scholarly journals Towards Reducing the Multidimensionality of Olap Cubes Using the Evolutionary Algorithms and Factor Analysis Methods

2016 ◽  
Author(s):  
Sami Naouali
Keyword(s):  
Factor Analysis ◽  
Evolutionary Algorithms ◽  
Analysis Methods

The Complementary Use of Cluster and Factor Analysis Methods

1983 ◽  
Vol 51 (4) ◽  
pp. 165-168 ◽  
Author(s):  
Bernard S. Gorman ◽  
Louis H. Primavera
Keyword(s):  
Factor Analysis ◽  
Analysis Methods

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

2021 ◽  
Vol 13 (37) ◽  
pp. 4188-4219
Author(s):  
Peter D. Wentzell ◽  
Cannon Giglio ◽  
Mohsen Kompany-Zareh
Keyword(s):  
Analytical Chemistry ◽  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Critical Comparison ◽  
Analysis Methods ◽  
Analysis Tools ◽  
Components Analysis

Principal components analysis (PCA) is widely used in analytical chemistry, but is only one type of broader range of factor analysis tools that are described in this article.

The Empirical Verification of an Assignment of Items to Subtests

2008 ◽  
Vol 68 (6) ◽  
pp. 923-939 ◽  
Author(s):  
Ilse Stuive ◽  
Henk A. L. Kiers ◽  
Marieke E. Timmerman ◽  
Jos M. F. ten Berge
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Common Factor ◽  
Model Error ◽  
Unique Variance ◽  
Empirical Verification ◽  
Multiple Group ◽  
Analysis Methods ◽  
Confirmatory Factor

This study compares two confirmatory factor analysis methods on their ability to verify whether correct assignments of items to subtests are supported by the data. The confirmatory common factor (CCF) method is used most often and defines nonzero loadings so that they correspond to the assignment of items to subtests. Another method is the oblique multiple group (OMG) method, which defines subtests as unweighted sums of the scores on all items assigned to the subtest, and (corrected) correlations are used to verify the assignment. A simulation study compares both methods, accounting for the influence of model error and the amount of unique variance. The CCF and OMG methods show similar behavior with relatively small amounts of unique variance and low interfactor correlations. However, at high amounts of unique variance and high interfactor correlations, the CCF detected correct assignments more often, whereas the OMG was better at detecting incorrect assignments.

scholarly journals PENGUJIAN VALIDITAS DAN RELIABILITAS SKALA DUKUNGAN IMAN

2019 ◽  
Author(s):  
Christiany Suwartono ◽  
Junianawaty Suhendra ◽  
Sylvia Soeherman ◽  
Aileen P. Mamahit
Keyword(s):  
Factor Analysis ◽  
Young People ◽  
Young Adulthood ◽  
Internal Consistency ◽  
Role Models ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Analysis Methods ◽  
Convenience Sampling ◽  
Adolescence And Young Adulthood

Abstract – The formation of faith in adolescents and young people is rooted in their interaction with parents, both father, and mother, as well as friends. Adolescence and young adulthood are periods of seeking faith in various aspects of life. However, there has not been a measurement of the extent to which adolescents and young people regard parents and friends as role models of their faith. This study was aimed to the Perceived Faith Support - Parents and Friends Scale to be the Scale of Faith Support. The study was conducted through convenience sampling, involving 1,390 participants from three major islands in Indonesia. The results of this adaptation proved to be reliable with internal consistency methods, especially Cronbach's Alpha and to be valid with factor analysis methods. Validation with age criteria is proven by the support of faith from the father and mother, but not with friends. The Faith Support Scale is argued to be fit for both theoretical and practical purposes. The development and application of this scale are further discussed. Abstrak — Pembentukan iman pada remaja dan kaum muda merupakan hasil interaksi mereka dengan orang tua, baik ayah maupun ibu, juga teman. Masa remaja dan kaum muda merupakan masa pencarian keyakinan (iman) di berbagai aspek kehidupan. Namun, belum ada pengukuran mengenai sejauh mana remaja dan kaum muda menganggap orang tua dan teman sebagai teladan iman dirinya. Studi ini bertujuan untuk mengadaptasi skala Perceived Faith Support – Parents and Friends menjadi Skala Dukungan Iman. Penelitian dilakukan dengan teknik convenience sampling dan melibatkan 1,390 partisipan dari tiga pulau besar di Indonesia. Hasil adaptasi ini terbukti reliabel dengan metode konsistensi internal, khususnya Cronbach’s Alpha dan valid dengan metode faktor analisis. Validasi dengan kriteria usia terbukti pada dukungan iman dari ayah dan ibu, namun tidak dengan teman. Skala Dukungan Iman ini siap digunakan dalam konteks penelitian maupun praktis. Pengembangan dan aplikasi skala ini dibahas lebih lanjut. 

scholarly journals Application of Factorisation Methods to Analysis of Elemental Distribution Maps Acquired with a Full-Field XRF Imaging Spectrometer

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7965
Author(s):  
Bartłomiej Łach ◽  
Tomasz Fiutowski ◽  
Stefan Koperny ◽  
Paulina Krupska-Wolas ◽  
Marek Lankosz ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Energy Resolution ◽  
Principal Component ◽  
Elemental Distribution ◽  
Imaging Data ◽  
Imaging Spectrometer ◽  
Electron Multiplier ◽  
Full Field ◽  
Limited Energy ◽  
Analysis Methods

The goal of the work was to investigate the possible application of factor analysis methods for processing X-ray Fluorescence (XRF) data acquired with a full-field XRF spectrometer employing a position-sensitive and energy-dispersive Gas Electron Multiplier (GEM) detector, which provides only limited energy resolution at a level of 18% Full Width at Half Maximum (FWHM) at 5.9 keV. In this article, we present the design and performance of the full-field imaging spectrometer and the results of case studies performed using the developed instrument. The XRF imaging data collected for two historical paintings are presented along with the procedures applied to data calibration and analysis. The maps of elemental distributions were built using three different analysis methods: Region of Interest (ROI), Non-Negative Matrix Factorisation (NMF), and Principal Component Analysis (PCA). The results obtained for these paintings show that the factor analysis methods NMF and PCA provide significant enhancement of selectivity of the elemental analysis in case of limited energy resolution of the spectrometer.

scholarly journals Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

2015 ◽  
Author(s):  
Andrew Anand Brown ◽  
Zhihao Ding ◽  
Ana Viñuela ◽  
Dan Glass ◽  
Leopold Parts ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Expression Levels ◽  
Biologically Relevant ◽  
Kegg Pathways ◽  
Analysis Methods ◽  
Gene Expression Levels

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 "pathway phenotypes" which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bonferroni threshold (P<5.38E-5). These phenotypes are more heritable (h^2=0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

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