Factor Analysis and Principal Component Analysis: Differences (Research and Statistics)

Factor Analysis and PCA

Factor Analysis
Reduce large number of variables into fewer number of factors
Co-variation is due to latent variable that exert casual influence on observed variables
Communalities – each variable’s variance that can be explained by factors
Principal Component Analysis
Variable reduction process – smaller number of components that account for most variance in set of observed variables
Explain maximum variance with fewest number of principal components
PCA Factor Analysis
Observed variance is analyzed Shared variance is analyzed
1.00’s are put in diagonal – all variance in variables Communalities in diagonal – only variance shared with other variables are included – exclude error variance and variance unique to each variable
Analyze variance Analyze covariance
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Factor analysis,PCA,research,statstics,SPSS,