Application of XRF and correspondence analysis to provenance studies of coastal and inland archaeological pottery from the Mngeni river area, South Africa

XRF spectrometry was applied to provenance studies of Iron Age pottery specimens that originated from the Mngeni river area in South Africa. Ten transition metals (Sc to Zn) mere determined in 107 potsherds, excavated from four different sites. The data were subjected to a computerized mathematical technique (correspondence analysis), which was used to group the samples according to the similarity of their elemental distributions. The groupings were interpreted in terms of social or cultural interaction between the sites. (C) 1997 by John Wiley & Sons, Ltd.

The use of correspondence analysis to compare major and trace elements for provenance studies of iron-age pottery from the Mngeni river area, South Africa

Provenance studies of iron-age pottery specimens originating from the Mngeni river area in South Africa was carried out by applying XRF spectrometry. A total of sixteen major and trace elements were analysed in a batch of 107 potsherds, excavated from four different archaeological sites in the aforementioned area. A multivariate statistical programme Correspondence Analysis was used in this study to obtain the relevant clustering patterns according to the similarity of the elemental distributions. Differences and similarities in the clusters obtained for the majors and trace elements are discussed.

Comparing correspondence analysis and the log-ratio alternative for representing categorical data

We compare correspondance análisis to the logratio approach based on compositional data. We also compare correspondance análisis and an alternative approach using Hellinger distance, for representing categorical data in a contingency table. We propose a coefficient which globally measures the similarity between these approaches. This coefficient can be decomposed into several components, one component for each principal dimension, indicating the contribution of the dimensions to the difference between the two representations. These three methods of representation can produce quite similar results. One illustrative example is given

Weighted Logratio Biplots, Correspondence Analysis and Spectral Maps

Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

The use of correspondence analysis to develop a scale to measure workplace morale from multi-level data

This paper focuses on the issue of comparing social groups or collectivities using measures derived from individual-level multivariate data. In this case, groups need to be differentiated such that: (a) between-group differences are maximized; (b) within-group differences are minimised; and (c) `differences' are calibrated to a scale that reflects a set indicators or observed variables.This paper demonstrates empirically how correspondence analysis can achieve this. It presents a scale of `workplace morale' derived from the responses of employees in a large sample of workplaces to questions concerning satisfaction with various facets of their job and their workplace. The scale derived through correspondence analysis is shown to achieve the three criteria described above.