TY - JOUR
T1 - Understanding Personality through Patterns of Daily Socializing
T2 - Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data
AU - Danvers, Alexander F.
AU - Sbarra, David A.
AU - Mehl, Matthias R.
N1 - Publisher Copyright:
© 2020 European Association of Personality Psychology
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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/.
AB - 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/.
KW - ambulatory assessment
KW - dynamic systems
KW - electronically activated recorder
KW - personality
KW - social interaction
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U2 - 10.1002/per.2282
DO - 10.1002/per.2282
M3 - Article
AN - SCOPUS:85088822502
SN - 0890-2070
VL - 34
SP - 777
EP - 793
JO - European Journal of Personality
JF - European Journal of Personality
IS - 5
ER -