TY - JOUR
T1 - Dynamic variation in receptive vocabulary acquisitions
T2 - Further evidence from the Young Lives study
AU - Aizawa, Toshiaki
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - This study aims to reveal the trajectory of disparity in receptive vocabulary developments attributable to early-life circumstances. Using the cohort data collected from Ethiopia (N = 840), India (N = 1777), Peru (N = 1624) and Vietnam (N = 1831) as the Young Lives study, this study explores the dynamics of variation in receptive vocabulary acquisitions over a 15-year period. The random forest, which is one of the most popular and versatile machine learning algorithms, is adopted to model the association between receptive vocabulary skills and early-life circumstances. Then the constructed model quantifies how much of the observed variation is due to the early-life circumstances. Such variation has already been shaped by the age of 5 and it persists even when children reach early adolescence. The prediction by the random forest suggests that compensation for disadvantaged circumstances at the early developmental stage would help marginalised children catch up and mitigate the disparity when they reach early adolescence.
AB - This study aims to reveal the trajectory of disparity in receptive vocabulary developments attributable to early-life circumstances. Using the cohort data collected from Ethiopia (N = 840), India (N = 1777), Peru (N = 1624) and Vietnam (N = 1831) as the Young Lives study, this study explores the dynamics of variation in receptive vocabulary acquisitions over a 15-year period. The random forest, which is one of the most popular and versatile machine learning algorithms, is adopted to model the association between receptive vocabulary skills and early-life circumstances. Then the constructed model quantifies how much of the observed variation is due to the early-life circumstances. Such variation has already been shaped by the age of 5 and it persists even when children reach early adolescence. The prediction by the random forest suggests that compensation for disadvantaged circumstances at the early developmental stage would help marginalised children catch up and mitigate the disparity when they reach early adolescence.
KW - Circumstances
KW - Cognitive development
KW - Random forests
KW - Receptive vocabulary
KW - Young lives
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U2 - 10.1016/j.cogdev.2021.101031
DO - 10.1016/j.cogdev.2021.101031
M3 - Article
AN - SCOPUS:85101612251
VL - 58
JO - Cognitive Development
JF - Cognitive Development
SN - 0885-2014
M1 - 101031
ER -