Multivariate Statistical Approaches in Archeology: A Systematic Review

One of the most important achievements of New Archaeology was a tendency toward application of variables and statistical analysis in archaeological research. Each statistical method should be applied to certain areas of archaeological studies; it is important to extract biological, economic, social and cultural information properly via statistical methods. In this study, which is a review through previously published research, articles in which multivariate statistical method are applied in archaeological investigations have been extracted from three worldwide databases, i.e. PubMed, Scopus and Science Direct, based on a protocol designed to search for related articles from January 2000 to January 2016. After application of inclusion and exclusion criteria, finally 384 articles were selected for this investigation. All of the 384 articles were classified based on multivariate statistical methods and then the application of these methods in archaeology and cultural material types was determined. They show that methods, including Cluster analysis, Principal Component Analysis, Discriminant Analysis, Multivariate Multiple Regression, Factor Analysis and Multidimensional Scaling have had respectively the highest application in archaeological investigations. The results of this systematic review indicate that cluster analysis is one of the most applied statistical analysis, perhaps because of usage, method and simple interpretation, compared to other methods of data reduction. This method is used for data reduction or clustering archaeological sites based on their similarities and helps with the comparison between site structures. Principle Component Analysis is the second most widely used methods due to its application in any data structure and simplicity of interpretation compared to other methods of dimensionality reduction.