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,许多研究和许多数据源已经成为数字化。数字或基于互联网的研究的一些优势，与传统的实验室研究相比(例如，全面的数据收集和存储，数据的可用性)是改进荟萃分析方法的理想选择。同时,在荟萃分析研究中，已经开发了不同类型的荟萃分析，以提供具有准确定量估计的研究综合。由于其丰富而独特的修正调色板，我们建议在数字化世界中使用Schmidt和Hunter方法进行荟萃分析.我们的入门课程以逐步的方式展示了如何考虑数字数据进行高质量的荟萃分析，并突出了最明显的陷阱（例如，仅使用裸露的荟萃分析，没有数据比较)不仅在数据聚合中，而且在文献检索和编码过程中，这是任何荟萃分析中必不可少的步骤。因此，这种荟萃分析的入门特别适合于未来大部分研究将转向数字研究的情况。绘制基于互联网的研究地图并揭示任何研究差距，我们进一步综合了基于互联网的研究的荟萃分析(15篇文章包含24种不同的荟萃分析，在745项研究中，具有1,601个效果大小），导致了该领域的第一个大型荟萃分析。我们发现缺乏个体参与者数据(例如，年龄和国籍)。因此，我们为高质量的荟萃分析和大型荟萃分析提供了入门，适用于许多即将进行的研究，以及基本的动手知识，以在数字化世界中进行或判断荟萃分析的质量。
