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  • 标题:Avoiding Methodological Biases in Meta-Analysis
  • 作者:Esther Kaufmann ; Ulf-Dietrich Reips ; Katharina Maag Merki
  • 期刊名称:Zeitschrift für Psychologie
  • 印刷版ISSN:2190-8370
  • 电子版ISSN:2151-2604
  • 出版年度:2016
  • 卷号:224
  • 期号:3
  • 页码:157-167
  • DOI:10.1027/2151-2604/a000251
  • 语种:English
  • 出版社:Hogrefe Publishing
  • 摘要: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 current discussions within the psychological community on study replication. Keywords:  meta-analysis , ecological fallacy , online versus offline , Simpson’s paradox , replication
  • 关键词:meta-analysis; ecological fallacy; online versus offline; Simpson’s paradox; replication
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