or principle axis factoring?And which turning method is the best with maximum likelihood or principle axis factoring in order to be able to confirm the scale model in Confirmatory factor analysis?( the participant number is between 400 and 500 and the research ares is social sciences-education)
I recommend unweighted least squares (ULS), and diagonally weighted least squares (DWLS)
Tc Tülay Öztürk, to begin with, your question is a little confusing. I'm not sure whether you want to conduct exploratory or confirmatory factor analysis (EFA and CFA, respectively). That aside, if you want to conduct both, you should not use the same data for a CFA that you'd used in EFA because doing so is highly likely to result in invalid confirmation of your model / factor structure.
Different researchers have different preferences with regard to their method of extraction in EFA, so I suggest you look up information on the web and choose the method that seems to suit your data and purpose(s) best. For what it's worth, I tend to use either maximum likelihood (ML) or principal axis factoring (PAF), depending on what my data look like.
By "turning", I expect you are referring to rotation in EFA. Under most circumstances, I think it is better to use an oblique rotation (e.g. promax or oblimin) rather than an orthogonal rotation (e.g., varimax).
I hope that's helpful.
ML is the basis for CFA, so if you want to maximize consistency, you should use it for your EFA as well. To avoid the "over-fitting" problem (i.e., simply confirming your EFA results by applying both methods to the same data), you can use the EFA on a randomly selected subset of the full data set.
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