Factor analysis applied to regional geochemical data: problems and possibilities

A large regional geochemical data set of C-horizon podzol samples from a 188,000 km(2) area in the European Arctic, analysed for more than 50 elements, was used to test the influence of different variants of factor analysis on the results extracted. Due to the nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multimodal data distributions), the simplest methods of factor analysis with the least statistical assumptions perform best. As a result of this test it can generally be suggested to use principal factor analysis with an orthogonal rotation for such data. Selecting the number of factors to extract is difficult, however, the scree plot provides some useful help. For the test data, a low number of extracted factors gave the most informative results. Deleting or adding just 1 element in the input matrix can drastically change the results of factor analysis. Given that selection of elements is often rather based on availability of analytical packages (or detection limits) than on geochemical reasoning this is a disturbing result. Factor analysis revealed the most interesting data structures when a low number of variables were entered. A graphical presentation of the loadings and a simple, automated mapping technique allows extraction of the most interesting results of different factor analyses in one glance. Results presented here underline the importance of careful univariate data analysis prior to entering factor analysis. Outliers should be removed from the dataset and different populations present in the data should be treated separately. Factor analysis can be used to explore a large data set for hidden multivariate data structures. (C) 2002 Elsevier Science Ltd. All rights reserved.