Contents
What is meant by factor loading?
Factor loadings are correlation coefficients between observed variables and latent common factors. Factor loadings can also be viewed as standardized regression coefficients, or regression weights. The number of rows of the matrix equals that of observed variables and the number of columns that of common factors.
How do you interpret eigenvalues in factor analysis?
The eigenvalue is a measure of how much of the variance of the observed variables a factor explains. Any factor with an eigenvalue ≥1 explains more variance than a single observed variable.
What does the loading mean in factor analysis?
Loading in factor analysis or in PCA (see 1, see 2, see 3) is the regression coefficient, weight in a linear combination predicting variables (items) by standardized (unit-variance) factors/components. Reasons for a loading to exceed $1$:
Is the following analysis defeats the purpose of doing a PCA?
Although the following analysis defeats the purpose of doing a PCA we will begin by extracting as many components as possible as a teaching exercise and so that we can decide on the optimal number of components to extract later. First go to Analyze – Dimension Reduction – Factor.
What is the intuitive reason behind doing rotations in PCA?
Rotations are done for the sake of interpretation of the extracted factors in factor analysis (or components in PCA, if you venture to use PCA as a factor analytic technique). You are right when you describe your understanding.
How are principal components different from factor analysis?
There are two approaches to factor extraction which stems from different approaches to variance partitioning: a) principal components analysis and b) common factor analysis. Unlike factor analysis, principal components analysis or PCA makes the assumption that there is no unique variance, the total variance is equal to common variance.