Purpose

Behaviours occur across complex, dynamic timelines. Research methods to analyse these complex timelines have repeatedly used behaviour sequence analysis (BSA) as a primary method. Traditional BSA outputs, however, are limited in that they do not show how prevalent a behaviour sequence is throughout a sample or group. Until now, how many people in a sample showed the sequence was not analysed and reported. This paper aims to provide a new metric to calculate prevalence scores in BSA data sets.

Design/methodology/approach

Open access recorded responses including nonverbal communication of deceptive and truthful individuals were analysed initially with a standard BSA approach and then the prevalence scores of transitions were calculated.

Findings

Prevalence scores offered new insights into the distribution of sequences across groups. The prevalence score showed differences in which transitions were seen across the truthful and guilty samples. This offers new approaches to analysing nonverbal communication.

Originality/value

This is the first paper to provide a prevalence score for BSA research and show how it can be used in applied research. The current prevalence score metric is provided and suggested for all future research into sequences.

中文翻译：

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流行率分数：行为序列分析的补充

目的

行为发生在复杂、动态的时间线上。分析这些复杂时间线的研究方法多次使用行为序列分析（BSA）作为主要方法。然而，传统 BSA 输出的局限性在于它们无法显示行为序列在整个样本或群体中的普遍程度。到目前为止，样本中有多少人显示出该序列尚未得到分析和报告。本文旨在提供一种新的指标来计算 BSA 数据集中的患病率分数。

设计/方法论/途径

开放获取记录的反应，包括欺骗性和诚实个体的非语言交流，最初使用标准 BSA 方法进行分析，然后计算转换的患病率分数。

发现

患病率分数为跨群体的序列分布提供了新的见解。患病率分数显示真实样本和有罪样本之间的转变存在差异。这提供了分析非语言交流的新方法。

原创性/价值

这是第一篇为 BSA 研究提供流行度评分并展示如何将其用于应用研究的论文。提供了当前的患病率评分指标，并为所有未来的序列研究提供建议。