Step 1: Determine the number of principal components
Use the proportion of inertia to determine the minimum number of principal components, also called principal axes, that account for most of the deviation from the expected values in the data. Retain the principal components that explain an acceptable proportion of total inertia. The acceptable level depends on your application. Ideally, the first one, two, or three components account for most of the total inertia.
If the minimum number of principal components needed does not match the number of components that you entered for the analysis, repeat the analysis using the appropriate number of components.
Step 2: Interpret the principal components
Use the quality values to determine the proportion of inertia represented by the components for each category. Quality is always a number between 0 and 1. Larger quality values indicate that the category is well represented by the components. Lower values indicate poorer representation. The quality values help you interpret the components.
Use the contribution values for the columns to assess which categories contribute most to the inertia of each component. To visually interpret the components, use the column plot.
Step 3: Examine the inertia of the categories
Examine calculated inertia values for the column categories. Categories that deviate more from their expected value have a higher inertia value and contribute more to the total chi-squared value.