It is recommended to make the exploratory factorial analysis with prominent rotation if its factors are correlated, also it has to see if it presents indicators in negative redaction, significant KMO bartlett, to fix the distribution of normality of the data. If you have previous studies of the instrument in your context and sample you can make a confirmatory hypothesis through confirmatory factor analysis.

in the settings of SPSS you need to add the rotation of your variables.

for instance, if you have 8 indicators, then your rotation should be based on this number.

additionally, you need to run reliability test and assure the consistency of all items that you want to have for your FA. this should be done in this stage and before CFA.

if you need further information on how to exactly run this analysis, let me know and I will try to help you step by step in the process. you will need to run the basic steps of SEM without going to AMOS for goodness-of-fit. but for this, you need CFA and perhaps EFA, depending on what you want.

(see Hair et al., 2010; Hayes, 2017)

also, before you run factor analysis, make sure there are no missing data or outlier in the data you have. you can do this in excel through (< > than your scale) and omit those with ST.D that is less than 1.

in this case, your results of FA will be relatively better.

I hope this helps.

Siddharth Tiwari, you need to set the number of factors to "eight" before you carry out your rotation. Based on the figure, I believe that you perform some form of orthogonal rotation. You should also attemple oblique rotation with Promax (the most powerful) if Varimax rotation did not yield the desired results. Empirically speaking, factors are associated with each other, and orthogonal rotations are used only when are sufficient theoretical basis or support from the extant literature.

If you performed an orthogonal rotation, for subsequent regression analysis, you need to compute factor scores in the form of Thurstone's or Bartlett's factor scores. The former is found under the "regression" option while the latter is found under "Bartlett" when you click on "Scores." For oblique rotation, you apply "Anderson-Rubin."

FYI: Factor scores are computed from matrix multiplication. An appropriate matrix of factor loadings are muliplied with the matrix containing your variable scores to obtain them.