Exploratory factor analysis: Difference between revisions

Revision as of 01:45, 26 April 2012

Revision as of 01:50, 26 April 2012

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===Maximum likelihood (ML)===

The maximum likelihood method has many advantages in that it allows researchers to compute of a wide range of indexes of the [[goodness of fit]] of the model, it allows researchers to test the [[statistical significance]] of factor loadings, calculate correlations among factors and compute [[confidence interval]]s for these parameters<ref>{{Cudeck, R., & O'Dell, L. L. (1994). Applications of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations. Psychological Bulletin, 115, 475-487. doi:10.1037/0033-2909.115.3.475.1994-32085-00110.1037/0033-2909.115.3.475}}</ref> . ML is the best choice when data are normally distributed because “it allows for the computation of a wide range of indexes of the goodness of fit of the model [and] permits statistical significance testing of factor loadings and correlations among factors and the computation of confidence intervals” <ref name=FabrigarPetty>{{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> . ML should not be used if the data are not normally distributed.

===Principal axis factoring (PAF)===