A meta-analysis of mother-child synchrony in respiratory sinus arrhythmia and contextual risk.

Biobehavioral frameworks of attachment posit that mother-child dyads engage in physiological synchrony that is uniquely formative for children's neurobiological, social, and emotional development. Much of the work on mother-child physiological synchrony has focused on respiratory sinus arrhythmia (RSA). However, the strength of the existing evidence for mother-child RSA synchrony during interaction is unclear. Using meta-analysis, we summarized results from 12 eligible studies comprising 14 samples and 1201 children ranging from infancy to adolescence (Mage  = 5.68 years, SD = 4.13, range = 0.4-17 years) and their mothers. We found that there was a statistically significant, albeit modest, positive within-dyad association between mother and child fluctuations in RSA. There also was evidence for significant heterogeneity across studies. Less mother-child RSA synchrony was observed in high-risk samples characterized by clinical difficulties, history of maltreatment, or socioeconomic disadvantage. We did not find that mother-child RSA synchrony significantly differed by task context, mean child age, or by epoch length for computing RSA. Collectively, these findings suggest that mother-child dyads show correspondence in their fluctuations in RSA, and that RSA synchrony is disrupted in high-risk contexts. Future directions and implications for the study of parent-child physiological synchrony are discussed.

Value of sample size for computation of the Bayesian information criterion (BIC) in multilevel modeling.

The Bayesian information criterion (BIC) can be useful for model selection within multilevel-modeling studies. However, the formula for the BIC requires a value for sample size, which is unclear in multilevel models, since sample size is observed for at least two levels. In the present study, we used simulated data to evaluate the rate of false positives and the power when the level 1 sample size, the effective sample size, and the level 2 sample size were used as the sample size value, under various levels of sample size and intraclass correlation coefficient values. The results indicated that the appropriate value for sample size depends on the model and test being conducted. On the basis of the scenarios investigated, we recommend using a BIC that has different penalty terms for each level of the model, based on the number of fixed effects at each level and the level-based sample sizes.

Contemporary applications of moderation analysis in counseling psychology.

Moderation analyses represent a statistical technique that can be particularly useful for counseling psychology researchers. This technique allows researchers to assess whether a given relationship depends on a third, moderating variable. The present article provides a brief overview of moderation, followed by 3 broad areas relevant for research: designing a study, analyzing the data, and interpreting results. Particular focus is applied to the topic of latent variable moderation, including an applied example with real data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).