Principal components analysis using SPSS (Oct 2019)

This video demonstrates the use of SPSS for carrying out Principal components analysis (PCA). I cover the topics of component retention (using Kaiser criterion, scree plot, parallel analysis) and interpretation (via orthogonal and oblique rotation). I also demonstrate how to obtain component scores. Copies of the dataset and Powerpoint referenced in the video can be downloaded here (https://drive.google.com/open?id=1ItnkZ68hBWYtKDHZJ29eLGYs7TgqH2FU) and here (https://drive.google.com/open?id=16-arUCU95wrU2G-aAykmiVbMWAzFEsJF). Here is the link to the website containing the parallel analysis engine (https://analytics.gonzaga.edu/parallelengine/). Here is the link to the PCA example at the UCLA Institute for Digital Research and Education (https://stats.idre.ucla.edu/spss/output/principal_components/).

More videos and resources on multivariate statistical procedures can be found here: https://sites.google.com/view/statistics-for-the-real-world/home

Видео Principal components analysis using SPSS (Oct 2019) канала Mike Crowson