Displays the eigenvalues associated with a component or factor in descending order versus the number of the component or factor. Used in principal components analysis and factor analysis to visually assess which components or factors account for most of the variability in the data.
The ideal pattern in a scree plot is a steep curve, followed by a bend and then a flat or horizontal line. Retain those components or factors in the steep curve before the first point that starts the flat line trend. In practice, you may have difficulty interpreting a scree plot. Use your knowledge of the data and the results from the other methods of selecting components or factors to help decide the number of important components or factors.
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A factor analysis was conducted on 12 different characteristics of job applicants. This scree plot shows that 5 of those factors account for most of the variability because the line begins to straighten after factor 5. The remaining factors account for a very small proportion of the variability and are likely unimportant.
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