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在数字化世界中的荟萃分析:一步步入门指南。

Meta-analysis in a digitalized world: A step-by-step primer.

机构信息

Research Methods, Assessment, and iScience, Department of Psychology, University of Konstanz, Konstanz, Germany.

出版信息

Behav Res Methods. 2024 Oct;56(7):1-21. doi: 10.3758/s13428-024-02374-8. Epub 2024 Apr 4.

Abstract

In recent years, much research and many data sources have become digital. Some advantages of digital or Internet-based research, compared to traditional lab research (e.g., comprehensive data collection and storage, availability of data) are ideal for an improved meta-analyses approach.In the meantime, in meta-analyses research, different types of meta-analyses have been developed to provide research syntheses with accurate quantitative estimations. Due to its rich and unique palette of corrections, we recommend to using the Schmidt and Hunter approach for meta-analyses in a digitalized world. Our primer shows in a step-by-step fashion how to conduct a high quality meta-analysis considering digital data and highlights the most obvious pitfalls (e.g., using only a bare-bones meta-analysis, no data comparison) not only in aggregation of the data, but also in the literature search and coding procedure which are essential steps in any meta-analysis. Thus, this primer of meta-analyses is especially suited for a situation where much of future research is headed to: digital research. To map Internet-based research and to reveal any research gap, we further synthesize meta-analyses on Internet-based research (15 articles containing 24 different meta-analyses, on 745 studies, with 1,601 effect sizes), resulting in the first mega meta-analysis of the field. We found a lack of individual participant data (e.g., age and nationality). Hence, we provide a primer for high-quality meta-analyses and mega meta-analyses that applies to much of coming research and also basic hands-on knowledge to conduct or judge the quality of a meta-analyses in a digitalized world.

摘要

近年来,大量的研究和数据源已经数字化。与传统的实验室研究相比,数字或基于互联网的研究具有一些优势(例如,全面的数据收集和存储,数据的可用性),这对于改进元分析方法非常理想。同时,在元分析研究中,已经开发出不同类型的元分析方法,以提供更准确的定量估计的研究综合。由于其丰富而独特的校正方法,我们建议在数字化世界中使用施密特和亨特方法进行元分析。我们的入门指南以逐步的方式展示了如何考虑数字化数据进行高质量的元分析,并强调了最明显的陷阱(例如,仅进行基本的元分析,不进行数据比较),不仅在数据聚合中,而且在文献搜索和编码过程中也是如此,这些都是任何元分析的重要步骤。因此,这本元分析入门指南特别适合未来研究的方向:数字化研究。为了绘制基于互联网的研究并揭示任何研究差距,我们进一步对基于互联网的研究进行元分析综合(包含 24 项不同元分析的 15 篇文章,涉及 745 项研究,包含 1601 个效应量),从而进行了该领域的第一次 mega meta 分析。我们发现缺乏个体参与者数据(例如,年龄和国籍)。因此,我们提供了适用于未来大部分研究的高质量元分析和 mega meta 分析的入门指南,以及在数字化世界中进行或判断元分析质量的基本实践知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c6c/11362208/40715a743bfd/13428_2024_2374_Fig1_HTML.jpg

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