Chemistry Versus Data Dispersion: Is There a Better Way to Assess and Interpret Archaeometric Data?

Abstract

Given the common use of chemical concentration data to define ceramic groups that aid in the exploration of ancient technology, trade and provenance, it is important to reflect on how we collectively establish and define both chemical groups and outliers. In this paper, we argue that commonly used data analysis procedures, such as principal component analysis and centred log-ratio principal component analysis favoured in the examination of ceramic chemical data, although rapid and easy, may overlook existing chemical groups and outliers, especially when the ratio of non-diagnostic to diagnostic elements is high. To evaluate whether geochemistry is more important than data dispersion in data assessment, we re-examine chemical concentration data from previously published ceramic, clay and daub samples from the lower Ohio River Valley. We begin by briefly discussing steps we took to ensure that the data set reflects geochemical differences, rather than analytical or data transfer errors. Next, we use bivariate plots, as well as PCA and CLR-PCA, to examine different versions of our altered data, using varying numbers of element combinations. We propose that the careful examination of bivariate plots is critical in establishing the elements that should be included in PCA and other multivariate analyses.

Contextualising the Neolithic Occupation of Southern Vietnam: The Role of Ceramics and Potters at An Son

“…In PCA, the elements are reduced to principal components, which can then be plotted against each other to identify compositional groupings. Michelaki and Hancock (2011) report the need to formulate bivariate plots before leaping into PCA or CLR-PCA (centred log ratio-principal component analysis) plots to identify elements that may diminish the chemical variation. It may only be necessary to include a few of the elements in a PCA.…”

“…The bivariate plots show each analysed element against another element to understand where the variability in the compositional data lies and which elements may be limiting the observed variability (as recommended by Michelaki and Hancock 2011). Each element was plotted against SiO 2 because of its consistent presence in all of the samples (Figure 6.4).…”

Reconstructing Regional Trajectories: the Provenance and Distribution of Archaic to Hellenistic Ceramics in CentralPisidia(South-west Turkey)

“…The contribution of specific elements to group separation can be observed together with the degree of variability. PCA is commonly used both as a tool to discover subgroups, and to assess the coherence of hypothetical groups suggested by other criteria; for example, petrographic groups, archaeological context and stylistic features (Baxter 2001;Michelaki and Hancock 2011).…”

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

“…Thus, archaeologists are consistently interested in the chemical groups that compositional analyses of ceramics suggest. Yet, it is not always clear that the definition of such chemical groups may be dependent on the way compositional data are transformed (Michelaki and Hancock, 2011), on the particular data exploration method selected (Baxter and Freestone, 2006) and even on the specific elements selected for consideration (Baxter and Jackson, 2001). Although some archaeometrists and statisticians consider such dependence obvious, the issue is rarely addressed clearly in the literature.…”