Desarrollo Organizacional

Coin data analysis falls into several broad categories. There are basic coin histograms, plotting coins by period found at a site or as part of a hoard, still used within site reports today, and there are also a range of more sophisticated comparative techniques comparing cumulative coin-loss to an average coin-loss profile, as developed by Richard Reece (1995). Whilst having no significant concerns with Reece’s comparative methods, Lockyear (2007, pp215-218) has highlighted the potential for the use of more advanced techniques. There is scope for numismatics to learn from techniques employed by other fields of archaeology, by employing methods widely used for other artefact types but rarely applied to

coin data. Pottery studies, such as Pitts (2005), have made use of cluster and correspondence analysis, which has an established role within the field of statistical pattern recognition. Another technique that has potential is the integration of GIS data, allowing spatial analysis of a site, and between sites (as employed by Guest, in Holbrook 1998) for the coin data from Cirencester. However, sometimes it is the simplest methods of analysis that can prove the most effective, where their use is appropriate, such as Reece’s early four-stage coinage system (e.g. Reece 1972) developed before his 21-stage summary. A multidisciplinary assessment of coinage and identity is perhaps best served, initially, by utilising simple methods (together with statistical and theoretical analysis) rather than through utilising established numismatic techniques.

Given the scope and constraints of this research, and my non-numismatic background, it would be unfeasible to attempt the type of methodological approach of Reece (1974; 1991 etc.) or Walton (2012), but I can attempt to provide an overview, and a basis to assess future potential. The role of data analysis here is an attempt to marry the theoretical ideas raised through my multidisciplinary consideration of coinage to the archaeological data, to assess the strengths and potential of the concepts beyond conceptual and theoretical discussion.

PAS Data:

As discussed (chapter 2.5) the PAS database represents a large, readily available and easily accessible dataset. All Roman coins recorded are directly comparable, and are recorded with a consistent format (including Reece issue periods, regions and issue/reverse types). To use the numismatic data within the PAS database requires an awareness of its limitations and biases - PAS numismatic data, and data collected from metal detecting finds, has its own set of issues. The British Museum have issued advice for utilising the PAS database as a tool for archaeological research, including a discussion of some of the problems and biases inherent in the dataset. These biases can be categorised as the geographical collection bias, the reporting bias, and the recording (or identification) bias.

The Geographical Collection Bias: A current PAS research project (previously piloted for Hampshire, Northamptonshire, and the Isle of Wight) may transform PAS data use in the future; this type of research is new, with no real parallel applied to site finds.

“...there is an urgent need to understand in greater detail the factors that influence the spatial distribution of finds data from the PAS (PAS) and the relationship between modern collection practices, artefact types, and space” (British Museum 2013).

This bias is not well understood, but collection affected by: The space available for metal detecting and field walking (depending on current land use, ownership, and attitudes towards detecting), the popularity of metal detecting and field walking in an area (number of detectors, frequency of detecting, and the area covered), and the methods and goals of detecting (such as equipment and techniques used, or the targeting of certain find types).

The Reporting Bias: This is a well known but unquantified phenomenon, hard to relate to a regional level. Some finders are unwilling to report finds to the PAS (for various reasons, political, practical, or misguided). Also, some finders only report ‘better’, or more ‘interesting’ finds (a subjective notion). Smaller copies, and copies with cruder designs, are often dismissively referred to as ‘grots’ within metal detecting. Although there have been concerted efforts by the PAS to ensure finders report all coin finds, regardless of ‘quality’, this potential bias within the data set could affect assessment of diameter (smaller coins potentially under recorded), copies (coins with cruder designs potentially under recorded), and reverse types (with common reverse types potentially underrepresented). Furthermore, a tendency to not clean the coins, unless they are seen as particularly good or interesting, makes identification more difficult. However, as has been discussed, this is also a problem with many site finds (not just objects found through metal detecting and recorded by the PAS). Similarly, over aggressive cleaning may lead to a loss of information available from the coin.

The Recording or Identification Bias: This bias is through the potential for recording and identification by different people, at different times, under different circumstances, with different levels of time, training, knowledge, interest, and resources. As with most coin publication, the PAS give more time and focus to rare coins, with less time and focus spent on common coins. There are extensive notes added by finds specialists to rare and interesting coins recorded, not matched with an effort to capture simple data such as diameters for ‘lesser’ coins, or to find and correct data entry errors in the ‘lesser’ coin records. Different research questions have different data requirements, and for my research the rare coins are relatively unimportant as they are not a significant part of the coinage in use.

Figure 16. PAS Biases (Adapted from the British Museum Website, 2013)

Despite these issues and biases, the PAS database still represents an excellent resource. There is potentially less of a collection bias than is inherent with site finds, whilst site finds also have a similar reporting and recording bias (with a focus on interesting coins or those used to date stratigraphy, and a failure to clean, record and publish in detail less important coins). The PAS is now an established data source for research (e.g. Walton 2012) despite these biases. The PAS database is a unified resource covering England and Wales, whereas site find publications have difficulties in compatibility, with coins recorded using different (often incompatible) systems. The Reece classification system also has the benefit of facilitating the study of copies, by classifying copies within the same issue period as their prototype official issues. Benefits also include ease of access, standardisation of terminology, and a dataset that is expanding and improving constantly.