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dc.contributor.authorPreacher, Kristopher J.
dc.contributor.authorMacCallum, Robert C.
dc.date.accessioned2007-04-25T17:02:58Z
dc.date.available2007-04-25T17:02:58Z
dc.date.issued2003
dc.identifier.citationUnderstanding Statistics, 2, 13-32.
dc.identifier.urihttp://hdl.handle.net/1808/1492
dc.description.abstractProper use of exploratory factor analysis (EFA) requires the researcher to make a series of careful decisions. Despite attempts by Floyd and Widaman (1995), Fabrigar, Wegener, MacCallum, and Strahan (1999), and others to elucidate critical issues involved in these decisions, examples of questionable use of EFA are still common in the applied factor analysis literature. Poor decisions regarding the model to be used, the criteria used to decide how many factors to retain, and the rotation method can have drastic consequences for the quality and meaningfulness of factor analytic results. One commonly used approach--principal components analysis, retention of components with eigenvalues greater than 1.0, and varimax rotation of these components--is shown to have potentially serious negative consequences. In addition, choosing arbitrary thresholds for factor loadings to be considered large, using single indicators for factors, and violating the linearity assumptions underlying EFA can have negative consequences for interpretation of results. It is demonstrated that, when decisions are carefully made, EFA can yield unambiguous and meaningful results.
dc.language.isoen_US
dc.subjectexploratory factor analysisen
dc.subjectprincipal componentsen
dc.subjectEFAen
dc.subjectPCAen
dc.titleRepairing Tom Swift's electric factor analysis machine
dc.typeArticle
dc.rights.accessrightsopenAccess


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