Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany.
Dev Cogn Neurosci. 2021 Feb;47:100902. doi: 10.1016/j.dcn.2020.100902. Epub 2020 Dec 17.
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
许多旨在提高研究结果的可重复性和可再现性的工作流程和工具已经被提出。在这篇综述中，我们讨论了这些努力为发展认知神经科学领域，特别是发展神经影像学带来的机会。我们重点讨论了与统计能力以及数据分析的灵活性和透明度广泛相关的问题。与统计能力相关的关键考虑因素包括在招募和测试年轻人群方面的挑战、如何提高小样本研究的价值，以及与使用大规模数据集相关的机会和挑战。发展研究涉及到一些挑战，例如关于年龄分组、生命历程建模、纵向变化分析以及可以以多种方式处理和分析的数据的选择。数据获取、分析和描述的灵活性可能会极大地影响结果。我们讨论了提高发展神经影像学透明度的方法，以及预注册如何提高方法学的严谨性。虽然概述了在数据收集之前、期间和之后可能出现的挑战和问题，但也强调了一些解决方案和资源，这些都有助于克服其中的一些问题。由于有用的工具和技术的数量不断增加，我们强调了许多实践可以逐步实施的事实。
