COLLABORATIVE RESEARCH: Multilevel Modeling Analysis of Cross-Cultural Data

合作研究：跨文化数据的多层次建模分析

• 批准号: 1534627
• 负责人: Richard Mcelreath
• 金额: $ 3.09万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534627&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; Multilevel; Modeling; Analysis

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.

社会和行为科学家认识到，在富裕的西方环境下进行研究得出的结论，可能并不能准确地代表全球其他环境中的人。因此，他们正在转向跨文化样本来评估、分析和解释人类变异性的范围。例如，如果他们发现，在以生存为导向的园艺社会中，个人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关于人类和社会变异性的理论。为了回答这样的问题，社会科学家越来越依赖于跨文化样本，研究人员相互合作，在世界各地的不同环境中收集可比数据。然而，与来自单一种群的随机样本相比，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，但非常适合描述跨文化样本的变异范围。该奖项将允许两名人类学家开发跨文化数据分析的统计方法，特别是对21个以生存为导向的社会中猎人的野生动物收获的汇编。分析的重点是狩猎熟练程度在一生中的变化方式，测试了一种假设，即人类幼年期相对于非人类灵长类动物的延长是一种适应，促进了对区分人类生态位的复杂觅食策略的逐渐掌握。更广泛地说，这项研究提供了对衰老的预期，以及经验在维持老年人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次狩猎的结果。以前对跨文化数据的分析往往依赖于汇总和平均值，而这一奖项将允许开发多层次建模方法，以说明复杂的数据结构和结果变量的挑战性，结果变量是零和连续正值的混合，很难通过传统统计方法进行分析。本项目中的统计模型将显示各研究地点与年龄相关的模式的变化范围，这对各个社会可以在多大程度上充当史前环境的模型或类似物具有影响。除了对独一无二的狩猎回归数据集进行实质性分析外，这项研究还将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。鉴于社会科学家对跨文化研究的重新重视，对汇编和合作产生的大型结构化数据集来说，越来越需要这种方法。