model of infectious disease spread. We want to investigate whether a vaccination effect can be identified when only partial epidemic curves are observed, and to assess the performance of these models when experiments are run with various sets of observational times.

Made available in DSpace on 2015-03-26T13:32:22Z (GMT). No. of bitstreams: 1 texto completo.pdf: 434501 bytes, checksum: 0ab5e3021a0cbd11c8c868f09b269488 (MD5) Previous issue date: 2014-02-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The analysis of growth curves of animals has been widely used to increase the efficiency of beef cattle ranching. Related to growth curves with nonlinear mixed models strategic, studies have strategic applications in genetic improving programs in defining selection criteria for earliness and weight gain, aimed at, that for each individual is estimated a random coefficient, facilitating identification and selection of more efficient animals based on the coefficients. This methodology considers the variability between and within individuals. The objective of this study was to evaluate the efficiency of the adjustment of growth curves by nonlinear mixed models. Nonlinear models, Michaelis-Menten Modified, Logistic, von Bertalanffy, Gompertz, Richards and Brody, were fitted, with and without the incorporation of random effects for analysis of growth in beef cattle Tabapuã race. For comparison between fixed and mixed models were used the following adjustment quality evaluators: Akaike s information criterion (AIC), Bayesian information criterion (BIC), mean absolute deviation (DMA), mean square error (MSE) and coefficient of determination (R2). The use of nonlinear mixed model was efficient to describe bovine growth curves. / A análise de curvas de crescimento de animais tem sido muito utilizada para aumentar a eficiência da pecuária de corte. Estudos relacionados a curvas de crescimento com modelos não lineares mistos podem ter aplicações estratégicas em programas de melhoramento genético na definição de critérios de seleção para precocidade e ganho de peso, tendo em vista, que para cada indivíduo é estimado um coeficiente aleatório, facilitando a identificação e seleção de animais mais eficientes com base nos coeficientes. Essa metodologia considera a variabilidade entre e dentro de indivíduos. O objetivo deste trabalho foi avaliar a eficiência do ajuste de curvas de crescimento através de modelos não lineares mistos. Foram ajustados os modelos não lineares Michaelis-Menten Modificado, Logístico, von Bertalanffy, Gompertz, Richards e Brody, com e sem a incorporação de efeitos aleatórios para análise de curva de crescimento de bovinos de corte da raça Tabapuã. Para comparação entre modelos fixos e mistos foram utilizados os seguintes avaliadores de qualidade de ajuste: critério de informação de Akaike (AIC), critério de informação bayesiano (BIC), desvio médio absoluto (DMA), erro quadrático médio (EQM) e coeficiente de determinação (R2). A utilização de modelos não lineares mistos foi eficiente para descrever curvas de crescimento de bovinos.

Made available in DSpace on 2014-06-11T19:26:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-02-25Bitstream added on 2014-06-13T19:33:22Z : No. of bitstreams: 1 boligon_aa_me_jabo.pdf: 786716 bytes, checksum: 55a12f5a16a7a8f8acd0c213b48d9308 (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Foram estimados parâmetros genéticos para pesos do nascimento à idade adulta de animais da raça Nelore por meio de análises uni, bi e multicaracterísticas e modelos de regressão aleatória. Os dados utilizados são de animais nascidos de 1975 e 2002, provenientes de 8 fazendas participantes do Programa de Melhoramento Genético da Raça Nelore (PMGRN). Os pesos foram obtidos do nascimento aos 8 anos de idade. Nas análises uni, bi e multicaracterísticas foram utilizados pesos em idades padrão como nascimento, desmama, ano, sobreano e aos 2, 3 e 5 anos de idade. Também foram realizadas análises utilizando o peso mais próximo aos 4,5 anos de idade como indicativo de peso adulto, considerando uma única medida a partir de 2, 3 e 4 anos de idade ou como registros repetidos de pesos a partir dessas mesmas idades. Nas análises de regressão aleatória, foram utilizados pesos de fêmeas do nascimento aos 8 anos de idade, considerando como variáveis independentes polinômios de Legendre da idade na data da pesagem. A variância residual foi modelada por meio de classes variando de 1 a 5. Foram utilizados 8 modelos de coeficientes de regressão aleatória para os efeitos direto e materno de animal, e de ambiente permanente de animal e materno. O modelo multicaracterística, incluindo registros de pesos ao desmame e à seleção é o mais indicado para a avaliação genética de pesos pós-desmama. Em avaliações genéticas para a característica de peso adulto, o emprego de modelos de repetibilidade, considerando pesos a partir de 3 anos de idade, seria o mais adequado em relação à utilização de medida única... / Weight records of Nelore cattle from birth to mature age were analyzed using univariate, bivariate, multivariate and random regression models. Records of Nelore cattle born from 1975 to 2002, from 8 herds participating in the Nelore Cattle Breeding Program (NCBP) were used. The weights were obtained from birth to 8 years of age. Weights at birth, weaning, yearling, 18 months and 2, 3 and 5 years of age were analyzed using univariate, bivariate and multivariate models. Also, as indicative of the mature weight, the weight closest to 4.5 years of age, was analyzed considering only one record or repeated records obtained from 2, 3 and 4 years of age. For random regression models age of cow varied from birth to 8 years. Direct and maternal genetic and, animal and maternal permanent environmental variances were modeled by random regression on Legendre polynomials of age at recording, with order of fit from 3 to 6 and a total of 8 models. Residual variances were modeled by a step function with 1 or 5 classes. The multivariate model including weight records at weaning and at selection age is the most indicated for genetic evaluation of pos-weaning weights. For genetic evaluation of mature weight to use repeated records obtained from 3 years of age is better than only one record per animal. The random regression models were able to model changes of variances with age adequately, with parameter estimates similar to those obtained by multivariate analyses. The model with direct and maternal genetic effects, animal and maternal permanent environmental effects ajusted by quartic, cubic, sixth and cubic polynomials, respectively, and residual variances modeled by 5 classes, was the most adequate to describe the covariance structure of the data...(Complete abstract, click electronic access below)