Content‐based techniques for credibility assessment (Criteria‐Based Content Analysis [CBCA], Reality Monitoring [RM]) have been shown to distinguish between experience‐based and fabricated statements in previous meta‐analyses. New simulations raised the question whether these results are reliable revealing that using meta‐analytic methods on biased datasets lead to false‐positive rates of up to 100%. By assessing the performance of and applying different bias‐correcting meta‐analytic methods on a set of 71 studies we aimed for more precise effect size estimates. According to the sole bias‐correcting meta‐analytic method that performed well under a priori specified boundary conditions, CBCA and RM distinguished between experience‐based and fabricated statements. However, great heterogeneity limited precise point estimation (i.e., moderate to large effects). In contrast, Scientific Content Analysis (SCAN)—another content‐based technique tested—failed to discriminate between truth and lies. It is discussed how the gap between research on and forensic application of content‐based credibility assessment may be narrowed.

中文翻译：

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基于内容的技术在可信度评估中的有效性-如何将研究偏见纳入扩展的荟萃分析？

在以前的荟萃分析中，基于内容的可信度评估技术（基于标准的内容分析[CBCA]，现实监控[RM]）已被证明可以区分基于经验的陈述和虚假陈述。新的模拟提出了一个疑问，即这些结果是否可靠，这表明在偏倚的数据集上使用荟萃分析方法会导致假阳性率高达100％。通过评估71套研究的性能并应用不同的偏倚校正荟萃分析方法，我们旨在获得更精确的效应量估算值。根据在先验指定的边界条件下表现良好的唯一偏差校正元分析方法，CBCA和RM区分了基于经验的陈述和捏造的陈述。但是，巨大的异质性限制了精确的点估计（即，中等到较大的效果）。相比之下，科学内容分析（SCAN）（另一种基于内容的测试技术）未能区分真相与谎言。讨论了如何缩小基于内容的可信度评估的研究与法医应用之间的差距。