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Principal Components Analysis (PCA) and Factor Analysis (FA) are multivariate statistical techniques used to reduce the complexity of large datasets. In Informetrics and Scientometrics, both methods can be applied to identify patterns, relationships and underlying structures in bibliographic, citation, publication and research-output data. However, their objectives and statistical assumptions are different. Principal Components Analysis is primarily a data-reduction technique. It transforms a set of correlated variables into a smaller number of uncorrelated components called principal components. The first component explains the maximum possible variance in the original variables, the second explains the maximum remaining variance, and so on. PCA uses the total variance of the observed variables, including common, unique ________ ___ _________ ____ _________ __________ ________.
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