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We therefore considered the possibility that systematic error variance may be causing these items to group together beyond their dependence on one principal factor.
The factorial analysis of the DUQOL identified one principal factor that accounted for 28.499% of the variance.
Also a recent study indicated that the EPDS contains three factors: one principal factor (depression) and two supplementary factors (loss of enjoyment, and despair/self-harm) [ 20].
We postulate that one principal factor determining what kind of biodiversity would survive a cataclysmic shift is the presence or absence of a refuge.
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Two criteria were used to attribute each item to one of the dimensions: like other authors [ 33], factor loading >0.60 with one principal component was chosen, instead of factor loading >0.40 [ 34], and when an item exhibited factor loading across several dimensions, it was attributed to the one for which it maximized internal consistency assessed by Cronbach's α-coefficient [ 34].
For exposure time individual factor matrix, only one principal component was needed to explain most of the variance.
We first carried out one principal component analysis in each lineage on the 9 factors to transform them into 9 independent (or orthogonal) principal components 9 (designated as PC), each being a linear combination of the 9 genomic factors.
Multivariate methods showed: (a) one principal component explained > 40% of the variance, (b) 7 factors summarised the data.
Exploratory factor analysis for these four items was performed, and only one principal component emerged (Eigenvalue 1.8).
To account empirically for variation in the regional opportunity structures, we have constructed one scaling variable based on principal component factor analysis.
In order to account for regional variation of the opportunity structures we construct one scaling variable based on principal component factor analysis and merge these with DAB data.
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