Principal component analysis is a powerful instrument in occupational hygiene inquiries.
Several investigators have successfully used principal component analysis (PCA) in interpreting occupational hygiene data. However, traditional textbooks in occupational hygiene provide no guidance for the application and interpretation of PCA. In this article I briefly review the basics of PCA (for those not statistically inclined), provide some guidelines for performing PCA (and designing studies that use the power of PCA), illustrate its application in understanding exposure to mixtures and the characterization of 'peak exposure', and highlight other benefits that occupational hygienists stand to gain by including PCA in their 'statistical toolkit'. I hope that this article will promote greater use and understanding of a data analysis approach that has long been helping investigators outside the field of occupational hygiene to unravel the structure behind the complex relationships among multiple correlated variables.
Burstyn
Igor
I
Department of Public Health Sciences, Faculty of Medicine & Dentistry, The University of Alberta, 13-103 Clinical Sciences Building, Edmonton, AB, T6G 2G3, Canada. iburstyn@ualberta.ca
D006801:Humans; D016273:Occupational Exposure; D016272:Occupational Health / Q000706:statistics & numerical data*; D025341:Principal Component Analysis