2012
DOI: 10.1371/journal.pone.0034410
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A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome

Abstract: BackgroundExploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst main… Show more

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Cited by 18 publications
(30 citation statements)
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“…Further, “insulin resistance/hyperinsulinemia” may be the common unifying factor that links all the core components 2326 . Despite blood pressure being loosely associated with the central features of metabolic syndrome, the current analyses suggest that blood pressure was the metabolic factor that most strongly associated with the occurrence of thyroid hypofunction.…”
Section: Discussionmentioning
confidence: 99%
“…Further, “insulin resistance/hyperinsulinemia” may be the common unifying factor that links all the core components 2326 . Despite blood pressure being loosely associated with the central features of metabolic syndrome, the current analyses suggest that blood pressure was the metabolic factor that most strongly associated with the occurrence of thyroid hypofunction.…”
Section: Discussionmentioning
confidence: 99%
“…For the current analysis, two variables were considered as irrelevant: (1) the physician who diagnosed the disease and (2) the information about the next follow-up consultation (the date). After reduction, relationships between the 18 variables were studied by cluster analysis, using the VARCLUS (variable cluster) procedure [27,28]. This procedure, which organizes a set of numeric variables into hierarchical clusters, can be used to examine redundancy between variables.…”
Section: Methodsmentioning
confidence: 99%
“…The PCs with an eigenvalue over 1 were consider ed significant, as suggested previously for data on samples of more than 100 participants. 45 Associations between PEMCS and significant PCs were tested with 2-sided t tests. Normality of the dependent variables (i.e., significant PCs) was assessed and statistical outliers (mean ± 3 standard deviations) were excluded (PC1 contained 2 outliers and PC2 contained 1 outlier).…”
Section: Methodsmentioning
confidence: 99%