Factor Analysis

Aabel provides the following options:

• The R-mode factor analysis, which considers the inter-relationships in a matrix of correlation between variables
• The Q-mode factor analysis,, which analyzes the inter-object relationships
• TheKaiser varimax rotation:

  In factor analysis, it is often difficult to interpret the loadings, because they may show intermediate correlations with a large number of variables. Rotation of extracted factors attempts to put the factors in a simpler position with respect to the original variables in a manner that either minimizes (move towards one) or maximizes (moves towards zero) individual variable loadings.

The factor analysis output includes the scores, loadings, and correlation matrices.

• To represent the factor loadings graphically, you can for example, use the binary scatter chart while using the option of plotting the X and Y axes through zero and connecting the data points to origo.
• To compare the fraction of variance of the variables explained by the model and the fraction that is not, you can also use the multiple-Y column graphs.
• For a graphical representation of the correlation matrices, you can use the heatmap diagram.

PCA and factor analysis methods allow optional pre-processing of the data prior to the main analysis. Examples of data transformations that can be used (as part of PCA or factor analysis) are:

• Logarithmisizing
• Log centering
• Mean centering
• Taking square root
• Ranking variables individually
• Ranking variables jointly