COLLABORATIVE RESEARCH: Multilevel Modeling Analysis of Cross-Cultural Data

社会和行为科学家认识到，从富裕的西方环境中得出的研究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，他们转向跨文化样本来评估，语境化，并解释人类变异的范围。例如，如果他们发现在以生存为导向的园艺社会中，个体在经济交易中的公平标准与西方社会的成员不同，他们可能会发现原因在于生态或群体规模的差异。因此，跨文化比较有助于社会科学家建立适用于所有人类社会的人类和社会变异性理论。为了回答这些问题，社会科学家越来越多地依赖于跨文化样本，以及在世界各地不同环境中收集可比数据的研究人员之间的合作。然而，与从单一人口中随机抽样相比，这些研究通常会产生复杂的分层数据结构，需要先进的统计方法，但这些方法非常适合描述跨文化样本的变异范围。该奖项将允许两位人类学家开发跨文化数据分析的统计方法，特别是21个以生存为导向的社会中猎人捕获野生动物的汇编。分析的重点是狩猎熟练度在整个生命周期中变化的方式，测试的假设，延长人类少年期相对于非人类灵长类动物是一种适应，促进逐步掌握复杂的觅食策略，区分人类的利基。更广泛地说，这项研究揭示了对衰老的期望，以及经验在老年人维持基于技能的表现中的作用。数据汇编包括1 000多名猎人约20 000次狩猎旅行的结果。鉴于以前对跨文化数据的分析通常依赖于聚合和平均值，该奖项将允许开发多层次建模方法，这些方法可以解释复杂的数据结构和结果变量的挑战性，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。除了对狩猎回报的独特扩展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未汇总跨文化数据的类似分析的方法。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。

Social and behavioral scientists recognize that conclusions drawn from research in affluent western settings may not accurately represent people in other settings around the globe. Therefore, they are turning to cross-cultural samples to assess, contextualize, and explain the range of human variability. For example, if they find that individuals in subsistence-oriented horticulturalist societies have different norms of fairness in economic transactions than do members of western societies, they might find that the explanation lies in differences in ecology or group size. Thus cross-cultural comparisons help social scientists to build theories of human and social variability that apply to all human societies. To answer such questions, social scientists increasingly rely on cross-cultural samples, with collaborations among researchers who collect comparable data in different settings around the world. As compared to random samples from a single population, however, these studies typically result in complex hierarchical data structures that require advanced statistical methods, but that are well suited to characterize the range of variation in cross-cultural samples.This award will allow two anthropologists to develop statistical methods for the analysis of cross-cultural data, specifically a compilation of wildlife harvests by hunters in 21 subsistence-oriented societies. The analysis focuses on the ways in which hunting proficiency varies across the lifespan, testing 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 the human niche. More broadly, this research informs 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 allow for the develop of 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. In addition to a substantive analysis of a uniquely expansive dataset of hunting returns, 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.