Exploratory factor analysis: Difference between revisions

Revision as of 01:25, 26 April 2012

Revision as of 01:29, 26 April 2012

Line 17:

==Selecting the appropriate number of factors==

When selecting how many factors to include in a model, researchers must try to balance [[parsimony]] (a model with relatively few factors) and plausibility (that there are enough factors to adequately account for correlations among measured variables). <ref>{{cncite book|datelast=AprilFabrigar|first=Leandre 2012R.|title=Exploratory factor analysis|publisher=Oxford University Press|___location=Oxford|isbn=9780199734177|coauthors=Wegener, Duane T.}}</ref> It is better to include too many factors (overfactoring) than too few factors (underfactoring).

''Overfactoring'' occurs when too many factors are included in a model. It is not as bad as underfactoring because major factors will usually be accurately represented and extra factors will have no measured variables load onto them. Still, it should be avoided because overfactoring may lead researchers to put forward constructs with little theoretical value.