Division of Developmental Behavioral Pediatrics, NeuroDevelopmental Science Center, Akron Children's Hospital, Akron, OH.
Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN.
J Dev Behav Pediatr. 2021 May 1;42(4):322-330. doi: 10.1097/DBP.0000000000000915.
Secondary analysis of existing large, national data sets is a powerful method to address many of the complex, key research questions in developmental behavioral pediatrics (DBP). Major advantages include decreasing the time needed to complete a study and reducing expenses associated with research by eliminating the need to collect primary data. It can also increase the generalizability of research and, with some data sets, provide national estimates that may form the basis for developing policy. However, few resources are available to direct researchers who seek to develop expertise in this area. This study aims to guide investigators with limited experience in this area who wish to improve their skills in performing secondary analysis of existing large data sets. This study provides direction on the steps to perform secondary analysis of existing data sets. It describes where and how data sets can be identified to answer questions of interest to DBP. Finally, it offers an overview of a number of data sets relevant to DBP.
二次分析现有的大型国家数据集是解决发展行为儿科学（DBP）中许多复杂关键研究问题的有力方法。主要优点包括减少完成研究所需的时间，并通过避免收集原始数据来降低与研究相关的费用。它还可以提高研究的普遍性，并且对于某些数据集，可以提供可能成为制定政策基础的全国性估计数。但是，很少有资源可以指导那些希望在这一领域发展专业知识的研究人员。本研究旨在指导在这一领域经验有限的研究人员提高其对现有大型数据集进行二次分析的技能。本研究提供了对现有数据集进行二次分析的步骤指导。它描述了如何以及从何处确定数据集来回答 DBP 感兴趣的问题。最后，它概述了一些与 DBP 相关的数据集。
