School Effectiveness Research (SER) is concerned with efforts to better understand the effectiveness enhancing relationship between student and school variables and how these variables primarily influence academic achievement (Scheerens, 2004). However, one identified methodological shortcoming in SER is the absence of cross-cultural perspectives (Kyriakides, 2006). This is a concern as what may prove effective in one nation does not necessarily mean that it can be easily and seamlessly imported into another with the same results. This study looked at the relationships between science self-beliefs and academic achievement in science across all nations who participated in the Programme for International Student Assessment (PISA) in 2006. It further explored the variance accounted for by cultural, social and economic capital (the elements of the PISA socioeconomic status variable) for each country in PISA 2006 when predicting scientific literacy. Lastly, it used hierarchical linear modeling (HLM) to analyze data from PISA 2006 for nations experiencing high rates of immigration (i.e., Germany, Spain, Canada, the United States, Australia and New Zealand). The outcome measures used for these countries were achievement scores in science, mathematics and reading. The variables examined at the student level were science self-efficacy, science self-concept, immigrant status and socioeconomic status. The variables examined at the school level were student level aggregates of school proportion of immigrants and school socioeconomic status. In the correlation analysis between science literacy and either science self-concept of science self-efficacy, findings suggest that at the student level, students with both higher science self-concept and higher science self-efficacy tend to achieve higher academically. However, at the country level the relationship was negative between self-concept and academic achievement in science (i.e., countries with higher science self-concept tend to achieve lower on scientific literacy). When the variables that comprised each of the cultural, social, and economic components of SES were regressed on scientific literacy for the PISA sample, cultural capital accounted for 16% of the variance in scientific literacy scores compared to 14% for social capital, 13% for the composite Economic Social and Cultural Status (ESCS), and 12% for economic capital. In the HLM null models, the intraclass correlations for the all countries except for Germany ranged from .16 to .29 (Germany’s was between .57 and .68). In the final models, at level-1 country, immigrant status tended to negatively influence achievement (i.e., non-native students are predicted to have lower performance), while science self-efficacy and science self-concept positively influenced achievement. The student level ESCS variable also impacted achievement positively. At the school level, level-2, school mean ESCS or school proportion of immigrants were found to significantly influence the level-1 predictors; however, a good deal of variability across nations was observed. The findings from this study demonstrate that there are some distinct national differences in the relationships between science self-beliefs, immigrant status and academic achievement.

Thesis (Ph.D.) -- University of Western Sydney, 2004. / "A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy" Bibliography : leaves 171-186.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 151-153).

Thesis (Ph.D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Mavris, Dimitri; Committee Member: Bishop, Carlee; Committee Member: Costello, Mark; Committee Member: Nickol, Craig; Committee Member: Schrage, Daniel

Thesis (PH. D.)--Michigan State University. Measurement and Quantitative Methods, 2008. / Title from PDF t.p. (viewed on Sept. 8, 2009) Includes bibliographical references (p. 85-89). Also issued in print.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Akihito Kamata, Florida State University, College of Education, Dept. of Educational Psychology and Learning Systems. Title and description from dissertation home page (viewed June 7, 2006). Document formatted into pages; contains x, 135 pages. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 108-118).

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 105-107).

FundaÃÃo de Amparo à Pesquisa do Estado do Cearà / Recent studies have shown the importance of self-employment as an alternative to wage employment and a response to unemployment. In this context, this paper aims to examine empirically the determinants that influence the workersâ choice between selfemployment and wage worker, emphasizing the influence of neighborhood effects on this choice. To accomplish this task itusesa Multilevel Model for Discrete Choice and data set about individualsâ labor market drawn from the Survey on Employment and Unemployment (Pesquisa de Emprego e Desemprego â PED), which covers the Metropolitan Area of Fortaleza for the years 2009 and 2010. The use of the Multilevel Model for testing the effects of neighborhoods on individuals, and the own database are contributions of this research. Among other results it was found that decision models in the labor markets excluding the possibility of social interaction in neighborhoods, may produce biased results. This effect was highly significant and positive indicating that a worker whose neighborhood has a high share of self-employment is also more likely to choose this type of work than another worker with the same characteristics, but who lives in another neighborhood with prevalence wage employees. / Estudos recentes tÃm evidenciado a importÃncia do status de ocupaÃÃo autÃnoma por este representar uma alternativa ao emprego remunerado e uma possibilidade de resposta ao desemprego. Nesse contexto, esta dissertaÃÃo tem por objetivo analisar empiricamente os determinantes que influenciam a escolha do indivÃduo em ser autÃnomo (vis-Ã-vis, ser assalariado), com Ãnfase para o efeito que o contexto social de vizinhanÃa pode exercer sobre esta escolha. Para isto utilizou-se modelos de MultinÃvel para escolha discreta, sendo as informaÃÃes dos indivÃduos no mercado de trabalho extraÃdas da Pesquisa de Emprego e Desemprego â PED da RegiÃo Metropolitana de Fortaleza para os anos de 2009 e 2010. O uso do modelo MultinÃvel para testar efeitos de vizinhanÃa e a prÃpria base de dados sÃo contribuiÃÃes desta pesquisa. Entre outros resultados verificou-se que modelos de decisÃo no mercado de trabalho que excluem a possibilidade de interaÃÃo social em vizinhanÃa, podem gerar resultados viesados. Este efeito mostrou-se altamente significativo e positivo indicando que um trabalhador, cuja vizinhanÃa possui uma alta participaÃÃo de trabalho autÃnomo, tem uma probabilidade maior de tambÃm escolher este tipo de trabalho, do que outro trabalhador que tem as mesmas caracterÃsticas, mas que mora em outra vizinhanÃa com prevalÃncia de trabalhadores assalariados.

Modelo multinível é uma ferramenta estatística cada vez mais popular para análise de dados com estrutura hierárquica. O objetivo deste trabalho é propor um método para realizar a predição de observações de novos grupos usando modelos de regressão logística multinível com 2 níveis. Além disso, é apresentado e comparado dois métodos de estimação para o modelo multinível: Quase-verossimilhança Penalizada (QVP) e Quadratura de Gauss-Hermite (QGH). A idéia central está baseada no trabalho de (Jiang e Lahiri, 2006) no qual se propõe o uso do chamado melhor estimador empírico para o efeito aleatório. Através deste estimador, utilizou-se a parte fixa do modelo em conjunto com uma estimativa do desvio padrão do efeito aleatório para fazer a predição de observações de novos grupos, encontrando a probabilidade estimada dessa observação apresentar o evento de interesse, dadas suas características. / Multilevel model is an statistical tool which is becoming more and more popular in data analysis with hierachical structure. The purpose of this dissertation is to present a method to make a prediction of new group observation in multilevel logistic regression models with 2 levels. Besides, were presented and compared two estimation methods for multilevel model: Penalized Quase-likelihood and Gauss-Hermite Quadrature. The central idea is based on the paper of Jiang and Lahiri (2006), which is presented the empirical best estimator for the random effect. Through this estimator was used the fixed part of the model with an estimative of the standard deviation of the random effect to find the estimated probability of this observation presenting the target event, in accordance with its characteristic.