Faculty Scholars Award: Multi-level Modeling Analysis of Behavioral Observation Data

该奖项通过NSF文化人类学教师学者计划颁发，该计划支持人类学家进行持续研究计划的专业技术培训。Jeremy M.博士辛辛那提大学的科斯特将接受高级统计技术的培训，这些技术支持对行为观察数据进行分析的多层次建模。 科斯特将采用多层次建模来分析尼加拉瓜低地土著园艺家生存策略的数据。 科斯特的研究旨在了解个人在整个季节性变化中如何满足生存和经济需求，因为他们的年龄，财富，技能，家庭组成和社交网络各不相同。这项研究很重要，因为更好地了解生存生计可以为美国与拥有自治土著人口的国家之间的互动提供信息，特别是在世界上政治动荡和冲突的地区。该奖项将使研究人员能够将先进的统计技术应用于常见类型的人类学数据，而这种培训的成果将包括其他研究人员也可以采用的分析方法。

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.