Previous studies that examined the associations among bullying experiences, psychological disturbances, and executive functions showed mixed results. They focused on certain subcomponents of the three sets of variable...
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Previous studies that examined the associations among bullying experiences, psychological disturbances, and executive functions showed mixed results. They focused on certain subcomponents of the three sets of variables and did not reflect the comprehensive relationship. The present study explored these associations through regularized generalized canonical correlation analysis and further examined executive functions and psychological disturbances associated with types of bullying participant roles (i.e., bully, victim, bully-victim, and noninvolved) within a sample of 1469 middle school students (62.5% male). Different subcomponents of executive functions, traditional bullying, cyberbullying, and psychological disturbances were assessed via a battery of questionnaires. Results showed that only one significant canonical variate was found and there were significant canonicalcorrelations among the three sets of variables. Participants with different bullying roles showed significant differences. Specifically, bully-victims showed the worst executive function and the highest levels of psychological disturbance. In contrast, noninvolved had the best executive function and the lowest levels of psychological disturbance. There were significant differences in externalizing problems between bullies and noninvolved, and significant differences in internalizing problems between victims and noninvolved. The findings contributed to the overall understanding of the relationship among bullying experiences, psychological disturbances, and executive functions and provided insights for the development of intervention programs.
Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach rem...
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Partial least squares path modeling has been widely used for component-based structural equation modeling, where constructs are represented by weighted composites or components of observed variables. This approach remains a limited-information method that carries out two separate stages sequentially to estimate parameters (component weights, loadings, and path coefficients), indicating that it has no single optimization criterion for estimating the parameters at once. In general, limited-information methods are known to provide less efficient parameter estimates than full-information ones. To address this enduring issue, we propose a full-information method for partial least squares path modeling, termed global least squares path modeling, where a single least squares criterion is consistently minimized via a simple iterative algorithm to estimate all the parameters simultaneously. We evaluate the relative performance of the proposed method through the analyses of simulated and real data. We also show that from algorithmic perspectives, the proposed method can be seen as a block-wise special case of another full-information method for component-based structural equation modeling-generalized structured component analysis.
The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasiz...
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The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. regularized generalized canonical correlation analysis (RGCCA) and its sparse generalizedcanonicalcorrelationanalysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully ***, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression.
There is a growing need to analyze datasets characterized by several sets of variables observed on a single set of observations. Such complex but structured dataset are known as multiblock dataset, and their analysis ...
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There is a growing need to analyze datasets characterized by several sets of variables observed on a single set of observations. Such complex but structured dataset are known as multiblock dataset, and their analysis requires the development of new and flexible tools. For this purpose, Kernel generalizedcanonicalcorrelationanalysis (KGCCA) is proposed and offers a general framework for multiblock data analysis taking into account an a priori graph of connections between blocks. It appears that KGCCA subsumes, with a single monotonically convergent algorithm, a remarkably large number of well-known and new methods as particular cases. KGCCA is applied to a simulated 3-block dataset and a real molecular biology dataset that combines Gene Expression data, Comparative Genomic Hybridization data and a qualitative phenotype measured for a set of 53 children with glioma. KGCCA is available on CRAN as part of the RGCCA package. (C) 2015 Elsevier B.V. All rights reserved.
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