Academic literature on the topic 'Multilevel Regression Analysis'

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

DNA microarray has become a powerful tool in genetics, molecular biology, and biomedical research. DNA microarray can be used for measuring the genotypes, structural changes, and gene expressions of human genomes. Detection and characterization of multilevel, high-throughput microarray genomic data pose new challenges to statistical pattern recognition and machine learning research. In this dissertation, we propose novel computational methods for analyzing DNA copy number changes and learning the trees of phenotypes using DNA microarray data. DNA copy number change is an important form of structural variations in human genomes. The copy number signals measured by high-density DNA microarrays usually have low signal-to-noise ratios and complex patterns due to inhomogeneous composition of tissue samples. We propose a robust detection method for extracting copy number changes in a single signal profile and consensus copy number changes in the signal profiles of a population. We adapt a solution-path algorithm to efficiently solve the optimization problems associated with the proposed method. We tested the proposed method on both simulation and real CGH and SNP microarray datasets, and observed competitively improved performance as compared to several widely-adopted copy number change detection methods. We also propose a chromosome instability measure to summarize the extracted copy number changes for assessing chromosomal instabilities of tumor genomes. The proposed measure demonstrates distinct patterns between different subtypes of ovarian serous carcinomas and normal samples. Among active research on complex human diseases using genomic data, little effort and progress have been made in discovering the relational structural information embedded in the molecular data. We propose two stability analysis based methods to learn stable and highly resolved trees of phenotypes using microarray gene expression data of heterogeneous diseases. In the first method, we use a hierarchical, divisive visualization approach to explore the tree of phenotypes and a leave-one-out cross validation to select stable tree structures. In the second method, we propose a node bandwidth constraint to construct stable trees that can balance the descriptive power and reproducibility of tree structures. Using a top-down merging procedure, we modify the binary tree structures learned by hierarchical group clustering methods to achieve a given node bandwidth. We use a bootstrap based stability analysis to select stable tree structures under different node bandwidth constraints. The experimental results on two microarray gene expression datasets of human diseases show that the proposed methods can discover stable trees of phenotypes that reveal the relationships between multiple diseases with biological plausibility.<br>Ph. D.

Human Papillomavirus (HPV) is a sexually transmitted infection that causes cervical cancer. A nested three-level logistic regression model was introduced in order to investigate whether, in the IARC HPV prevalence surveys, co-infection with different HPV types occurs more or less frequently than expected if the infections are independent from one another. Two random effects, at individual and study-area level, were specified, while the fixed-effect covariates at individual level were age and lifetime number of sexual partners. The Best Linear Unbiased Predictors (BLUP) technique was used to estimate the random components. The predictions of the random effects at individual level are particularly important because they can be considered as a synthetic estimate of all those residual sources of individual variability, i.e., unmeasured risk factors due to sexual habits, that otherwise could not be accounted for. Individual probabilities of being positive for each HPV type are thus estimated, and the expected vs observed number of infections are compared, given the positivity for a different HPV type. Few positive associations (HPV58 with 33 being the strongest) were found in this analyses. However, the majority of HPV types, particularly the two most oncogenic types, HPV16 and 18, that are also included in the prophylactic vaccine, were not associated with one another.

A partire dagli anni ’80, la diffusione dei metodi statistici, abbinata ai progressi nelle capacità computazionali dei personal computers, ha progressivamente facilitato i ricercatori nel testare empiricamente le proprie teorie. Gli economisti sono diventati in grado di eseguire milioni di regressioni prima di pranzo senza abbandonare le proprie scrivanie. Purtroppo, ciò ha portato ad un accumulo di evidenze spesso eterogenee, quando non contradditorie se non esplicitamente in conflitto. Per affrontare il problema, questa tesi fornirà una panoramica dei metodi meta-analitici disponibili in economia. Nella prima parte verranno introdotte le intuizioni alla base dei modelli gerarchici a fattori fissi e casuali capaci di risolvere le problematicità derivanti dalla presenza di osservazioni non indipendenti. Verrà inoltre affrontato il tema dell’errore sistematico di pubblicazione in presenza di elevata eterogeneità tra gli studi. La metodologia verrà successivamente applicata, nella seconda e terza parte, a due diverse aree della letteratura economica: l’impatto del rapporto banca-impresa sulle prestazioni aziendali e il dibattito sulla relazione fra democrazia e crescita. Mentre nel primo caso la correlazione negativa non è influenzata da fattori specifici ai singoli paesi, il contrario è vero per spiegare l’impatto (statisticamente non significativo) delle istituzioni democratiche sullo sviluppo economico. Quali siano questi fattori è però meno chiaro; gli studiosi non hanno ancora individuato le co-variate – o la corretta misurazione di esse – capaci di spiegare questa discussa relazione.<br>Starting in the late 1980s, improved computing performances and spread knowledge of statistical methods allowed researchers to put their theories to test. Formerly constrained economists became able [to] run millions of regressions before lunch without leaving their desks. Unfortunately, this led to an accumulation of often conflicting evidences. To address such issue, this thesis will provide an overview of the meta-analysis methods available in economics. The first paper will explain the intuitions behind fixed and random effects models in such a framework. It will then detail how multilevel modelling can help overcome hierarchical dependence issues. Finally, it will address the problem of publication bias in presence of high between-studies heterogeneity. Such methods will be then applied, in the second and third papers, to two different areas of the economics literature: the effect of relationship banking on firm performances and the democracy and growth conundrum. Results are far-reaching. While in the first case the documented negative relation is not driven by country-specific characteristics the opposite is true for the (statistically insignificant) impact of democratic institutions on economic growth. What these characteristics are is, however, less clear. Scholars have not yet found the covariates - or their suitable proxies - that matter to explain such much-debated relationship.

A partire dagli anni ’80, la diffusione dei metodi statistici, abbinata ai progressi nelle capacità computazionali dei personal computers, ha progressivamente facilitato i ricercatori nel testare empiricamente le proprie teorie. Gli economisti sono diventati in grado di eseguire milioni di regressioni prima di pranzo senza abbandonare le proprie scrivanie. Purtroppo, ciò ha portato ad un accumulo di evidenze spesso eterogenee, quando non contradditorie se non esplicitamente in conflitto. Per affrontare il problema, questa tesi fornirà una panoramica dei metodi meta-analitici disponibili in economia. Nella prima parte verranno introdotte le intuizioni alla base dei modelli gerarchici a fattori fissi e casuali capaci di risolvere le problematicità derivanti dalla presenza di osservazioni non indipendenti. Verrà inoltre affrontato il tema dell’errore sistematico di pubblicazione in presenza di elevata eterogeneità tra gli studi. La metodologia verrà successivamente applicata, nella seconda e terza parte, a due diverse aree della letteratura economica: l’impatto del rapporto banca-impresa sulle prestazioni aziendali e il dibattito sulla relazione fra democrazia e crescita. Mentre nel primo caso la correlazione negativa non è influenzata da fattori specifici ai singoli paesi, il contrario è vero per spiegare l’impatto (statisticamente non significativo) delle istituzioni democratiche sullo sviluppo economico. Quali siano questi fattori è però meno chiaro; gli studiosi non hanno ancora individuato le co-variate – o la corretta misurazione di esse – capaci di spiegare questa discussa relazione.<br>Starting in the late 1980s, improved computing performances and spread knowledge of statistical methods allowed researchers to put their theories to test. Formerly constrained economists became able [to] run millions of regressions before lunch without leaving their desks. Unfortunately, this led to an accumulation of often conflicting evidences. To address such issue, this thesis will provide an overview of the meta-analysis methods available in economics. The first paper will explain the intuitions behind fixed and random effects models in such a framework. It will then detail how multilevel modelling can help overcome hierarchical dependence issues. Finally, it will address the problem of publication bias in presence of high between-studies heterogeneity. Such methods will be then applied, in the second and third papers, to two different areas of the economics literature: the effect of relationship banking on firm performances and the democracy and growth conundrum. Results are far-reaching. While in the first case the documented negative relation is not driven by country-specific characteristics the opposite is true for the (statistically insignificant) impact of democratic institutions on economic growth. What these characteristics are is, however, less clear. Scholars have not yet found the covariates - or their suitable proxies - that matter to explain such much-debated relationship.

This thesis aims at analysing the attitudes of youngsters in Brussels towards sociological minorities. The term “minorities” is used to refer to the main social groups that suffer from subordination and misrecognition by the wider society according to the philosophical theory of recognition: women, lesbians and gay men, and ethnic minorities. Our dataset is composed of a sample of seventy schools in the Brussels Capital Region. In total, three thousand one hundred and twenty one pupils attending in 2007 the last grade of secondary education participated in the study. About half of the sample consists of pupils with a migrant background originating from about 100 different countries. This cultural diversity, reflecting one of the main characteristics of the population of the Brussels Capital Region, is at the centre of the thesis. <p>Because of the hierarchical structure of the sample (pupils aggregated within schools), the culturally diverse population of our sample and the multidimensionality of prejudice, multilevel multivariate linear responses models were performed. In brief, these models allowed us to interpret items regrouped according to their common variation across social (and ethnic) groups and not according to their a priori content similarities. Furthermore, these models allowed us to integrate three different research traditions on prejudice: social psychology on the dimensionality of prejudice, sociology on the impact of socio demographic characteristics on prejudice and school effectiveness research on the role schools may play in reducing pupils’ prejudice. With these models, we could demonstrate the capacity of multilevel techniques to encompass the complexity of prejudice and norms, and to provide an interdisciplinary approach of social processes. <p>Besides the impact of gender and socio economic differences on prejudice, the association between ethnic origin and prejudice was the focus of the analysis at the individual level. Hence, the empirical literature showed that respondents of foreign descent and respondents from the receiving society do not hold similar attitudes towards minorities. This association was investigated in a twofold strategy: after having assessed ethnic differences on the different kinds of prejudice, the explanatory power of possible mediators -such as the experience of group-level institutional discrimination or the bidimensional identification- on this association was tested. The choice of these mediators was influenced by different disciplines of the social sciences. Hence, besides the empirical literature specific to the topic of prejudice, these mediators are derived from theories of political sciences, of sociology of immigration, of social psychology and of cross-cultural psychology. The results showed that these mediators could indeed explain to a large extent ethnic differences on prejudice towards minorities. <p>On the school level, we have shown that the impact schools may have on pupils’ prejudice is a differentiated one. Hence, this impact varies according to both the targets and the dimensions of prejudice. Moreover, besides school institutional characteristics, several contextual characteristics were investigated such as the cultural and social diversity within a school. Our results showed that the impact on prejudice of social and cultural diversity within schools was non-significant. This is, however, most probably related to a masking effect by the specificities of the education landscape in Brussels: differences between schools are huge and homogeneity within schools is important, given that the educational field is highly segregated both in social and in cultural terms. The implications of these results based on an interdisciplinary approach for future research and for policymakers are discussed. <p><br>Doctorat en Sciences politiques et sociales<br>info:eu-repo/semantics/nonPublished

Increasing urbanization across the world has led to increased research on ecology in urban settings in the last decade. Urban ecological studies have investigated both impacts of urban development on native ecosystems and the dynamics of urban environments themselves as ecosystems. In both areas of research, streams of urban areas have an important part to play because their position in the landscape makes these ecosystems particularly vulnerable to impacts associated with landcover change. Urban stream ecosystems are affected by multiple stressors and their effects are synthetized in the “urban stream syndrome” theorized in Walsh et al. 2005. Consistent symptoms of the urban stream include flashier hydrograph, elevated concentrations of nutrients and contaminants, altered channel morphology and stability and reduced biodiversity, with increased tolerant species. These ecological effects are often accompanied by other symptoms not observed in all urban areas, such as reduced baseflow and increased suspended solids. In impaired freshwater ecosystems, it is known that ecological integrity can be subdivided into two components, structural and functional integrity. Structural indicators of ecosystem health may be defined as the qualitative and quantitative composition of biological communities. Fish and macroinvertebrate assemblages have been the main focus for assessing structural integrity, although a variety of alternative targets such as benthic algal communities, protozoans, and macrophytes have also been used. Macroinvertebrate assemblages play a central ecological role in many stream ecosystems and are among the most ubiquitous and diverse organisms in fresh waters. Macroinvertebrates are easily recognizable and classifiable and some taxa are representative of every different habitat and condition (sensibility or tollerance to pollution and environmental changes) and so it is easy to aggregate results of macrobenthos analyses into synthetic indeces (such as STAR_ICMi). Function indicators instead, that have a much shorter history, are complementary to structural indicators and refer to the autoecology of biological communities and ecological attributes within the ecosystem in which they are located. In Water Framework Directive, develops by European Union to advance more comprehensive water legislation, the river basins with above mentioned characteristics are defined heavily modified water bodies (HMWBs). HMWBs have unique water quality characteristics that, in most cases, are comparatively different from normal stream conditions upstream of the discharge or at regional reference sites. Reference sites are commonly used in bioassessment studies to identify undisturbed or pristine conditions and hence management targets. The increase of urban development often results in the absence of reference sites in HMWBs and this leads in difficulties to define a target condition for restoring urban stream sites. The WFD requires that all waters achieve good ecological status and only slightly deviate from natural reference conditions, which has become the main objective of most restoration projects in Europe. The ecological status is quantified in many European member states using multi-metric indices, and good ecological status corresponds to a specific score value. However, there is little information on the limiting effects of large-scale pressures on the biological metrics. As suggested by numerous research works, the scale to approach river investigations can be considered from the microhabitat level to basin scale. A river may be analysed across a variety of levels, which can be ordered into a hierarchy, with different degree of sensitivity and recovery time. Impacts of human activity are becoming increasingly unacceptable to a global community that focuses on environmental sustainability. Therefore, whole catchment approach management have been developed to preserve stream ecosystems or restore damaged ecosystems, and mitigate against further damage. The individuation of which factors set limits to biological community development and their respective values is of great interest for river managers and river restoration campaigns. In urban streams is usually hard to assess causal relationships among specific stressors and responses of biological communities using the most common statistical tools. Using macroinvertebrate assemblages as biological indicators in micro- and mesohabitat level works, applied statistics may be viewed as an elaboration of the linear regression model and associated estimation methods of least square. In whole basin analyses, data variability is high and classic statistical approach may even become uninformative. Moreover, the effects of many stressors (local and global) may influence simultaneously the response of biological community leading to a decrease of statistical model fit. In this perspective, alternative statistical approaches are necessary. In 1978 Koenker and Bassett theorized the quantile regression in econometric sciences, a robust alternatives to the least squares estimator for the linear model. Other authors introduced this kind of regression in ecology declaring that quantile regression allows the various stressors to be considered as “constraints” to the distribution of biological communities, without compromising the model causal relationship. Aim of this work is to assess the overall pressure of human activities in river basins of Lombardy piedmont and floodplain area and to relate changes in the biological communities as a result of habitat loss and changes in both hydromorphological and physico-chemical properties. In this area, many rivers have a “channelized” nature with straight section, clear of river bank tree and uniform bed morphology. Flow regulation and modification have also been widespread. The quantity and timing of water availability have been altered for irrigation and industrial purposes, through the construction of dams and reservoir for water supply. Changes in water quality are also common, in particular in lowland areas where urbanization and agriculture are more strong. For these reasons, the work is focusing on different scale (microhabitat, site, river reach and basin levels) to have a better resolution and understanding of existing dynamics among structural and functional indicators and pressures in impaired environments. These areas undergo different stresses (habitat loss, changes in physico-chemical properties and changes in flow) that affect the integrity of the ecosystems. Assessing the condition of ecosystems is a prerequisite to reduce the induced anthropogenic pressure. Decision-making in river restoration programs can also be helped by multilevel kind of information.

Students transitioning into college from public school require more than just academic readiness; they also need the personal attributes that allow them to successfully transition into a new community (Braxton, Doyle, Hartley III, Hirschy, Jones, & McLendon, 2014; Nora, 2002; Nora, 2004; Tinto, 1975). Rural students have a different educational experience than their peers at schools in suburban and urban locations (DeYoung & Howley, 1990; Gjelten, 1982). Additionally, the resources, culture, and educational opportunities at rural schools also vary among different types of rural communities. Although some studies have examined the influence of rural students' academic achievement on college access and success, little research has analyzed the relationship between students of different types of rural communities and their persistence in post-secondary education. This study examined the likelihood for college-going students from three different types of rural communities to successfully transition into and persist at a four-year residential college. Multilevel logistic modeling was used to analyze the likelihood for students to persist in college for up to two academic years based on whether they were from rural tourist communities, college communities, and other rural communities. The analysis controlled for a variety of student and high school factors. Findings revealed that student factors related to poverty and academic readiness have the greatest effects, while the type of rural community has no significant influence on college persistence.

In the last three decades recursive residuals and estimation have received extensive attention as important and powerful tools in providing a diagnostic test of the structural change and functional misspecification in regression models. Recursive residuals and their relationship with recursive estimation of regression parameters have been developed for fixed effect models. Such residuals and estimation have been used to test the constancy of regression models over time and their usage has been suggested for almost all areas of regression model validation. These recursive techniques have not been developed for some of the more recent generalisations of Linear Models such as Linear Mixed Models (LMM) and their important extension to Generalised Linear Mixed Models (GLMM) which provide a suitable framework to analyse a variety of special problems in an unified way. The aim of this thesis is to extend the idea of recursive residuals and estimation to Mixed Models particularly for LMM and GLMM. Recurrence formulae are developed and recursive residuals are defined.