There are a number of common uses for PCA: (a) you have measured many variables (e.g., 7-8 variables, represented as 7-8 questions/statements in a questionnaire) and you believe that some of the variables are measuring the same underlying construct (e.g., depression). ![]() Its aim is to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for most of the variance in the original variables. Principal components analysis (PCA, for short) is a variable-reduction technique that shares many similarities to exploratory factor analysis. Principal Components Analysis (PCA) using SPSS Statistics Introduction
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