Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis (RQA) to Naturalistically Observed Intensive Longitudinal Social Interaction Data

Ambulatory assessment methods provide a rich approach for studying daily behavior. Too often, however, these data are analyzed in terms of averages, neglecting patterning of this behavior over time. This paper describes Recurrence Quantification Analysis (RQA), a non-linear time series technique for analyzing dynamic systems, as a method for analyzing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively-assessed social behavior (e.g. talking to another person) coded from the Electronically Activated Recorder (EAR). Conceptual interpretations of RQA parameters, and an analysis of EAR 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 behavior. Materials: https://osf.io/5nkr9/