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Hi Vernica,   If I am not wrong, I think the minimum sample size for Exploratory factor analysis (EFA) is 50. Usually, they use the rule of thumb method to feed the sample data, however sometimes, this method is unreliable. It is better to know the dataset well, and different data screening methods will help you understand the objective purpose well. But before reducing the sample size of your data, be careful with your data fields because sometimes, if you remove the required field, it will lead to improper solutions.    And I am afraid that the sample data in EFA may not give you a feasible solution if any significant factor has been removed.    Good Luck

Many researchers use sample datasets to predict their results; thus, the larger datasets will not provide relevant and accurate results. However, it is still doubtful to the researchers whether the small sample data yields a proper decision regarding the number of factors. So, I suggest you be more selective and vigilant while selecting the factors, especially in sample data.

I hope this article will give you some ideas on using sample size data in EFA or PCA approach. https://www.tandfonline.com/doi/abs/10.1080/00273170902794206?journalCode=hmbr20

I can help you with my experience. Just try sample size at least 5-6 subjects per variable. And it would help if you concentrated on independent variables including no. of factors, no. of items, different levels of loadings it may differ from researches you pursue. You should have some clues on what you are planning to do like the factors you are trying to include before you start to use the sample data in your research.    Compared to EFA and CFA, I always prefer to use sample datasets in the Confirmatory Factor Analysis (CFA) because the main job of the CFA is to test the measurement model. Here, I would like to clarify that I am not judging the models, as I give my personal opinion according to the external validity.   All the Best  

  Hi, The small sample data have a higher chance to provide inappropriate solutions. But still, you can try with 3-7 per factor.   If you are still confused about the concept, you can look into research websites like Chanakya Research to know more. The experts will provide you with authentic and best possible solutions for your research-related queries.   https://www.chanakya-research.com/   All the Best!