Exploratory factor analysis: Difference between revisions

Revision as of 01:20, 26 April 2012

Revision as of 01:24, 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>{{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>Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). "Evaluating the use of exploratory factor analysis in psychological research". ''Psychological Methods'', 4(3), 272-299.</ref> It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables.<ref>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{{cn|date=April 2012}} 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

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===Kaiser criterion===

Compute the eigenvalues for the correlation matrix and determine how many of these eigenvalues are greater than 1. This number is the number of factors to include in the model. A disadvantage of this procedure is that it is quite arbitrary (e.g. an eigenvalue of 1.01 is included whereas an eigenvalue of .99 is not). This procedure often leads to overfactoring and sometimes underfactoring.{{cn|date=April 2012}} Therefore, this procedure should not be used. <ref name =Fabrigar />)

===Model comparison===