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Exploratory Factor Analysis (EFA) is used to uncover the underlying structure of a relatively large set of variables. It is commonly used by researchers when developing a scale and serves to identify a set of [[Latent variable|latent constructs]] underlying a battery of measured variables<ref>{{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 carefully consider the number of measured variables to include in the analysis. 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>.)
The researcher's assumption when conducting EFA is that any indicator/measured variable may be associated with any factor. When developing a scale, researchers should use EFA first before moving on to [[Confirmatory Factor Analysis]] (CFA). EFA requires the researcher to make a number of important decisions about how to conduct the analysis because there is no one set method.
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[[Category:Statistics]]
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