Material Correlates Analysis (MCA)

Abstract

ABSTRACTTheories developed and validated using ethnographic and historical resources are often difficult to examine using sparse or fragmentary archaeological material. However, a number of statistical techniques make it possible to integrate data from ethnographic, historical, and archaeological resources into a single analytical framework. This article introduces Material Correlates Analysis (MCA)—a new method of filling gaps in the archaeological data using a strategic combination of data collection, multidimensional scaling, principal component analysis, and generalized liner modeling. Generalized liner modeling is a particularly useful tool in formal inferential statistics for comparing a priori classified groups of historical and/or ethnographic (known) cases with archaeological (unknown) ones on the basis of relevant variables. MCA allows us to overcome the inherent material culture limitations regarding data on key variables by using available historical or ethnographic evidence to make statistically testable inferences regarding archaeological data. Using the Modes of Religiosity theory as an example, we demonstrate how major gaps in the evidentiary record can be overcome using the techniques we outline. Specifically, we use the MCA approach to ascertain whether the agricultural transition in southwest Asia was associated with a shift from an imagistic to an increasingly doctrinal mode of religiosity.