避免元分析中的方法论偏差:在线与离线个人参与者数据(IPD)在教育心理学中的应用

IF 2 4区 心理学 Q2 PSYCHOLOGY, MULTIDISCIPLINARY
Esther Kaufmann, Ulf-Dietrich Reips, K. M. Merki
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引用次数: 23

摘要

摘要个体参与者数据(IPD)荟萃分析是荟萃分析的黄金标准。本文指出了IPD元分析相对于经典元分析的几个优势,如避免了聚集偏差(如生态谬误或辛普森悖论),并展示了如何通过基于互联网的研究来克服其两个主要缺点(时间和成本)。理想情况下,我们建议进行IPD荟萃分析,考虑在线与离线数据收集过程并检查数据质量。通过全面的文献检索,我们调查了在教育心理学领域发表的IPD荟萃分析是否已经遵循了这些建议;但事实并非如此。因此,本文论证了理想的教师判断准确性元分析的特点,并将其与最近关于该主题的元分析联系起来。这些建议对meta分析研究者以及meta分析的读者和审稿人都很重要。我们的论文也与当前的…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Avoiding Methodological Biases in Meta-Analysis : Use of Online Versus Offline Individual Participant Data (IPD) in Educational Psychology
Abstract. Individual participant data (IPD) meta-analysis is the gold standard of meta-analyses. This paper points out several advantages of IPD meta-analysis over classical meta-analysis, such as avoiding aggregation bias (e.g., ecological fallacy or Simpson’s paradox) and shows how its two main disadvantages (time and cost) can be overcome through Internet-based research. Ideally, we recommend carrying out IPD meta-analyses that consider online versus offline data gathering processes and examine data quality. Through a comprehensive literature search, we investigated whether IPD meta-analyses published in the field of educational psychology already follow these recommendations; this was not the case. For this reason, the paper demonstrates characteristics of ideal meta-analysis on teachers’ judgment accuracy and links it to recent meta-analyses on that topic. The recommendations are important for meta-analysis researchers and for readers and reviewers of meta-analyses. Our paper is also relevant to curr...
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来源期刊
Zeitschrift Fur Psychologie-Journal of Psychology
Zeitschrift Fur Psychologie-Journal of Psychology PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
4.10
自引率
5.60%
发文量
37
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