Quantification of nonverbal synchrony using linear time series analysis methods: Lack of convergent validity and evidence for facets of synchrony

• Published in: Behavior research methods 2019-02, Vol.51 (1), p.361-383
• Main Authors: Schoenherr, Désirée, Paulick, Jane, Worrack, Susanne, Strauss, Bernhard M., Rubel, Julian A., Schwartz, Brian, Deisenhofer, Anne-Katharina, Lutz, Wolfgang, Stangier, Ulrich, Altmann, Uwe
• Format: Article
• Language: English
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Factor analysis; Measurement methods; Psychology; Psychotherapy; Synchronization; Time series; Validity
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-018-1139-z

Nonverbal synchrony describes coordination of the nonverbal behavior of two interacting partners. Additionally, it seems to be important in human interactions, such as during psychotherapy. Currently, there are several options for the automated determination of synchrony based on linear time series analysis methods (TSAMs). However, investigations into whether the different methods measure the same construct have been missing. In this study, N = 84 patient–therapist dyads were videotaped during psychotherapy sessions. Motion energy analysis was used to assess body movements. We applied seven different TSAMs and recorded multiple output scores (average synchrony, maximum synchrony, and frequency of synchrony; in total, N = 16 scores). Convergent validity was examined using correlations of the output scores and exploratory factor analysis. Additionally, two criterion-based validations were conducted: investigations of concordant validity with a more generalized nonlinear method, and of the predictive validity of the synchrony scores for improvement in interpersonal problems at the end of therapy. We found that the synchrony measures only partially correlated with each other. The factor analysis did not support a common-factor model. A three-factor model with a second-order synchrony variable showed the best fit for eight of the selected synchrony scores. Only some synchrony scores were able to predict improvement at the end of therapy. We concluded that the considered TSAMs do not measure the same synchrony construct, but different facets of synchrony: the strength of synchrony of the total interaction, the strength of synchrony during synchronization intervals, and the frequency of synchrony.