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. An advantage of PAF is that it can be used when the assumption of normality has been violated. <ref>{{Fabrigar,L.name 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.}}<=FabrigarPetty/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.
