Non iterative Principal Factor Analysis (PFA). This is an approach which tries to detect underlying structures in the relationships between the variables of interest. Unlike PCA, the PFA is focused only on the shared variances of the set of variables. It is suited when the goal is to uncover the latent structure of the variables. It works on a slightly modified version of the correlation matrix where the diagonal, the prior communality estimate of each variable, is replaced by its squared multiple correlation with all others.
Harris Component Analysis. This is a non-iterative factor analysis approach. It tries to detect underlying structures in the relationships between the variable of interest. Like Principal Factor Analysis, it focuses on the shared variances of the set of variables. It works on a modified version of the correlation matrix.
Principal Component Analysis. Two functionalities are added: the reproduced and residual correlation matrices can be computed, the variables can be sorted according to the loadings in the output tables.
These three components can be combined with the FACTOR ROTATION component (varimax or quartimax).
They can be combined to the re-sampling approaches for the detection of the relevant number of factors (PARALLEL ANALYSIS and BOOTSTRAP EIGENVALUES).
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