Hello Shivam,

I'd suggest a couple of things for you to consider:

1. Instead of using principal components extraction, do consider either maximum likelihood or principal axis extraction. The assumptions of PCA (principal components analysis) are unrealistic for measures of human perception/sentiment.

2. Instead of using orthogonal rotation, do consider using oblique or promax rotation. In general, it's unlikely that factors having to do with perception/sentiment are completely uncorrelated.

3. Instead of relying on default cutoffs for number of factors/components like eigenvalue > 1, consider using parallel analysis instead as a guide.

Good luck in completing your project.

Having read the project, I will recommend you use PAF, oblique rotation. The component matrix may be ignored. Use the patten matrix instead. Name the factors based on the underlying factors loading to them. Compute item means and standard deviation. Do a Cronbach reliability test for each of the factor using the items loading to them.

Thank you David Morse & Valentine Joseph Owan for your recommendations, here are few confusions I have:

1)In the pattern matrix, 3rd component has 3 variables and 2 of them are above .5 and 1 of them is at -.5. How to interpreter the meaning of this result.

2) Is it okay if a component doesn't make meaningful sense?

3)In PAF, my commutative variance is around 59%, is it acceptable?