{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Why does SAS give negative eigenvalues in Proc Factor

# Why does SAS give negative eigenvalues in Proc Factor - Why...

This preview shows pages 1–2. Sign up to view the full content.

Why do some softwares give negative eigenvalues when doing an Exploratory Factor Analysis Depending on which factor extraction method is used, the eigenvalues that SAS (and STATA) and maybe some other softwares (I don’t know about SPSS) produces are different than simply the eigenvalues of the correlation matrix. NOTE: Mplus only produces the eigenvalues of the correlation matrix. Here I detail the different eigenvalues produced (and why) by SAS Proc FACTOR… By default if no method = option is given, SAS Proc Factor will perform principal component analysis. In this case, the eigenvalues it presents are the eigenvalues for the observed correlation matrix. This is because the eventual q factors it will present (rotated or unrotated) are simply the first q eigenvectors. Hence the concept of “variability explained by the q factors” is represents the proportion of total variability in the standardized observed variables (which is equal to the sum of the diagonal of the correlation matrix,i.e. p) that can be accounted for by the first q factors (which is the sum of the first q eigenvalues). When we ask for a different extraction method, for example method = prinit which performs “Iterated Principal Factor Analysis” or method = ml which performs “Maximum Likelihood”, then SAS will present two sets of eigenvalues the first being “Preliminary eigenvalues” and the second being the “Eigenvalues of the Reduced Correlation Matrix”. Given these extraction methods, the software is now performing a true Common Factor Model and not Principal Component Analysis. Recall

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}