Fitting procedures are used to estimate the factor loadings and unique variances of the model. (''Factor loadings'' are the regression coefficients between items and factors and measure the influence of a common factor on a measured variable). There are several factor analysis fitting methods to choose from, however there is little information on all of their strengths and weaknesses and many don’t even have an exact name that is used consistently. Principal axis factoring (PAF) and [[maximum likelihood]] (ML) are two extraction methods that are generally recommended.{{cn|date=April 2012}} In general, ML or PAF give the best results, depending on whether data are normally-distributed or if the assumption of normality has been violated.
