Ambulatory assessment methods provide a rich approach for studying daily behaviour. Too often, however, these data are analysed in terms of averages, neglecting patterning of this behaviour over time. This paper describes recurrence quantification analysis (RQA), a non‐linear time series technique for analysing dynamic systems, as a method for analysing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively assessed social behaviour (e.g. talking to another person) coded from the Electronically Activated Recorder. Conceptual interpretations of RQA parameters, and an analysis of Electronically Activated Recorder data in adults going through a marital separation, are provided. Using machine learning techniques to avoid model overfitting, we find that adding RQA parameters to models that include just average amount of time spent talking (a static measure) improves prediction of four Big Five personality traits: extraversion, neuroticism, conscientiousness, and openness. Our strongest results suggest that a combination of average amount of time spent talking and four RQA parameters yield an R² = .09 for neuroticism. Neuroticism is shown to be associated with shorter periods of extended conversation (periods of at least 12 minutes), demonstrating the utility of RQA to identify new relationships between personality and patterns of daily behaviour. Materials: https://osf.io/5nkr9/. © 2020 European Association of Personality Psychology

中文翻译：

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通过日常社交模式了解人格：将递归量化分析应用于自然观察的纵向纵向社会互动数据

动态评估方法为研究日常行为提供了丰富的方法。但是，这些数据经常以平均值进行分析，而忽略了这种行为随时间的变化。本文介绍递归量化分析（RQA），这是一种用于分析动态系统的非线性时间序列技术，它是一种用于分析密集型纵向动态评估数据模式的方法。我们将RQA应用于由电子激活记录器编码的客观评估的社交行为（例如与他人交谈）。提供了RQA参数的概念性解释，以及对经过婚姻分离的成年人进行电子激活记录器数据的分析。使用机器学习技术来避免模型过度拟合，我们发现，将RQA参数添加到仅包含平均谈话时间（静态度量）的模型中，可以改善对四大五种人格特质的预测：外向性，神经质，尽责性和开放性。我们最强的结果表明，平均通话时间和四个RQA参数共同产生了 对于神经质，R ² = .09。神经质被证明与更长时间的交谈（至少12分钟）有关，这表明RQA可以用于识别人格与日常行为之间的新关系。资料：https：//osf.io/5nkr9/。©2020欧洲人格心理学协会