Eigenvalues: This table shows the eigenvalues associated with the various factors together with the corresponding percentages and cumulative percentages.

Residual correlation matrix: This matrix is calculated as the difference between the variables correlation matrix and the reproduced correlation matrix.

Reproduced correlation matrix: This matrix is the product of the factor loadings matrix with its transpose.

Goodness of fit test: The goodness of fit test is only displayed when the likelihood maximum method has been chosen.

For the maximum likelihood method, the evolution of a criterion which is proportional to the opposite of the likelihood maximum is also displayed.

Maximum change in communality at each iteration: This table is used to observe the maximum change in communality for the last 10 iterations.

The significant correlations are displayed in bold.

Correlation/Covariance matrix: This table shows the data to be used afterwards in the calculations.

Promax: A mixed procedure that consists initially of a Varimax rotation followed by an oblique rotation.

Oblique transformations: Quartimin and Oblimin.

Orthogonal rotations: Varimax, Quartimax, Equamax, Parsimax and Orthomax.

XLSTAT provides three types of rotations: Once the results have been obtained, they may be transformed in order to make them more easy to interpret, for example by trying to arrange that the coordinates of the variables on the factors are either high (in absolute value), or near to zero. Maximum likelihood: This method assumes that the input variables follow a normal distribution.The initial communalities can be calculated according to various methods. The calculations are stopped when the maximum change in the communalities is below a given threshold or when a maximum number of iterations is reached. It is an iterative method which enables the communalities to be gradually converged. Principal factors: This method is probably the most used.Principal components: This method is also used in Principal Component Analysis (PCA).Three methods of extracting latent factors are offered by XLSTAT: The structure linking factors to variables is initially unknown and only the number of factors may be assumed. XLSTAT - Factor Analysis Principles of Factor AnalysisĮxploratory factor analysis (or EFA) is a method which reveals the possible existence of underlying factors which give an overview of the information contained in a very large number of measured variables.

Principal component analysis xlstat