User Guide

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Chapter 29
Factor Analy
sis Extraction
Figure 29-5
Factor Analysis Extraction dialog box
Method. Allows you to specify the method of factor extraction. Available methods are
principal components, unweighted least squares, generalized least squares, maximum
likelihood, principal axis factoring, alpha factoring, and image factoring.
 Principal Components Analysis. A factor extraction method used to form
uncorrelated linear combinations of the observed variables. The first component
has maximum variance. Successive components explain progressively smaller
portions of the variance and are all uncorrelated with each other. Principal
components analysis is used to obtain the initial factor solution. It can be used
when a correlation matrix is singular.
 Unweighted Least-Squares Method. A factor extraction method that minimizes the
sum of the squared differences between the observed and reproduced correlation
matrices ignoring the diagonals.
 Generalized Least-Squares Method. A factor extraction method that minimizes the
sum of the squared differences between the observed and reproduced correlation
matrices. Correlations are weighted by the inverse of their uniqueness, so
that variables with high uniqueness are given less weight than those with low
uniqueness.
 Maximum-Likelihood Method. A factor extraction method that produces parameter
estimates that are most likely to have produced the observed correlation matrix
if the sample is from a multivariate normal distribution. The correlations are
weighted by the inverse of the uniqueness of the variables, and an iterative
algorithm is employed.