Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data.

Abstract

In this article, we introduce dynamical correlation, a new method for quantifying synchrony between 2 variables with intensive longitudinal data. Dynamical correlation is a functional data analysis technique developed to measure the similarity of 2 curves. It has advantages over existing methods for studying synchrony, such as multilevel modeling. In particular, it is a nonparametric approach that does not require a prespecified functional form, and it places no assumption on homogeneity of the sample. Dynamical correlation can be easily estimated with irregularly spaced observations and tested to draw population-level inferences. We illustrate this flexible statistical technique with a simulation example and empirical data from an experiment examining interpersonal physiological synchrony between romantic partners. We discuss the advantages and limitations of the method, and how it can be extended and applied in psychological research. We also provide a set of R code for other researchers to estimate and test for dynamical correlation. (PsycINFO Database Record

Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies.

“…Thus, a primary strength of the model that we present is the flexibility to incorporate these cues, making the model particularly ideal for researchers intending to empirically test the processes underlying influence. This is especially important because relatively little work has examined these processes (see Liu et al, 2016). In this section, we now discuss how physiology of both partners, in combination with measured cues, can be incorporated into a single analytical approach.…”

“…Such approaches may therefore be particularly useful during exploratory phases of research when researchers are unsure whether an adequate number of time points have been measured and simply want to document whether similarity between physiological responses exists (e.g., Liu et al, 2016).…”

“…In addition, because they are recorded unobtrusively, physiological data allow researchers to measure psychological processes without disrupting the natural dynamics of an interaction, which is critical for collecting DYADIC PHYSIOLOGICAL INFLUENCE 4 ecologically valid interpersonal data (e.g., how upset people feel during a conflict conversation or how engaged they are during a negotiation task). Lastly, concomitant psychophysiological measurements of dyads and groups can lead to novel insights and theoretical advancements regarding interpersonal dynamics, such as how quickly emotions "spread" through social groups and how shared emotional experiences in classrooms improve learning for students.Given the number of benefits of collecting physiological data in dyadic interactions, interest in studying shared physiological experiences in interactions has increased, and so too has the number of methodological papers that discuss how dyadic physiological data can be analyzed (e.g., Butner, Amazeen, & Mulvey, 2005;Gates & Liu, 2016;Gottman, 1990;Helm, Sbarra, & Ferrer, 2012;Liu, Zhou, Palumbo, & Wang, 2016;McAssey, Helm, Fushing, Sbarra, & Ferrer, 2013). There have also been many theoretical and empirical papers that emphasize what psychological processes one can assess by examining similarities in partners' physiological states (e.g., Butler, 2011;Levenson & Ruef, 1992;Sbarra & Hazan, 2008;Waters, West, & Mendes, 2014; see Palumbo et al, 2016 and Timmons et al, 2015 for reviews).…”

“…We posit that for physiological influence to be related to a social process between two people, physiology in the sender must be associated with signals that the receiver detects, consciously or non-consciously, that potentiate influence. Specifically, these signals can either 1) be effortlessly or automatically detected without awareness (i.e., via a low-level route), such as sensorial signals like odor, touch, visual displays, or voice frequency (see Liu et al, 2016), or 2) require effortful and motivated detection (i.e., via a high-level route) such as understanding whether crying is due to excitement or anxiety, that might necessitate attention or experience in decoding affective responses.Principle 3 is predicated on two psychological processes that are thought to give rise to reading another person's psychological states during interpersonal encounters: one, the perceptivity of the receiver-the ability to be attuned to the sender's psychological states-and two, the expressivity of the sender-the quality and quantity of the cues that the sender gives off that reflect his or her psychological states (similar to Funder's RAM model; 1995, which refers to "good judges" and "good targets" in the study of accuracy, and also Gosling, Ko, Mannarelli, Physiological influence can occur in the absence of shared information about a partner's physiological state (e.g., when the two partners are in a separate room and are not communicating) but this process may reflect exposure to shared stimuli or similar physiological milieu and not a social process between the two individuals. Dyad researchers have long considered how a shared environment or stimulus can affect partners in simultaneous ways, which leads to interdependence in responses (e.g., "common fate model," also known as the latent group model; Gonzalez & Griffin, 2002;Kenny et al, 2006;Kenny & La Voie, 1985).…”

Directly assessing interpersonal RSA influences in the frequency domain: An illustration with generalized partial directed coherence

“…If researchers are interested in capturing interpersonal linkage in these trends, gPDC would not be appropriate. Instead, time domain methods for modeling multivariate change trajectories, such as growth curve analysis (Laurenceau & Bolger, 2005;Reed, Randall, Post, & Butler, 2013) and dynamical correlation (Liu, Zhou, Palumbo, & Wang, 2016), may be considered. Even if there is no systematic trend, the stationarity assumption may still be violated if influences between social partners change over time.…”