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New light on old data: Toward understanding settlement and social organization in Middle Bronze Age Aeolian Islands (Sicily) through quantitative and multivariate analysis

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... While sparse data may prove more ubiquitous in archaeology than one could expect at first (and is an issue that deserves a specific study), the example takes into account one such data set which summarises the distribution of 488 artifacts across 20 prehistoric huts unearthed at Capo Milazzese (Sicily). The data have been compiled and discussed at length by Alberti (2017). Some concluding remarks are made in Section 7. ...
... Our study in this section is based on data, summarised in Table 1, that were compiled by Bernadò Brea and Cavalier (1968), and analysed extensively by Alberti (2017), and come from an archaeological site at Capo Milazzese, Sicily. Table 1, which is of size 20 × 13, cross-classifies 374 artifacts found in 13 huts at the site. ...
... Also, the analysis of his table was undertaken by first aggregating a carefully selected set of row categories. The labels in Table 1 were selected to match the labels (Alberti, 2017) used in his Table 4. The largeness of the contingency table and the relative sparsity of many objects in the sites makes it a particularly interesting data set to analyse. ...
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Traditionally, simple correspondence analysis is performed by decomposing a matrix of standardised residuals using singular value decomposition where the sum-of-squares of these residuals gives Pearson's chi-squared statistic. Such residuals, which are treated as being asymptotically normally distributed, arise by assuming that the cell frequencies are Poisson random variables so that their mean and variance are the same. However, studies in the past reveal that this is not the case and that the cell frequencies are prone to overdispersion. There are a growing number of remedies that have been proposed in the statistics, and allied, literature. One such remedy, and the focus of this paper, is to stabilise the variance using the Freeman–Tukeytransformation. Therefore, the properties that stem from performing correspondence analysis will be examined by decomposing the Freeman–Tukey residuals of a two-way contingency table. The application of this strategy shall be made by studying one large, and sparse, set of archaeological data.
... In so doing, the characterisation of the spatial configuration and functional variability of the spaces in settlement areas represents a key step. However, though spatial studies have indeed increased (Achino and Barceló 2019;Alberti 2017;Aquino et al. 2016;Cantisani 2015;Moroni et al. 2020;Peinetti et al. 2015;Vullo et al. 1999), holistic analyses integrating various ranges of data, particularly the bio-archaeological ones, still remain limited (Speciale et al. 2016). Moreover, studies have mainly focussed on well-preserved contexts, such as those affected by sudden destructions and collapses (Cazzella et al. 2002;Malorgio and Maggiulli 2011;Scarano 2011;Vullo et al. 1999). ...
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Spatial analyses have been increasingly used to investigate behavioural patterns and human activities in archaeological and ethnoarchaeological studies. For the Bronze Age in the central Mediterranean spatial analyses integrating various ranges of data, from artefacts to ecofacts, still remain limited in number. Moreover, studies have mainly focussed on well-preserved contexts affected by sudden destructions; spaces inhabited over long periods and so subjected to dynamic depositional processes, are more challenging to interpret as to their function(s). Yet, these latter are commonly encountered archaeological contexts. Representing palimpsests of repeated activities, they are valuable case studies for investigating the spatial organisation of activities. This paper presents an integrated spatial analysis of a long-occupied area of the Coppa Nevigata settlement from the Late Bronze Age. It is a trial, aimed at both building a viable methodology to deal with ‘dynamic’ deposits and verifying the potential of the observed record in terms of activity areas and fossilised patterns of behaviour. https://www.archaeopress.com/ArchaeopressShop/Public/displayProductDetail.asp?id={F629B808-C1A9-4AF6-AABB-A21AB53A8D7A}
... The proposed functional analysis addresses some of the main issues touched in general publications on archaeological ceramics (among others, Rice 1987;Braun, 1983;Orton and Hughes, 2013). It follows the methodologies applied in the last three decades to the analysis of very different pottery assemblages, from prehistoric to contemporary contexts (Recchia 1997(Recchia , 2010Skibo 2013;Alberti 2016). ...
... The proposed functional analysis addresses some of the main issues touched in general publications on archaeological ceramics (among others, Rice 1987;Braun, 1983;Orton and Hughes, 2013). It follows the methodologies applied in the last three decades to the analysis of very different pottery assemblages, from prehistoric to contemporary contexts (Recchia 1997(Recchia , 2010Skibo 2013;Alberti 2016). ...
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The reconstruction of the technical systems of ceramic production and of its ‘chaîne opératoire’ is a means of exploring certain social structures in time and space. For many years, methodological procedures based on multidisciplinarity have made it possible to analyse both materials and methods of fabrication for this purpose. Session IV-3 organised at the 18th Congress of the UISPP in 2018 aimed to highlight the contribution of technological approaches to ceramics, both in archaeology and in ethnology, to the analysis of pre- and protohistoric societies. The case studies focus on the Neolithic and the European Bronze Age, but also on the megalithism of our era in Senegal. Open acess here : https://archaeopress.com/ArchaeopressShop/Public/download.asp?id={2204B00F-4359-4A7B-B6E1-F71D573C000E}
... inspection of the association between the huts and the objects446 found within them can be made by performing correspondence analysis on 447Table 1.Alberti (2017) performed such an analysis in his detailed description 448 of the data, although he used the classical approach which is based on the 449 Pearson chi-squared statistic, X 2 ; see, for example,Greenacre (1984) and Beh450 and Lombardo (2014) for a comprehensive discussion of the various practical451 and technical issues of correspondence analysis.452The two-dimensional correspondence plot of the contingency table, using 453 the classical approach, is given on the left ofFigure 2; note that the configura-454 tion of these points is identical to the plots given by Alberti (2017b,Figure 6) 455 apart from the reflection about both axes. ...
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Traditionally, simple correspondence analysis is performed by decomposing a matrix of standardised residuals using singular value decomposition where the sum-of-squares of these residuals gives Pearson's chi-squared statistic. Such residuals, which are treated as being asymptotically normally distributed , arise by assuming that the cell frequencies are Poisson random variables so that their mean and variance are the same. However, studies in the past reveal that this is not the case and that the cell frequencies are prone to over-dispersion. There are a growing number of remedies that have been proposed in the statistics, and allied, literature. One such remedy, and the focus of this paper, is to stabilise the variance using the Freeman-Tukey transformation. Therefore, the properties that stem from performing correspondence analysis will be examined by decomposing the Freeman-Tukey residuals of a two-way contingency table. The application of this strategy shall be made by studing one large, and sparse, set of archaeological data.
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