COLLABORATIVE RESEARCH: Multilevel Modeling Analysis of Cross-Cultural Data

该奖项将使两名人类学家能够开发用于分析跨文化数据的统计工具。社会和行为科学家认识到，从西方社会的研究中得出的结论可能并不能准确地代表地球仪其他环境中的人们。因此，科学家们使用跨文化样本来评估，情境化和解释人类变异的范围。例如，如果他们发现某些社会中的个人在经济交易中的公平准则与西方社会的成员不同，他们可以分析来自许多社会的比较数据，看看是否有共同的因素，如生态或群体规模，这可能会解释准则的差异。但是，使用这种跨文化样本可能会带来新的挑战，因为它们通常会导致复杂的分层数据结构，而不是平均值，因此需要先进的统计方法。因此，除了对独特的广泛数据集进行实质性分析外，这项研究还将导致引入可适用于对未汇总的跨文化数据进行类似分析的方法。鉴于社会科学家重新强调跨文化研究，对于通过汇编和合作产生的大型结构化数据集，越来越需要这种方法，本项目使用的数据将根据对21个以生存为导向的社会中猎人收获野生动物的研究汇编。分析将集中在狩猎熟练度如何在整个生命周期中变化。当前的研究目标是检验这样一个假设，即人类幼年期相对于非人类灵长类的延长是一种适应，它促进了人类逐渐掌握区分人类的复杂觅食策略。更广泛地说，这项研究告知了关于衰老的进化和期望，以及经验在维持老年人技能表现中的作用。数据汇编包括1 000多名猎人约20 000次狩猎旅行的结果。虽然以前对跨文化数据的分析通常依赖于聚合和平均值，但该奖项将采用多层次建模方法，这些方法考虑了复杂的数据结构和结果变量的挑战性，即零和连续正值的混合物，难以通过传统的统计方法进行分析。本项目中的统计模型将展示各研究地点与年龄相关的模式的变化范围，这对各个社会在多大程度上可以作为史前环境的模型或类似物具有影响。新的数据集和改进的方法将提供给其他科学家。

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.