Building theories of consistency and variability in children's language development: A large-scale data approach

被引:0
|
作者
Tsui, Angeline Sin Mei [1 ]
Marchman, Virginia A. [1 ]
Frank, Michael C. [1 ]
机构
[1] Stanford Univ, Dept Psychol, Stanford, CA 94305 USA
关键词
INFANT-DIRECTED SPEECH; CROSS-LANGUAGE; PUBLICATION BIAS; SAMPLE-SIZE; PERCEPTION; PREFERENCE; LESSONS; POWER;
D O I
10.1016/bs.acdb.2021.04.003
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
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.
引用
收藏
页码:199 / 221
页数:23
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