Computer-Aided Multivariate Analysis 1996
DOI: 10.1007/978-1-4899-3342-3_14
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Principal components analysis

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Cited by 5 publications
(7 citation statements)
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“…H. pylori effect within LTBI). For multiplex-cytokine analysis, Principal Components Analysis [27] was performed with the correlation matrix composed of six variables found to discriminate by Wilcoxon's test between 40 latently infected and 25 controls ( Tables S1 and S2 ), and components having Eigenvalue >1 were selected to compute summary component scores for each subject using eigenvector coefficients.…”
Section: Methodsmentioning
confidence: 99%
“…H. pylori effect within LTBI). For multiplex-cytokine analysis, Principal Components Analysis [27] was performed with the correlation matrix composed of six variables found to discriminate by Wilcoxon's test between 40 latently infected and 25 controls ( Tables S1 and S2 ), and components having Eigenvalue >1 were selected to compute summary component scores for each subject using eigenvector coefficients.…”
Section: Methodsmentioning
confidence: 99%
“…We used the Kaiser-Meyer-Olkin measure to test the suitability of the items for factor analysis. The decision on how many factors to retain was made using Kaiser's rule of retaining only factors with eigenvalues of 1 or higher and examining the "elbow" of the scree plot (plot of eigenvalues) (Afifi, Clark, & May, 2004;DeVellis, 2016;Hinkin et al, 1997). Oblique rotation, which allows for correlation between the rotated factors, was used because the person-centered care domains are theoretically related (Afifi et al, 2004;Hinkin et al, 1997).…”
Section: Psychometric Analysesmentioning
confidence: 99%
“…Dietary patterns give more insights into real-life conditions and may have a greater effect on health outcomes than amounts of individual foods or nutrients (18) . Principal component analysis (PCA) is an established multivariate technique to reduce food consumption data to a smaller number of underlying factors or dietary patterns (19,20) . These patterns are uncorrelated with each other and explain variations in food intake across a population.…”
mentioning
confidence: 99%