Conclusion

Principal component analysis is an effective procedure for reducing a number of observed variables into a smaller number that account for most of the variance in datasets. This technique is particularly useful when a data reduction procedure is required that makes no assumptions concerning an underlying causal structure responsible for covariation in the data. When such an underlying causal structure can be envisioned, it might be more appropriate to analyze the data using exploratory factor analysis.