Determine the number of factors. Perform principal component analysis on the data to obtain eigenvalues. Create a scree plot of the eigenvalues, with the variance explained on the y axis and the eigenvalue on the x axis. Find the "elbow," or the place where the values suddenly drop in the scree plot. The number of the eigenvalue closest to this elbow is the number of factors you should include in the factor analysis. For example, if after running a principle component analysis and graphing the scree plot you find that the variance explained by the principal components drops off sharply after the fourth eigenvalue, then the most suitable number of factors for your forthcoming factor analysis is four.
Run the common factor model. Set the squared multiple correlations as your initial estimates for the commonalities. The end result will be the solution in terms of the number of factors that you selected in the previous step. In the previous example, you had four factors, which means for the common factor model solution you have four as-of-yet unlabeled factors.
Interpret the solution. Because the common factor model only rarely produces interpretable solutions, you should apply a rotation to the solution. Try many forms of rotation to get a comparison. Choose a rotation that gives the solution the most interpretability. Interpret each factor in the solution with as few words as possible. In the example, after finding the four-factor solution, you can try rotation methods such as the varimax rotation and the quartimax rotation, comparing their results. Assume you are analyzing breakfast cereal on variables such as "filling," "energizing," "sweet," "fun" and other descriptions and you find the varimax rotation puts words that demonstrate the cereal being healthful such as "filling" and "energizing" together on one factor, then puts words that demonstrate kid-friendly marketing such as "sweet" and "fun" together on another factor. Then this result is easily interpretable, and you can let it be your final solution.