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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 restriction of related knowledge and theory in social science. The advantage of using machine learning techniques in modelling is to uncover non-linear and complex relationships behind social issues. The main purpose of our methodological workflow is to find important fields relevant to the target and provide appropriate predictions. However, to explain the result still needs theory and knowledge from social science. In this paper, we trained a methodological workflow with left-behind children as the social issue case, and all steps and full results are included.

SUBMITTER: Wu C

PROVIDER: S-EPMC7678991 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Wu Chao C Wang Guolong G Hu Simon S Liu Yue Y Mi Hong H Zhou Ye Y Guo Yi-Ke YK Song Tongtong T

PloS one 20201120 11

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 applie ...[more]

PMID: 33216786

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Project description:Existing research has found that parental migration may negatively impact the psychological adjustment of left-behind children. However, limited longitudinal research has examined if and how future orientation (individual protective factor) and social support (contextual protective factor) are associated with the indicators of psychological adjustment (i.e., life satisfaction, school satisfaction, happiness, and loneliness) of left-behind children. In the current longitudinal study, we examined the differences in psychological adjustment between left-behind children and non-left behind children (comparison children) in rural areas, and explored the protective roles of future orientation and social support on the immediate (cross-sectional effects) and subsequent (lagged effects) status of psychological adjustment for both groups of children, respectively. The sample included 897 rural children (Mage = 14.09, SD = 1.40) who participated in two waves of surveys across six months. Among the participants, 227 were left-behind children with two parents migrating, 176 were with one parent migrating, and 485 were comparison children. Results showed that, (1) left-behind children reported lower levels of life satisfaction, school satisfaction, and happiness, as well as a higher level of loneliness in both waves; (2) After controlling for several demographics and characteristics of parental migration among left-behind children, future orientation significantly predicted life satisfaction, school satisfaction, and happiness in both cross-sectional and longitudinal regression models, as well as loneliness in the longitudinal regression analysis. Social support predicted immediate life satisfaction, school satisfaction, and happiness, as well as subsequent school satisfaction. Similar to left-behind children, comparison children who reported higher scores in future orientation, especially future expectation, were likely to have higher scores in most indicators of psychological adjustment measured at the same time and subsequently. However, social support seemed not exhibit as important in the immediate status of psychological adjustment of comparison children as that of left-behind children. Findings, implications, and limitations of the present study were discussed.

Project description:BackgroundSeveral studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.MethodsWe construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.FindingsFor the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.ConclusionsClassifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

Dataset Information

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

Publications

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

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