Exploratory factor analysis: Difference between revisions

Revision as of 01:24, 26 April 2012

Revision as of 01:25, 26 April 2012

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==Fitting procedures==

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. <ref name =Fabrigar />)

===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> .{{cn|date=April 2012}} 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>{{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> .{{cn|date=April 2012}} ML should not be used if the data are not normally distributed.

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