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三、由小及大:将大规模数据纳入发展科学的方法。

III. FROM SMALL TO BIG: METHODS FOR INCORPORATING LARGE SCALE DATA INTO DEVELOPMENTAL SCIENCE.

出版信息

Monogr Soc Res Child Dev. 2017 Jun;82(2):31-45. doi: 10.1111/mono.12297.

Abstract

For decades, developmental science has been based primarily on relatively small-scale data collections with children and families. Part of the reason for the dominance of this type of data collection is the complexity of collecting cognitive and social data on infants and small children. These small data sets are limited in both power to detect differences and the demographic diversity to generalize clearly and broadly. Thus, in this chapter we will discuss the value of using existing large-scale data sets to tests the complex questions of child development and how to develop future large-scale data sets that are both representative and can answer the important questions of developmental scientists.

摘要

几十年来,发展科学主要基于针对儿童和家庭的相对小规模数据收集。这种数据收集类型占据主导地位的部分原因是,对婴儿和幼儿进行认知和社会数据收集非常复杂。这些小型数据集在检测差异的能力以及进行明确和广泛概括的人口统计多样性方面都受到限制。因此,在本章中,我们将讨论利用现有大规模数据集来检验儿童发展复杂问题的价值,以及如何开发既有代表性又能回答发展科学家重要问题的未来大规模数据集。

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