Dataset Information

The Use of Partial Least Squares-Path Modelling to Understand the Impact of Ambivalent Sexism on Violence-Justification among Adolescents.

ABSTRACT: Gender violence is generally conceived as a phenomenon concerning only adults. Nonetheless, it is also perpetrated within teenagers' relationships, as many empirical studies have shown. We therefore have focused our attention on a non-probabilistic sample consisting of 400 adolescents living in Naples (Italy), to study the association between sexism and the justification of violent attitudes. Generally, sexism is recognised as a discriminatory attitude towards people, based on their biological sex. However, it is conventional to talk about sexism as a prejudice against women. The Ambivalent Sexism Inventory (ASI) for adolescents was used to evaluate the two dimensions of ambivalent sexism, i.e., hostile sexism (HS) and benevolent sexism (BS). Moreover, the questionnaire regarding attitudes towards diversity and violence (CADV) was administered to assess participants' attitudes towards violence. A Partial Least Square-Second Order Path Model reveals that girls' ambivalent sexism is affected more by benevolent sexism than hostile sexism. On the contrary, among boys, hostile sexism has a higher impact. Finally, benevolent sexist girls justify domestic violence more than boys do.

SUBMITTER: Fasanelli R

PROVIDER: S-EPMC7400311 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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The Use of Partial Least Squares-Path Modelling to Understand the Impact of Ambivalent Sexism on Violence-Justification among Adolescents.

Fasanelli Roberto R Galli Ida I Grassia Maria Gabriella MG Marino Marina M Cataldo Rosanna R Lauro Carlo Natale CN Castiello Chiara C Grassia Filomena F Arcidiacono Caterina C Procentese Fortuna F

International journal of environmental research and public health 20200710 14

Gender violence is generally conceived as a phenomenon concerning only adults. Nonetheless, it is also perpetrated within teenagers' relationships, as many empirical studies have shown. We therefore have focused our attention on a non-probabilistic sample consisting of 400 adolescents living in Naples (Italy), to study the association between sexism and the justification of violent attitudes. Generally, sexism is recognised as a discriminatory attitude towards people, based on their biological s ...[more]

PMID: 32664458

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Project description:BACKGROUND:Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables. RESULTS:With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data. CONCLUSIONS:msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS.

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Dataset Information

The Use of Partial Least Squares-Path Modelling to Understand the Impact of Ambivalent Sexism on Violence-Justification among Adolescents.

Publications

The Use of Partial Least Squares-Path Modelling to Understand the Impact of Ambivalent Sexism on Violence-Justification among Adolescents.

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