In factor analysis, what does the pattern matrix represent?

In factor analysis, what does the pattern matrix represent?

• A. The matrix of correlations between observed variables and factors.
• B. The eigenvectors of the correlation matrix.
• C. A matrix in factor analysis containing the regression coefficients for each variable on each factor.
• D. The covariance matrix of the observed variables.

Answer: (C)

In factor analysis, the pattern matrix shows the standardized regression coefficients for each observed variable on each latent factor. These coefficients, the loadings, reflect how strongly and in what direction a variable is associated with a particular factor after rotation and while accounting for the influence of the other factors. This matrix is what you use to interpret which variables load onto which factors and how much each variable contributes to each factor. It differs from the structure matrix, which contains the correlations between variables and factors (especially important with oblique rotation where factors correlate). The pattern matrix is the specific part that captures the unique regression relationship of variables to factors.

In factor analysis, the pattern matrix shows the standardized regression coefficients for each observed variable on each latent factor. These coefficients, the loadings, reflect how strongly and in what direction a variable is associated with a particular factor after rotation and while accounting for the influence of the other factors. This matrix is what you use to interpret which variables load onto which factors and how much each variable contributes to each factor. It differs from the structure matrix, which contains the correlations between variables and factors (especially important with oblique rotation where factors correlate). The pattern matrix is the specific part that captures the unique regression relationship of variables to factors.