Distinct academic traditions have come to formally theorize culture as socially learned information that can influence beliefs and behaviors. Cultural processes therefore impact a myriad of applied issues, ranging from national security strategy to health policy. In all these cases, actors may strive to maximize some utility, but they do so in a limited information environment, much of which they have sampled from local social networks and longstanding inherited traditions.
Drawing from cognitive and evolutionary anthropology traditions, the authors describe a set of tools capable of dealing with cultural data at various emergent levels, ranging from variation among individuals within local subcultures to small- and large-scale network topologies and finally to longstanding lineages of inherited cultural information. Cultural consensus analysis, an approach closely related to principal component analysis, is a key tool at the most granular level of variation among individuals within groups. Social network analysis provides tools for examining how cultural variations are structured within and across groups, and autoregression approaches provide means to deal with longstanding patterns of cultural variation among groups.
These various techniques and their application to different levels of culture emergence are known in the published literature; however, they have never been organized into a single manual structured around a formally theorized notion of culture. This tool strives to do this; it may be a helpful resource for students, academics, and applied researchers who need to work with cultural data at multiple levels of analysis.
Table of Contents
Introduction: The Need for an Operational Concept of Culture
What We Know About How Culture Operates and Why
Methods to Understand Culture: Cultural Consensus Analysis
Using Cultural Measurements in Statistical Models with Other Variables
Dealing with Cultural Data-Point Nonindependence — Galton's Problem