Factor analysis is used mostly for data reduction purposes:
– To get a small set of variables (preferably uncorrelated) from a large set of
variables (most of which are correlated to each other)
– To create indexes with variables that measure similar things (conceptually).
Two types of factor analysis:
· Exploratory Factor Analysis(EFA)- It is exploratory when you do not have a pre-defined idea of the structure or how many dimensions are in a set of variables.
· Confirmatory Factor Analysis(CFA)- It is confirmatory when you want to test specific hypothesis about the structure or the number of dimensions underlying a set of variables (i.e. in your data you may think there are two dimensions and you want to verify that).
Factor analyses are performed by examining the pattern of correlations (or covariances) between the observed measures. Measures that are highly correlated (either positively or negatively) are likely influenced by the same factors, while those that are relatively uncorrelated are likely influenced by different factors.
In general, you want to use EFA if you do not have strong theory about the constructs underlying responses to your measures and CFA if you do. It is reasonable to use an EFA to generate a theory about the constructs underlying your measures and then follow this up with a CFA, but this must be done using seperate data sets. You are merely fitting the data (and not testing theoretical constructs) if you directly put the results of an EFA directly into a CFA on the same data. An acceptable procedure is to perform an EFA on one half of your data, and then test the generality of the extracted factors with a CFA on the second half of the data.
If you perform a CFA and get a significant lack of it, it is perfectly acceptable to follow this up with an EFA to try to locate inconsistencies between the data and your model. However, you should test any modifications you decide to make to your model on new data.
Submitted By:
Devina Singh
Roll No:12056
Group 12
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