A data driven methodology for social science research with left-behind children as a case study.

ABSTRACT: 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 regression methods in some cases. Under the circumstances, this article proposes a methodological workflow for data analysis by machine learning techniques that have the possibility to be widely applied in social issues. Specifically, the workflow tries to uncover the natural mechanisms behind the social issues through a data-driven perspective from feature selection to model building. The advantage of data-driven techniques in feature selection is that the workflow can be built without so much r

SUBMITTER: Wu C 

PROVIDER: S-EPMC7678991 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature