Principal components analysis and factor analysis are common methods used to analyze groups of variables for the purpose of reducing them into subsets represented by latent constructs (Bartholomew, 1984; Grimm & Yarnold, 1995). Even though PCA shares some important characteristics with factor analytic methods such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), the ...

Background: Recent studies show cardiovascular disease (CVD) risk factors have the tendency to intermingle. Integration between some biological and homodynamic risk factors have made the new hypothesis for etiology of CVDs. The aim of this study was to determine the clustering pattern of metabolic variables and left ventricular (LV) mass among smoker and non-smoker individuals and the interrela...

This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and classical spurious regression. We show that a few such absorb nearly all variation. The corresponding scree plot suggests contain factors, which is corroborated by standard panel information criteria. Furthermore, Dickey–Fuller tests unit root hypothesis applied to estimate...