Called “principal” axis factoring because the first factor accounts for as much common variance as possible, then the second factor next most variance, and so on. PAF is a descriptive procedure so it is best to use when the focus is just on your sample and you do not plan to generalize the results beyond your sample.{{cn|date=April 2012}} An advantage of PAF is that it can be used when the assumption of normality has been violated. <ref>{{Fabrigar, L. r., & Petty, R. E. (1999). The role of the affective and cognitive bases of attitudes insusceptibility to affectively and cognitively based persuasion. Personality and Social Psychologybulletin, 25, 91-109.}}</ref> . Another advantage of PAF is that it is less likely than ML to produce improper solutions<ref>{{Finch, J. F., & West, S. G. (1997). The investigation of personality structure: Statistical models. Journal of Research in Personality, 31 (4), 439-485.1997-42719-00110.1006/jrpe.1997.2194}}</ref> . A downside of PAF is that it provides a limited range of goodness-of-fit indexes compared to ML and does not allow for the computation of confidence intervals and significance tests.
