A data driven methodology for social science research with left-behind children as a case study.
For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research and social issues, which may outperform standard r...
Main Authors: | Chao Wu, Guolong Wang, Simon Hu, Yue Liu, Hong Mi, Ye Zhou, Yi-Ke Guo, Tongtong Song |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0242483 |
Similar Items
-
Alone and “left behind”: a case study of “left-behind children” in rural China
by: Yang Hong, et al.
Published: (2019-01-01) -
A comparative analysis of suicide attempts in left-behind children and non-left-behind children in rural China.
by: Hongjuan Chang, et al.
Published: (2017-01-01) -
Determination of left-behind children
Published: (2015) -
Social Cognitive Domain Coordination in Left-Behind Children: A Comparative Study of Left-Behind and Non-Left-Behind Children in Rural China
by: Jianjin Liu
Published: (2016-09-01) -
Social Cognitive Domain Coordination in Left-Behind Children: A Comparative Study of Left-Behind and Non-Left-Behind Children in Rural China
by: Jianjin Liu
Published: (2016-09-01)