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The Roman Pottery of Kent
by Dr Richard J. Pollard  -  Chapter 2  page 23
Doctoral thesis completed in 1982, published 1988

ware". . . But we can say that if 75% of the pottery recorded at X is Oxfordshire ware, and only 50% at Y is, then there is relatively more Oxfordshire ware at X than Y. . . . the figures themselves may not be useful individually’. The emphasis in this study is thus placed on inter-assemblage rather than intra-assemblage analysis and interpretation.

2. Inter-assemblage comparison
The quantified data provide limited opportunities for making statements of the kind quoted in the previous paragraph. Points of similarity and dissimilarity have been isolated from these data, and from these a series of impressions of the degrees of similarity between sites has been gained. Ideally, objective statistical techniques should be applied to the raw data to provide a check on the subjective impressions. However, it was felt that the size of the quantified data base was not large enough to justify the application of multivariate analysis. Even with the increasing concern shown by pottery reporters for quantification, it is unlikely that a full coverage of the variables of spatial and temporal location and of function will be provided by quantified sites in the foreseeable future, and that presence/absence data will still be required to provide such coverage. A number of statistical measures of similarity between sites/assemblages have been discussed by Doran and Hodson (1975, 135—43), and of these Gower’s Flexible Coefficient is deemed particularly useful, as it is able to cope satisfactorily with both presence/absence and quantified data. The correlation coefficients computed by this technique might then be treated to multivariate analysis, such as Principal Components Analysis (Doran and Hodson 1975, 190—7), in the search for major trends of correlation between assemblage components and in order to provide a summary of the relationships between assemblages as units or between sites. The elucidation of production and marketing 

systems would thus proceed on a more rigorous, objective data base.
   The limitation of information on most sites to presence/absence data renders expressions of similarity on a numerical scale both difficult and potentially misleading. However, it was felt desirable to have some such index, and to this end Jaccard’s Coefficient was adopted on a limited basis. The formula is cited by Doran and Hodson (1975, 141—2), and is simple to compute, if time-consuming. The coefficient Sj = a/(a+b+c), where a = the number of pottery types that a pair of sites have in common, b= the number of types present at site X but absent at Site Y, and c = the number present at Y but not at X. The shortcomings of presence/absence data render the resulting correlation coefficients crude representations of similarity in comparison with Gower’s coefficient, but they do at least provide some check to subjective impressions.
   One inherent weakness of Jaccard’s coefficient is that it fails to take account of situations wherein the assemblages at site X are effectively subsets of those at site Y. In practice this is often the case, as between a minor, rural site and a neighbouring town (e.g. Chariton and London/ Southwark). The correlation coefficient computed in the case of a subset to set pair of sites may be of the same order of magnitude as one computed for two sites with very different ‘kitchen ware’ assemblages but fundamentally identical ‘table ware’ assemblages. The latter situation is widespread in the second century in Kent with the existence of several ‘kitchen ware’ marketing zones but an overall occurrence of exotic ‘table wares’ such as samian, Nene Valley/Rhineland, and fine grey ware. Constant recourse to the assemblage/pottery type matrices constructed as a preliminary to computation is necessary for the interpretation of the coefficients to be verified.

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