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描述、预测和因果关系:儿童和青少年发展研究的方法学挑战。

Description, prediction and causation: Methodological challenges of studying child and adolescent development.

机构信息

Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, The Netherlands.

Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, The Netherlands.

出版信息

Dev Cogn Neurosci. 2020 Dec;46:100867. doi: 10.1016/j.dcn.2020.100867. Epub 2020 Oct 24.

Abstract

Scientific research can be categorized into: a) descriptive research, with the main goal to summarize characteristics of a group (or person); b) predictive research, with the main goal to forecast future outcomes that can be used for screening, selection, or monitoring; and c) explanatory research, with the main goal to understand the underlying causal mechanism, which can then be used to develop interventions. Since each goal requires different research methods in terms of design, operationalization, model building and evaluation, it should form an important basis for decisions on how to set up and execute a study. To determine the extent to which developmental research is motivated by each goal and how this aligns with the research designs that are used, we evaluated 100 publications from the Consortium on Individual Development (CID). This analysis shows that the match between research goal and research design is not always optimal. We discuss alternative techniques, which are not yet part of the developmental scientist's standard toolbox, but that may help bridge some of the lurking gaps that developmental scientists encounter between their research design and their research goal. These include unsupervised and supervised machine learning, directed acyclical graphs, Mendelian randomization, and target trials.

摘要

科学研究可以分为

a)描述性研究,主要目的是总结群体(或个体)的特征;b)预测性研究,主要目的是预测可用于筛选、选择或监测的未来结果;c)解释性研究,主要目的是了解潜在的因果机制,然后可以用于开发干预措施。由于每个目标在设计、操作化、模型构建和评估方面都需要不同的研究方法,因此它应该成为决定如何设计和执行研究的重要基础。为了确定发展研究在多大程度上受到每个目标的驱动,以及这与所使用的研究设计如何一致,我们评估了个体发展联盟(CID)的 100 篇出版物。这项分析表明,研究目标和研究设计之间的匹配并不总是最佳的。我们讨论了替代技术,这些技术尚未成为发展科学家标准工具包的一部分,但可能有助于弥合发展科学家在研究设计和研究目标之间遇到的一些潜在差距。这些技术包括无监督和有监督机器学习、有向无环图、孟德尔随机化和目标试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27f7/7670214/6955fba8c589/gr1.jpg

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