We apply the Hawkes process to the analysis of dyadic interaction. The Hawkes process is applicable to excitatory interactions, wherein the actions of each individual increase the probability of further actions in the near future. We consider the representation of the Hawkes process both as a conditional intensity function and as a cluster Poisson process. The former treats the probability of an action in continuous time via non-stationary distributions with arbitrarily long historical dependency, while the latter is conducive to maximum likelihood estimation using the EM algorithm. We first outline the interpretation of the Hawkes process in the dyadic context, and then illustrate its application with an example concerning email transactions in the work place.

Kenny has proposed a variance-components model for dyadic social interaction. His Social Relations model estimates variances and covariances from a round-robin of two-person interactions. The current paper presents a matrix formulation of the Social Relations model. It uses the formulation to derive exact and estimated standard errors for round-robin estimates of Social Relations parameters.

Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area is the model that was proposed by Gottman and Murray, which is based on nonlinear coupled difference equations. In this paper, it is shown that their model is a special case of the threshold autoregressive (TAR) model. As a consequence, we can make use of existing knowledge about TAR models with respect to parameter estimation, model alternatives and model selection. We propose a new estimation procedure and perform a simulation study to compare it to the estimation procedure developed by Gottman and Murray. In addition, we include an empirical example based on interaction data of three dyads.

Associations between trait dominance and cardiovascular reactivity were examined in previously unacquainted healthy men and women. Subjects participated in three mixed-gender dyadic interactions with the same partner while their cardiovascular responses were assessed. Among men, but not women, trait dominance was positively and significantly associated with systolic blood pressure reactivity. For men and women, diastolic blood pressure reactivity was positively and significantly associated with trait dominance while participants prepared to interact and with partner's trait dominance while they interacted. All effects held after controlling for trait hostility. Dominance merits attention as a correlate of cardiovascular reactivity, a finding that parallels emerging patterns in the cardiovascular disease literature. Gender and gender-related social factors as potential moderators of this relationship are discussed.