Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother–Infant Interactions During Infant Immunization

Abstract

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

Harsh parenting, child behavior problems, and the dynamic coupling of parents’ and children’s positive behaviors.

“…Research suggests that the most effective assessment of coregulation processes involves measuring the dyad as a unit of analysis (Stifter & Rovine, 2015), employing a challenge that prompts a regulatory response by the (in this case, dyadic) system (Cole, Martin, & Dennis, 2004), and appreciating that the function of dyadic behavior may change in light of contextual demands (Lunkenheimer et al, 2012). Accordingly, we examined the coregulation of autonomy support and autonomous behavior during interactions in which mother and child were challenged to complete a task, with the dyad as the unit of analysis, and aggregated across two different interaction contexts, specifically a clean-up task and a teaching task.…”

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

“…Examples of such new models include extensions of the dynamic factor model with time-varying parameters using a state space approach (Chow, Zu, Shifren, & Zhang, 2011;Molenaar, De Gooijer, & Schmitz, 1992), an extension of the (multilevel) vector-autoregressive (VAR) model using threshold parameters representing, for example, emotion dynamics under decreased and increased negative affect (threshold autoregressive models; Haan-Rietdijk, Gottman, Bergeman, & Hamaker, 2014;Hamaker, Zhang, & Maas, 2009;Madhyastha, Hamaker, & Gottman, 2011), and regime switching models, in which different states of emotion dynamics can be specified (Frühwirth-Schnatter, 2006;Hamaker, Grasman, & Kamphuis, 2010;Stifter & Rovine, 2015). Additionally, exploratory tools have been developed to discover which aspects or periods of dyadic interactions show similar patterns (Boker, Rotondo, Xu, & King, 2002;Ferrer, Steele, & Hsieh, 2012;Hsieh, Ferrer, Chen, & Chow, 2010).…”

Developmental Methods for Emotion Dynamics

“…To examine developmental changes in micro time scale dynamics, multiple burst designs can involve repeating the same behavioral observation tasks at different ages (Cole, Lougheed, & Ram, 2018). Examples include repeated observations of mothers soothing infants during immunizations, a challenging emotional experience (Benson, Ram, & Stifter, 2018;Stifter & Rovine, 2015); young children during laboratory tasks that challenge their self-regulation (Helm, Ram, Cole, & Chow, 2016;Morales et al, 2018); children and parents during structured and unstructured observations (Stoolmiller, 2016;Stoolmiller & Snyder, 2014); and of parents and adolescents during conflict discussions (van der Giessen et al, 2013). It is critical to consider the potential for practice effects when using behavioral tasks repeatedly in multiple burst designs.…”