Principal components analysis is generally used to reduce the number of IVs, which is advantageous when conducting multivariate techniques in which the IVs are not correlated.

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Principal components analysis (PCA) is used to reduce the dimensionality of a data set by transforming variables into a smaller number of uncorrelated variables called principal components, but it does not specifically require the original variables (IVs) to be uncorrelated for its application.

Principal Components Analysis Is Generally Used to Reduce the Number