Dataset Information

Data to model the effect of awareness on the success of IT Governance implementation: A partial least squares structural equation modeling approach (PLS-SEM).

ABSTRACT: This article presents raw inferential statistical data that determine the influence of awareness on the successful implementation of IT Governance. Data were collected from respondents in all regions of Indonesia. Quantitative research methods are used to analyze data. The structured questionnaire was distributed to respondents in all regions of Indonesia who understood the field of IT Governance whose reliability and validity were confirmed. Structural equation modeling (SEM) using Smart PLS software, version 3, is used to present data. SEM path analysis shows an estimate of the relationship of the main constructs in the data. The results obtained from this dataset show a positive relationship between Risk Management, IT Resources, Budget, Stakeholder Involvement, Policy, Business Strategy, Organization, Commitment, Competence, Communication to awareness and consciousness also has a significant influence on the success of IT Governance implementation. However, politics has proven to have a negative and insignificant influence on the awareness and success of implementing IT Governance.

SUBMITTER: Yudatama U

PROVIDER: S-EPMC6717165 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Data to model the effect of awareness on the success of IT Governance implementation: A partial least squares structural equation modeling approach (PLS-SEM).

Yudatama Uky U Hidayanto Achmad Nizar AN Nazief Bobby A A BAA Phusavat Kongkiti K

Data in brief 20190727

This article presents raw inferential statistical data that determine the influence of awareness on the successful implementation of IT Governance. Data were collected from respondents in all regions of Indonesia. Quantitative research methods are used to analyze data. The structured questionnaire was distributed to respondents in all regions of Indonesia who understood the field of IT Governance whose reliability and validity were confirmed. Structural equation modeling (SEM) using Smart PLS so ...[more]

PMID: 31489349

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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.

Dataset Information

Data to model the effect of awareness on the success of IT Governance implementation: A partial least squares structural equation modeling approach (PLS-SEM).

Publications

Data to model the effect of awareness on the success of IT Governance implementation: A partial least squares structural equation modeling approach (PLS-SEM).

Similar Datasets

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