What is Principal Component Analysis and what software should you use?
Principal Component Analysis (PCA) is a variable-reduction technique that is used to emphasize variation, highlight strong patterns in your data and identify interrelationships between variables.
It aims to reduce the number of correlated variables into a smaller number of uncorrelated variables called principal components. Thus, giving you much smaller and easier components to work with! Each component attempts to account for as much variability in the data as it can.
PCA is useful for identifying underlying dimensions of consumer behavior, summarizing data and identifying redundant questions in questionnaires. It is also frequently done as the preliminary step in a series of analyses. For example, you might use principal components before you perform a regression analysis to avoid multicollinearity.
