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Principal components analysis offers researchers a "feel" for analysing particular sets of multidimensional data. A multivariate analysis technique applied to a wide variety of settings such as medicine and chemistry as well as the social sciences, principal components analysis can be used to determine the number of factors to be retained in a factor analysis; for extracting the initial factors in a factor analysis; and in selecting a subset of variables to represent a much larger set. It is particularly useful in coping with multicolinearity in regression analysis, a persistent problem in behavioral and social science data sets.

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