Factor structure of the Mini-Maternal Behavior Q-Sort and associations with infant attachment: Informing precision in research and intervention

We examined the factor structure of parental sensitivity to infants as assessed by the Mini-Maternal Behavior Q-Sort (Mini-MBQS), a 25-item short-form of the original 90-item MBQS. We aimed to: (1) identify latent factors of the Mini-MBQS; and (2) validate each factor by testing associations with infant attachment classifications. Data on parent-infant dyads (n = 313; 222 mothers with 281 children, 29 fathers with 32 children) were drawn from a three-generation Australian cohort study. Exploratory Factor Analysis and Exploratory Structural Equation Modelling examined the structure of the Mini-MBQS. Two latent Mini-MBQS factors were identified, requiring 8 of 25 original items: (1) Attention and Responsiveness and (2) Contingency in Interactions. Infants with insecure attachment classifications had parents with lower sensitivity across both factors relative to infants classified secure. In particular, infants with resistant attachment classifications had parents with notably low Contingency in Interactions scores. Infants with disorganised attachment classifications had parents with the lowest relative sensitivity across both factors, and in these dyads Attention and Responsiveness scores were especially low. Results provide an empirically derived factor structure for the Mini-MBQS. Two subscales, each with significant infant attachment associations, may improve precision in clinical intervention and research translation.     

Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts

High levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, but the exact nature of the dose-response relationship is less well understood. To investigate this relationship, data were assembled from six longitudinal birth cohort studies examining the effects of PAE on cognitive outcomes from early school age through adolescence. Structural equation models (SEMs) are a natural approach to consider, because of the way they conceptualise multiple observed outcomes as relating to an underlying latent variable of interest, which can then be modelled as a function of exposure and other predictors of interest. However, conventional SEMs could not be fitted in this context because slightly different outcome measures were used in the six studies. In this paper we propose a multi-group Bayesian SEM that maps the unobserved cognition variable to a broad range of observed outcomes. The relation between these variables and PAE is then examined while controlling for potential confounders via propensity score adjustment. By examining different possible dose-response functions, the proposed framework is used to investigate whether there is a threshold PAE level that results in minimal cognitive deficit.