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Use the components in the steep curve before the first point that starts the line trend. The ideal pattern is a steep curve, followed by a bend, and then a straight line. Scree plot The scree plot orders the eigenvalues from largest to smallest. For example, using the Kaiser criterion, you use only the factors with eigenvalues that are greater than 1. Retain the factors with the largest eigenvalues.
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You can use the size of the eigenvalue to determine the number of factors. Variance (Eigenvalues) If you use principal components to extract factors, the variance equals the eigenvalue. However, if you want to perform other analyses on the data, you may want to have at least 90% of the variance explained by the factors. For descriptive purposes, you may need only 80% of the variance explained. The acceptable level depends on your application. Retain the factors that explain an acceptable level of variance. % Var Use the percentage of variance (% Var) to determine the amount of variance that the factors explain. Then use one of the following methods to determine the number of factors. If you do not know the number of factors to use, first perform the analysis using the principal components method of extraction, without specifying the number of factors.