There are many manuals. However, I would advise you this one:

Fabrigar, L. R., &Wegener, D. T. (2012). Exploratory factor analysis . OUP USA.

ISBN13: 9780199734177

There are many manuals. However, I would advise you this one:

Mulaik, S. A. (2009). Foundations of Factor Analysis, Second Edition. Chapman & Hall/CRC, 2009.

ISBN: 978-1-4200-9961-4

In the following video you will see how to store the files. Remember that, at moment FACTOR allows to manage different groups of participants, and to decide which groups are included in the factor analysis at hand. This feature is a first step towards much more interesting analyses, like factor invariance analyses. We expect to be able to develop and implement such analyses in a near future.

FACTOR allows you to define (partially) specified targets that can be used to explore if a factor model is plausible one in your data. This analysis would be the unrestricted version of a confirmatory factor analysis.

FACTOR allows you to define (partially) specified targets that can be used to explore if a factor model is plausible one in your data. Please note that RETAM can help you refine your target matrix. RETAM identifies which values in the target (the ones that you defined as free values) can be set as zero values. In addition, it is advisable to use the crossvalidation analysis (also implemented in FACTOR) in order to safely interpret the refined target.

Yes and no. Yes, because FACTOR splits your data in two subsamples if your data has at least 400 observations. And no, because FACTOR does not implement random splitting algorithm. The random splitting algorithm could produce non equivalent subsamples. Instead, FACTOR implement Solomon: a method for splitting a sample into equivalent subsamples in factor analysis.

In the video tutorial, you can learn how to use it in your analyses.

Yes, all you have to do is to load the data file as a multygroup dataset. In the video tutorial, you can learn how to use it in your analyses.