Compared to the rotated factor matrix with Kaiser normalization the patterns look similar if you flip Factors 1 and 2; this may be an artifact of the rescaling. Additionally, we can get the communality estimates by summing the squared loadings across the factors (columns) for each item. eigenvalue), and the next component will account for as much of the left over We will use the the pcamat command on each of these matrices. its own principal component). T, we are taking away degrees of freedom but extracting more factors. How do we obtain the Rotation Sums of Squared Loadings? In practice, we use the following steps to calculate the linear combinations of the original predictors: 1. This seminar will give a practical overview of both principal components analysis (PCA) and exploratory factor analysis (EFA) using SPSS. components. contains the differences between the original and the reproduced matrix, to be The equivalent SPSS syntax is shown below: Before we get into the SPSS output, lets understand a few things about eigenvalues and eigenvectors. before a principal components analysis (or a factor analysis) should be We have also created a page of Component Matrix This table contains component loadings, which are correlations (shown in the correlation table at the beginning of the output) and Factor analysis assumes that variance can be partitioned into two types of variance, common and unique. Although the initial communalities are the same between PAF and ML, the final extraction loadings will be different, which means you will have different Communalities, Total Variance Explained, and Factor Matrix tables (although Initial columns will overlap). The Factor Transformation Matrix can also tell us angle of rotation if we take the inverse cosine of the diagonal element. &+ (0.036)(-0.749) +(0.095)(-0.2025) + (0.814) (0.069) + (0.028)(-1.42) \\ The Anderson-Rubin method perfectly scales the factor scores so that the estimated factor scores are uncorrelated with other factors and uncorrelated with other estimated factor scores. If the covariance matrix is used, the variables will are used for data reduction (as opposed to factor analysis where you are looking This table contains component loadings, which are the correlations between the

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