COLLABORATIVE RESEARCH: Multilevel Modeling Analysis of Cross-Cultural Data

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

This award will allow two anthropologists to develop statistical tools for the analysis of cross-cultural data. Social and behavioral scientists recognize that conclusions drawn from research in western societies may not accurately represent people in other settings around the globe. Therefore, scientists use cross-cultural samples to assess, contextualize, and explain the range of human variability. For example, if they find that individuals in some societies have different norms of fairness in economic transactions than do members of western societies, they can analyze comparative data from many societies to see if there are common factors, such as ecology or group size, that might explain the difference in norms. But working with such cross-cultural samples can present new challenges because they typically result in complex hierarchical data structures rather than averages and therefore require advanced statistical methods. Therefore, in addition to a substantive analysis of a uniquely expansive dataset, this research will also result in the introduction of methods that can be adapted to similar analyses of unaggregated cross-cultural data. Given the renewed emphasis on cross-cultural research by social scientists, such methods are increasingly needed for the large, structured datasets that result from compilations and collaborations. The data to be used in this project will be compiled from studies of wildlife harvests by hunters in 21 subsistence-oriented societies. The analysis will focus on how hunting proficiency varies across the lifespan. The immediate research goal is to test the hypothesis that the extension of the human juvenile period relative to non-human primates is an adaptation that promotes the gradual mastery of the complex foraging strategies that distinguish humans. More broadly, this research informs evolution and expectations about senescence and the role of experience in the maintenance of skills-based performance among aging adults. The compilation of data includes the outcomes of approximately 20,000 hunting trips by more than 1,000 hunters. Whereas previous analyses of cross-cultural data have often relied on aggregations and averages, this award will engage multilevel modeling approaches that account for the complex data structure and the challenging nature of the outcome variable, a mixture of zeroes and continuous positive values that is difficult to analyze via conventional statistical methods. The statistical models in this project will demonstrate the range of variation in age-related patterns across study sites, which has implications for the extent to which individual societies can serve as models or analogues for prehistoric contexts. Both the new data set and the improved methodology will be made available to other scientists.