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.

科学研究可分为： a）描述性研究，主要目的是总结一个群体（或个人）的特征；b)预测性研究，主要目标是预测可用于筛选、选择或监测的未来结果；c)解释性研究，主要目标是了解潜在的因果机制，然后可用于制定干预措施。由于每个目标在设计、实施、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建立和执行研究的重要基础。为了确定每个目标在多大程度上推动发展研究以及这如何与所使用的研究设计保持一致，我们评估了个人发展联盟(CID) 的 100 份出版物。该分析表明，研究目标和研究设计之间的匹配并不总是最佳的。我们讨论替代技术，这些技术尚未成为发展科学家标准工具箱的一部分，但这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。其中包括无监督和监督机器学习、有向非循环图、孟德尔随机化和目标试验。