in factor analysis when should i use the principal axis factoring method and when the principal components method.what's the diffrence between these two .how can i decide which one i have to use.
dear principal axis and principal component are different they're methods but all methods in factor analysis depend on your sample's number.
the number of the sample is 250,which one should i use ? can you please provide me the article that agrees with that.thank you
I recommend this reading. Good luck.
Book Best Practices in Exploratory Factor Analysis
Anissa,
Principal components analysis (PCA) differs from the many versions of common factor analysis (CFA)--including principal axis extraction--in several important ways. PCA assumes that all variance is common variance; measurement error (error variance) and variance that is specific to an individual variable are presumed not to exist. CFA allows for all three sources: common variance, error variance, and specific variance, but only uses the common variance to help describe the factor structure. If there is an appreciable amount of error and/or specific variance, PCA results will not match those of CFA.
In general, when the number of variables being factored is low-moderate, PCA tends to yield higher estimates of variable-component relationships ("loadings") than does CFA. Thus, these would be perceived as biased from a CFA perspective. More important, the interpretation of salient variables and factors/components could vary.
PCA is frequently characterized as a data reduction framework, in which the goal is to capture as much of the total variance in a smaller number of independent components, thereby reducing the total number of variables and eliminating any concern over multicollinearity.
Here are two resources that help elaborate these distinctions:
Richard L. Gorsuch (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Bruce Thompson (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.
Good luck with your work!
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