The amount of time that FACTOR takes is roughly proportional to the cube of the number of variables. Factoring 100 variables, therefore, takes about 1,000 times as long as factoring 10 variables. Iterative methods (PRINIT, ALPHA, ULS, ML) can also take 100 times as long as noniterative methods (PRINCIPAL, IMAGE, HARRIS).
No computer program is capable of reliably determining the optimal number of factors, since the decision is ultimately subjective. You should not blindly accept the number of factors obtained by default; instead, use your own judgment to make a decision.
Singular correlation matrices cause problems with the options PRIORS=SMC and METHOD=ML. Singularities can result from using a variable that is the sum of other variables, coding too many dummy variables from a classification variable, or having more variables than observations.
If you use the CORR procedure to compute the correlation matrix and there are missing data and the NOMISS option is not specified, then the correlation matrix might have negative eigenvalues.
If a TYPE=CORR, TYPE=UCORR, or TYPE=FACTOR data set is copied or modified using a DATA step, the new data set does not automatically
have the same TYPE as the old data set. You must specify the TYPE= data set option in the DATA statement. If you try to analyze
a data set that has lost its TYPE=CORR attribute, PROC FACTOR displays a warning message saying that the data set contains
_NAME_
and _TYPE_
variables but analyzes the data set as an ordinary SAS data set.
For a TYPE=FACTOR data set, the default is METHOD=PATTERN, not METHOD=PRIN.