Abstract
The detection and interpretation of variability in archaeological data has been a long-standing effort in the field. This paper aims to introduce the application of Bayesian multilevel modelling as a tool for the detection of variability at levels within nested archaeological data. Model structure, ways of construction, and the potential of using variability information to enhance archaeological interpretations is presented. This is demonstrated through the analysis of two case study datasets: Neolithic pottery finds from Mala (Nova) Pećina cave excavations in Croatia and stone finds from the Bronze Age site of Akrotiri, Thera, Greece. This is followed by a discussion of the multilevel model results and the possible interpretations that can be derived from them. Finally, propositions are made on how these and other models can be extended.
Highlights
The idea of using statistics and computational modelling for the analysis of archaeological datasets appeared with the emergence of New Archaeology in the late 1960s (Doran and Hodson 1975; Drennan 2009; Orton 1980; Sullivan and Olszewski 2016; White and Thomas 1972 and more)
Methods include the use of Coefficient of Variation (CV) on lithic arte fact patterns, bivariate regression anal ysis on ethnoarchaeological data to understand human mobility (Kent 1992), Random-effects Logistic Regression Analysis to study agricultural practices (McCorriston 2002), and hierarchical classification systems and cluster analysis on archaeological ceramics (Plog 1980)
This paper introduces multilevel modelling as a tool for the detection and interpretation of variability archaeological datasets, going beyond previous applications of Bayesian modelling in archaeology
Summary
The idea of using statistics and computational modelling for the analysis of archaeological datasets appeared with the emergence of New Archaeology in the late 1960s (Doran and Hodson 1975; Drennan 2009; Orton 1980; Sullivan and Olszewski 2016; White and Thomas 1972 and more). The consequent rise of interpretational approaches to archaeological data brought more methods of detecting variability and understanding its meaning in archaeological assemblages. These methods were distant from previous statistical and computational analyses of datasets; they were experi mental, typological and cognitive (see Sullivan and Olszewski 2016). In the last decade the interpre tational ‘gap’ between the descriptive and testing statistical applications has been reconciled This has, in part, been a consequence of the increasing application of Bayesian Statistics into archaeological prac tice, which incorporates the researcher’s beliefs, and to an extent the researcher’s perspective, into the statistical analysis (for a review see Otarola-Castillo and Torquato 2018)
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