Kaiser's criterion in factor extraction retains factors with eigenvalues ...

Kaiser's criterion in factor extraction retains factors with eigenvalues ...

• A. Using the scree plot to determine
• B. Retaining factors with communalities above 0.7
• C. Retaining factors with eigenvalues greater than 1
• D. Retaining factors with eigenvalues less than 1

Answer: (C)

In factor extraction, eigenvalues indicate how much variance a factor accounts for in the observed variables. Since variables are often standardized to have variance 1, a factor with an eigenvalue greater than 1 explains more variance than a single original variable on average. Kaiser's criterion uses this rule of thumb and retains factors whose eigenvalues exceed 1. If a factor explains less variance than one variable, it’s typically not kept. Remember, this is a guideline and not perfect, so researchers may also use scree plots or parallel analysis to decide.

In factor extraction, eigenvalues indicate how much variance a factor accounts for in the observed variables. Since variables are often standardized to have variance 1, a factor with an eigenvalue greater than 1 explains more variance than a single original variable on average. Kaiser's criterion uses this rule of thumb and retains factors whose eigenvalues exceed 1. If a factor explains less variance than one variable, it’s typically not kept. Remember, this is a guideline and not perfect, so researchers may also use scree plots or parallel analysis to decide.