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- All Subjects: Mediation (Statistics)
- All Subjects: Dynamics
- All Subjects: Social sciences--Statistical methods.
- Creators: Mackinnon, David P
- Creators: Amazeen, Polemnia
The main outcome of interest was empirical power to detect the mediated effect. The main effects of mediation effect size, sample size, and missing data rate performed as expected with power increasing for increasing mediation effect sizes, increasing sample sizes, and decreasing missing data rates. Consistent with expectations, power was the greatest for analysis methods that included all three mediators, and power decreased with analysis methods that included less information. Across all design cells relative to the complete data condition, Method 1 with 20% missingness on M1 produced only 2.06% loss in power for the mediated effect; with 50% missingness, 6.02% loss; and 80% missingess, only 11.86% loss. Method 2 exhibited 20.72% power loss at 80% missingness, even though the total amount of data utilized was the same as Method 1. Methods 3 – 5 exhibited greater power loss. Compared to an average power loss of 11.55% across all levels of missingness for Method 1, average power losses for Methods 3, 4, and 5 were 23.87%, 29.35%, and 32.40%, respectively. In conclusion, planned missingness in a multiple mediator design may permit higher quality characterization of the mediator construct at feasible cost.
The present study aimed to directly examine and compare synchrony from three distinct approaches: observed microanalytic behavioral sequences, observed global dyadic qualities, and physiological attunement between mothers and infants. The sample consisted of 323 Mexican American mothers and their infants followed from the third trimester of pregnancy through the first year of life. Mothers were interviewed prenatally, observed at a home visit at 12 weeks postpartum, and were finally interviewed for child social-emotional problems at child age 12 months. Specific aspects of synchrony (microanalytical, global, and physiological) were examined separately as well as together to identify comparable and divergent qualities within the construct.
Findings indicated that multiple perspectives on synchrony are best examined together, but as independent qualities to account for varying characteristics captured by divergent systems. Dyadic relationships characterized by higher reciprocity, more time and flexibility in mutual non-negative engagement, and less tendency to enter negative or unengaged states were associated with fewer child social-emotional problems at child age 12 months. Lower infant cortisol was associated with higher levels of externalizing problems, and smaller differences between mother and child cortisol were associated with higher levels of child dysregulation. Results underscore the complex but important nature of synchrony as a salient mechanism underlying the social-emotional growth of children. A mutually engaged, non-negative, and reciprocal environment lays the foundation for the successful social and self-regulatory competence of infants in the first year of life.
A dynamical approach is used to avoid isolating systems and instead view systems as interacting together. The current study applied a dynamical approach to heart rate variability and personality. There were two main research questions that this study sought to answer with a dynamical analysis of heart rate variability and personality: “Can we listen to a heartbeat and draw connections to behavior and personality?” and “Is dynamical analysis more effective than traditional analysis at finding correlations between heart rate variability and personality?” To answer these questions a dynamical analysis of heart rate variability was conducted (detrended fluctuation analysis; DFA) along with traditional analysis (standard deviations of NN intervals, SDNN, and root mean squared of successive deviations, RMSSD) and then correlations between heart rate variability measures and personality traits from the Big Five Inventory, Positive and Negative Affect schedule, and State-Trait Anxiety Inventory were examined. Data for this study came from the Rapid Automatic & Adaptive Model for Performance Prediction (RAAMP2) Dataset that was part of The Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. There were no statistically significant correlations between heart rate variability and personality. However, there were notable correlations between extraversion and SDNN and RMSSD and between positive affect and SDNN and RMSSD. We found that SDNN and RMSSD were more closely correlated to each other compared to DFA to either measure. This suggests that DFA can provide information that SDNN and RMSSD do not. Future research can explore dynamic analysis of heart rate variability and other nested systems.