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bd40bc7c7a If the reproduced matrix is very similar to the original correlation matrix, then you know that the factors that were extracted accounted for a great deal of the variance in the original correlation matrix, and these few factors do a good job of representing the original data. g. Initial - With principal factor axis factoring, the initial values on the diagonal of the correlation matrix are determined by the squared multiple correlation of the variable with the other variables. Factor - The columns under this heading are the rotated factors that have been extracted. c. Because these are correlations, possible values range from -1 to +1.
Mean - These are the means of the variables used in the factor analysis. If the determinant is 0, then there will be computational problems with the factor analysis, and SPSS may issue a warning message or be unable to complete the factor analysis. a. Factor - The columns under this heading are the unrotated factors that have been extracted. Taken together, these tests provide a minimum standard which should be passed before a factor analysis (or a principal components analysis) should be conducted. Extraction - The values in this column indicate the proportion of each variable's variance that can be explained by the retained factors. An identity matrix is matrix in which all of the diagonal elements are 1 and all off diagonal elements are 0. Factor Transformation Matrix - This is the matrix by which you multiply the unrotated factor matrix to get the rotated factor matrix. This means that the first three factors together account for 68.313% of the total variance. The scree plot graphs the eigenvalue against the factor number.