欺骗检测分析教程或:我是如何学会停止汇总真实性判断并接受信号检测理论混合模型的?

IF 1.2 3区 心理学 Q4 PSYCHOLOGY, SOCIAL
Mircea Zloteanu, Matti Vuorre
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引用次数: 0

摘要

欺骗检测研究历来依赖于对反应准确性的因子分析来进行推断。然而,这种做法忽略了重要的变异性来源,导致了潜在的误导性估计,并可能将反应偏差与参与者从真话中识别谎言的潜在敏感性混为一谈。我们展示了一种采用信号检测理论(SDT)和广义线性混合模型框架的替代方法,以解决这些局限性。这种 SDT 方法包含了评判者和发送者的个体差异,而个体差异是欺骗研究中虚假结论的主要来源。通过避免数据转换和汇总,这种方法优于传统方法,能提供更多信息和更可靠的效果估计。这个成熟的框架为研究人员提供了分析欺骗数据的强大工具,并加深了我们对真实性判断的理解。所有代码和数据均可公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks important sources of variability resulting in potentially misleading estimates and may conflate response bias with participants’ underlying sensitivity to detect lies from truths. We showcase an alternative approach using a signal detection theory (SDT) with generalized linear mixed models framework to address these limitations. This SDT approach incorporates individual differences from both judges and senders, which are a principal source of spurious findings in deception research. By avoiding data transformations and aggregations, this methodology outperforms traditional methods and provides more informative and reliable effect estimates. This well-established framework offers researchers a powerful tool for analyzing deception data and advances our understanding of veracity judgments. All code and data are openly available.

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来源期刊
Journal of Nonverbal Behavior
Journal of Nonverbal Behavior PSYCHOLOGY, SOCIAL-
CiteScore
4.80
自引率
9.50%
发文量
27
期刊介绍: Journal of Nonverbal Behavior presents peer-reviewed original theoretical and empirical research on all major areas of nonverbal behavior. Specific topics include paralanguage, proxemics, facial expressions, eye contact, face-to-face interaction, and nonverbal emotional expression, as well as other subjects which contribute to the scientific understanding of nonverbal processes and behavior.
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