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Dear all, Many thanks for all your replies. As documented in my email yesterday....a covariance based PCA is thought preferable for the problem below. However texts recommend standardisation (i.e. correlation based PCA) for a situation in which some variables vary more than others (to avoid such variables having an undue influence on the PCs). Bearing this in mind, what should be done if, say, some of the variables had a relatively large variance in my situation?...e.g. say values: 1,1,1,1,1,5,5,5,5,5 and other variables had smaller variances e.g.values: 2,2,2,2,2,2,3,3,3,3. Would it still make sense to do a covariance based PCA? Many thanks again, Kim. ORGINAL EMAIL: Hello all I am going to do a principal components analysis. I know that we can standardise the variables (correlation-based PCA) to make them 'equally important'. I have read that 1)if we omitted standardisation, a variable which varied lot would tend to dominate the principal components, and 2) standardisation to make variables equally important is suggested when the variables are measured in different units. My question is: if my variables are all measured in the same units (say each variable contained scores for n people and each score could take one of 5 values 1 to 5), would it still be OK to do a correlation based PCA (i.e. using covariance matrix of standardised variables) or would covariance based PCA (I.e. using covariance matrix of unstandardised variables) be more appropriate? Many thanks, in advance for your help, All the Best, Kim.