Building theories of consistency and variability in children's language development: A large-scale data approach.
Building theories of consistency and variability in children's language development: A large-scale data approach.
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DOI：
10.1016/bs.acdb.2021.04.003
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发表时间：
2021
影响因子：
--
通讯作者：
Frank MC
中科院分区：
文献类型：
--
作者：
Tsui ASM;Marchman VA;Frank MC
幼儿通常在生命的头两年开始学习单词。另一方面，他们在语言学习上也有很大的不同。语言学习的异同需要一种定量理论来预测和解释早期语言的哪些方面是一致的，哪些是可变的。然而，目前的发展研究实践限制了我们建立这种定量理论的能力，因为样本量小，并且与再现性和可复制性相关的挑战。在本章中，我们建议三种方法——荟萃分析、多站点合作和二手数据聚合——可以共同解决当前发展领域研究的一些局限性。我们回顾了每种方法的优势和局限性，最后讨论了结合这三种方法的潜在影响。
Young children typically begin learning words during their first 2 years of life. On the other hand, they also vary substantially in their language learning. Similarities and differences in language learning call for a quantitative theory that can predict and explain which aspects of early language are consistent and which are variable. However, current developmental research practices limit our ability to build such quantitative theories because of small sample sizes and challenges related to reproducibility and replicability. In this chapter, we suggest that three approaches—meta-analysis, multi-site collaborations, and secondary data aggregation—can together address some of the limitations of current research in the developmental area. We review the strengths and limitations of each approach and end by discussing the potential impacts of combining these three approaches.
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