Nonlinear principal components analysis with CATPCA: a tutorial

J Pers Assess. 2012;94(1):12-25. doi: 10.1080/00223891.2011.627965.


This article is set up as a tutorial for nonlinear principal components analysis (NLPCA), systematically guiding the reader through the process of analyzing actual data on personality assessment by the Rorschach Inkblot Test. NLPCA is a more flexible alternative to linear PCA that can handle the analysis of possibly nonlinearly related variables with different types of measurement level. The method is particularly suited to analyze nominal (qualitative) and ordinal (e.g., Likert-type) data, possibly combined with numeric data. The program CATPCA from the Categories module in SPSS is used in the analyses, but the method description can easily be generalized to other software packages.

MeSH terms

  • Nonlinear Dynamics
  • Principal Component Analysis / methods*