I have read many post but I dont understand. I think they are same meaning. Both of them is same in the practice. I collect data and I apply at SPSS I cant see differences. Can you help?
They are similar in many ways. PCA is an approach for only data reduction to get indices. But FA ia an approach for create measurement models.
If you want to choose correct one:
1. If you assume or wish to test a theoretical model of latent factors causing observed variables ---- Run factor analysis
2. If you want to simply reduce your correlated observed variables to a smaller set of important independent composite variables ---- Run principal component analysis
Hello Murat,
In addition to Hakan's good advice, please allow me to elaborate a little.
The chief distinction between PCA and FA is that all the observed variation in a data set is presumed to be common variation in PCA, while FA (or, common factor analysis) distinguishes between common variance (that which is shared among variables), specific variance (that which is specific to an individual variable), and error variance (that which is due to measurement error).
As a result, unless you have a substantial number of variables in the data set, PCA tends to bias variable-component loadings (correlations) higher in magnitude relative to FA. That can result in differing interpretations of both structure and salience (whether a variable does or does not affiliate with a component/factor).
Here's a brief, very readable overview of some of the differences as applied to a data set:
https://stats.idre.ucla.edu/spss/seminars/introduction-to-factor-analysis/a-practical-introduction-to-factor-analysis/
Good luck with your work.
In basic SPSS version (without AMOS), you can run from the menu only Dimension reduction/Factor analysis, where in the output table it sais it is actually PCA. In AMOS, an addition to SPSS, you can run confirmatory factor analysis (a "real" factor analysis) for creating models. Hope this helps.
Adding to what has already been said. PCA is a (rather crude) data reduction technique which produces a set of uncorrelated (orthogonal) components. Factor analysis can be much more nuanced and allows comparison and testing of underlying (potentially correlated) structures trying to get a better picture of reality in conditions of measurement uncertainty. In general, if you are interested in understanding you should be using factor analysis.
Moroever, there have been recent major developments in factor analysis, see for example
Article Exploratory Structural Equation Modeling: An Integration of ...
Here is a recent application that evaluates a number of alternative models of mental illness and points to recent developments of the FA technique:
Article Understanding the population structure of the GHQ-12: Method...
Thank you everyone, I found here the answer. https://www.youtube.com/watch?v=abQA0n83hBQ&lc=z23bczq5murpudse5acdp435r0bsleedddnxojlfcg1w03c010c.1574518268999375 Details on comments
In practice we choose principal component analysis as method for pca but another way we choose principal axis factoring for fa. (spss)
Factor Analysis is the procedure to determine the usefulness of items measuring certain construct while PCA is one of the extraction procedure in FA process.
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