Exploratory factor analysis: Difference between revisions

Revision as of 02:59, 26 April 2012

Revision as of 02:59, 26 April 2012

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In [[multivariate statistics]], '''exploratory factor analysis''' (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within [[Factor Analysis]] whose overarching goal is to identify the underlying relationships between measured variables <ref name=Norris> {{cite journal|last=Norris|first=Megan|coauthors=Lecavalier, Luc|title=Evaluating the Use of Exploratory Factor Analysis in Developmental Disability Psychological Research|journal=Journal of Autism and Developmental Disorders|date=17 July 2009|volume=40|issue=1|pages=8–20|doi=10.1007/s10803-009-0816-2}}</ref> . It is commonly used by researchers when developing a scale{{clarify|reason=undefined technical term|date=April 2012}} and serves to identify a set of [[Latent variable|latent constructs]] underlying a battery of measured variables. <ref name=Fabrigar> {{cite journal|last=Fabrigar|first=Leandre R.|coauthors=Wegener, Duane T., MacCallum, Robert C., Strahan, Erin J.|title=Evaluating the use of exploratory factor analysis in psychological research.|journal=Psychological Methods|date=1 January 1999|volume=4|issue=3|pages=272–299|doi=10.1037/1082-989X.4.3.272}}</ref> It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables.<ref name=Finch>Finch, J. F., & West, S. G. (1997). "The investigation of personality structure: Statistical models". ''Journal of Research in Personality'', 31 (4), 439-485.</ref> ''Measured variables'' are any one of several attributes of people that may be observed and measured. An example of a measured variable would be one item on a scale. Researchers must carefully consider the number of measured variables to include in the analysis.<ref name =Fabrigar /> EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis. There should be at least 3 to 5 measured variables per factor.<ref>Maccallum, R. C. (1990). "The need for alternative measures of fit in covariance structure modeling". ''Multivariate Behavioral Research'', 25(2), 157-162.</ref>

EFA is based on the common factor model. Within the common factor model, measured variables are