This award is made through the NSF Cultural Anthropology Faculty Scholars Program, which supports specialized technical training for anthropologists with on-going research programs. Dr. Jeremy M. Koster of the University of Cincinnati will receive training in advanced statistical techniques that support multi-level modeling for the analysis of behavioral observation data.
Koster will employ multi-level modeling to analyze data on the subsistence strategies of indigenous horticulturalists in lowland Nicaragua. Koster's research seeks to understand how individuals behave to meet subsistence and economic needs throughout the seasonal round given variation in their ages, wealth, skills, household composition, and social networks. The research is important because better understanding of subsistence livelihoods can inform interactions between the United States and countries with autonomous indigenous populations, particularly in parts of the world that have been characterized by political unrest and conflict. This award will allow the researcher to apply advanced statistical techniques to a common type of anthropological data, and the outcomes of this training will include analytical approaches that also can be employed by other researchers.
This award allowed the project investigator to develop proficiency in multilevel modeling statistical analyses. These analytical methods are often necessary for anthropological datasets because the repeated observations of individuals leads to statistical dependence, which violates the assumptions of conventional statistical methods that are suited primarily for randomized, exchangeable samples. Although multilevel modeling (also known as "mixed-effects modeling") approaches were developed decades before, the increase in computing power and the development of specialized software have recently resulted in the burgeoning use of these methods throughout the social and biological sciences. In order to gain firsthand experience with multilevel modeling, the project investigator worked on a publishable analysis of empirical data with collaborators and statisticians at the University of California-Davis. The project investigator had used "spot-check" observational methods to collect time allocation data in lowland Nicaragua, and an anthropologist at UC-Davis had collected a similar dataset in highland Peru. In both settings, adult men frequently migrate to pursue moneymaking opportunities in distant locales. To test the hypothesis that the temporary absence of male household heads requires an increase in "men’s work" as substitute labor by other household residents, the research team conducted a multilevel modeling analysis. The results indicate that household co-residents in both societies provide substitute labor only rarely, which contrasts with survey data collected by sociologists and agrarian scholars. This project reveals the potential for detailed anthropological data to reveal subtle contingencies of human behavior.
