Dear friend,

There is a lot of confusion here. First of all, talking about using EFA and CFA is nonsense because I'm either using one or the other and I'm doing it in the context of my scientific intent. It shouldn't look like I'll just do it and see. Regarding the results (KMO indicates that the data is not suitable for the use of this type of analysis - I recommend a thorough study of the correlation matrix). At the same time, I would like to note that Ste did not perform EFA or CFA, but PCA with regard to the chosen estimate. I assume that you used SPSS and left everything in the basic settings, that's a mistake. We also do not know what the research design is, the choice of rotation can also be a problem. Much more than a few numbers are needed for interpretation. It is necessary to understand the research, what is hidden behind the variables, as well as the overall meaning and significance of the analysis.

I recommend thinking more deeply about the meaning of analysis and a deeper understanding of outputs as well as input information and methods.

Good luck.

Hello Ahmed Shiyam. Some of the points Marián Čvirik raised are also discussed in this nice article by Preacher & MacCallum (2003). Perhaps you will find it helpful.

https://quantpsy.org/pubs/preacher_maccallum_2003.pdf

Dear friend

consider the following points regrading your query:

Choice between EFA and CFA:The decision to use either Exploratory Factor Analysis (EFA) or Confirmatory Factor Analysis (CFA) should be driven by a well-defined scientific intent rather than a trial-and-error approach. It's crucial to establish a clear rationale for the chosen method and align it with the research objectives.

Dear Ahmed, please refer to my study guide if you choose to do an EFA. PCA is probably not the best technique. Your KMO value is rather low. The eigenvalue higher that 1 rule will probably give you too many factors. All the extracted factors need to have significant weighing on at least 3 variables. You need to avoid cross matching.