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Since the 1970s, archaeologists have increasingly depended on archaeometric rather than strictly stylistic data to explore questions of ceramic provenance and technol- ogy, and, by extension, trade, exchange, social networks and even identity. It is accepted as obvious by some archaeometrists and statisti- cians that the results of the analyses of compo- sitional data may be dependent on the format of the data used, on the data exploration method employed and, in the case of multivari- ate analyses, even on the number of elements considered. However, this is rarely articulated clearly in publications, making it less obvious to archaeologists. In this short paper, we re- examine compositional data from a collection of bricks, tiles and ceramics from Hill Hall, near Epping in Essex, England, as a case study to show how the method of data exploration used and the number of elements considered in multivariate analyses of compositional data can affect the sorting of ceramic samples into chemical groups. We compare bivariate data splitting (BDS) with principal component analysis (PCA) and centered log ratio-principal component analysis (CLR-PCA) of different unstandardized data formats [original concen- tration data and logarithmically transformed (i.e. log10 data)], using different numbers of elements. We confirm that PCA, in its various forms, is quite sensitive to the numbers and types of elements used in data analysis.

On establishing ceramic chemical groups: exploring the influence of data analysis methods and the role of the elements chosen in analysis