Chemistry versus data dispersion: Is there a better way to assess and interpret archaeometric data?

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1259-1279
Number of pages21
Issue number6
StatePublished - Dec 2011


  • Bivariate plots
  • Data analysis
  • INAA
  • Mississippian
  • Normalization
  • Ohio
  • Outliers
  • PCA
  • PIXE
  • Pottery
  • Shell-tempered ceramics

ASJC Scopus subject areas

  • History
  • Archaeology


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