The most common rule for extracting factors in factor analysis is to base the number of factors on the number of eigenvalues greater than 1.0

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This is referred to as the Kaiser-Guttman rule and is commonly used in factor analysis. Factors with eigenvalues greater than 1.0 are considered to explain more variance than individual variables, so they are retained as important factors.

The Most Common Rule for Extracting Factors in Factor Analysis