Methods for Summarizing Radiocarbon Datasets

TLDR

Bayesian models are widely used for large radiocarbon datasets, yet challenges persist in validating model assumptions, handling artifacts, and applying methods when underlying processes cannot be quantified or sample selection biases obscure event distributions. This study compares three approaches—Sum distributions, postulated undated events, and kernel density methods—for summarizing radiocarbon data. The authors implement these approaches in the OxCal software and assess their suitability for visualizing chronological and geographic analyses with or without useful prior information. They conclude that kernel density analysis is a powerful method that could be applied more broadly across dating applications.

Abstract

Abstract Bayesian models have proved very powerful in analyzing large datasets of radiocarbon ( 14 C) measurements from specific sites and in regional cultural or political models. These models require the prior for the underlying processes that are being described to be defined, including the distribution of underlying events. Chronological information is also incorporated into Bayesian models used in DNA research, with the use of Skyline plots to show demographic trends. Despite these advances, there remain difficulties in assessing whether data conform to the assumed underlying models, and in dealing with the type of artifacts seen in Sum plots. In addition, existing methods are not applicable for situations where it is not possible to quantify the underlying process, or where sample selection is thought to have filtered the data in a way that masks the original event distribution. In this paper three different approaches are compared: “Sum” distributions, postulated undated events, and kernel density approaches. Their implementation in the OxCal program is described and their suitability for visualizing the results from chronological and geographic analyses considered for cases with and without useful prior information. The conclusion is that kernel density analysis is a powerful method that could be much more widely applied in a wide range of dating applications.