The temporal dimension in the understanding of maternal sensitivity in caregiver-infant interactions: The ‘Early Mother-Child Interaction Coding System’

Caregiver-child interaction is known to play a central role in children’s socioemotional development, yet the diversity of measures commonly used to evaluate it call into question the nature of the construct being assessed. Contingency within caregiver-child interactions has been identified as a key dyadic process that requires taking a temporal perspective to evaluating interactions. The Early Mother-Child Interaction Coding System is a psychometrically sound measure that sequentially code the flow of interactive and non-interactive behaviours within a caregiver-infant system. Its use of the framework of nonlinear dynamical systems (NDS) expands our understanding of contingent caregiver-infant dyadic behaviour.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.