Structure matrix in factor analysis represents what?

Structure matrix in factor analysis represents what?

• A. Correlations between observed variables and factors.
• B. Regression coefficients of observed variables on factors.
• C. Eigenvalues of the factor correlation matrix.
• D. The mean of the observed variables for each factor.

Answer: (A)

The structure matrix shows the correlations between observed variables and the latent factors. It tells you how strongly each observed variable is related to each factor, which helps you interpret which variables cluster on which factors. In practice, high (positive or negative) correlations indicate that a variable loads onto a particular factor, guiding your naming and understanding of the factor. This is distinct from the pattern matrix, which contains the regression coefficients (the unique contribution of each factor to a variable after accounting for other factors). It’s also not about eigenvalues of the factor correlation matrix, which reflect variance explained, nor about the means of the observed variables.

The structure matrix shows the correlations between observed variables and the latent factors. It tells you how strongly each observed variable is related to each factor, which helps you interpret which variables cluster on which factors. In practice, high (positive or negative) correlations indicate that a variable loads onto a particular factor, guiding your naming and understanding of the factor. This is distinct from the pattern matrix, which contains the regression coefficients (the unique contribution of each factor to a variable after accounting for other factors). It’s also not about eigenvalues of the factor correlation matrix, which reflect variance explained, nor about the means of the observed variables.