Matching Items (5)
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- All Subjects: Infant
- All Subjects: Psychology--Research--Methodology.
- Creators: Mackinnon, David P
Description
The development of self-regulation is believed to play a crucial role in predicting later psychopathology and is believed to begin in early childhood. The early postpartum period is particularly important in laying the groundwork for later self-regulation as infants' dispositional traits interact with caregivers' co-regulatory behaviors to produce the earliest forms of self-regulation. Moreover, although emerging literature suggests that infants' exposure to maternal stress even before birth may be integral in determining children's self-regulatory capacities, the complex pathways that characterize these developmental processes remain unclear. The current study considers the complex, transactional processes in a high-risk, Mexican American sample. Data were collected from 305 Mexican American infants and their mothers during prenatal, 6- and 12-week home interviews. Mother self-reports of stress were obtained prenatally between 34-37 weeks gestation. Mother reports of infant temperamental negativity and surgency were obtained at 6-weeks as were observed global ratings of maternal sensitivity during a structured peek-a-boo task. Microcoded ratings of infants' engagement orienting and self-comforting behaviors were obtained during the 12-week peek-a-boo task. Study findings suggest that self-comforting and orienting behaviors help to modulate infants' experiences of distress, and also that prenatal stress influences infants' engagement in each of those regulatory behaviors, both directly by influence tendencies to engage in orienting behaviors and indirectly by programming higher levels of infant negativity and surgency, both of which may confer risk for later regulatory disadvantage. Advancing our understandings about the nature of these developmental pathways could have significant implications for targets of early intervention in this high-risk population.
ContributorsLin, Betty (Author) / Crnic, Keith A (Thesis advisor) / Lemery-Chalfant, Kathryn S (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation examines a planned missing data design in the context of mediational analysis. The study considered a scenario in which the high cost of an expensive mediator limited sample size, but in which less expensive mediators could be gathered on a larger sample size. Simulated multivariate normal data were generated from a latent variable mediation model with three observed indicator variables, M1, M2, and M3. Planned missingness was implemented on M1 under the missing completely at random mechanism. Five analysis methods were employed: latent variable mediation model with all three mediators as indicators of a latent construct (Method 1), auxiliary variable model with M1 as the mediator and M2 and M3 as auxiliary variables (Method 2), auxiliary variable model with M1 as the mediator and M2 as a single auxiliary variable (Method 3), maximum likelihood estimation including all available data but incorporating only mediator M1 (Method 4), and listwise deletion (Method 5).
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 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.
ContributorsBaraldi, Amanda N (Author) / Enders, Craig K. (Thesis advisor) / Mackinnon, David P (Thesis advisor) / Aiken, Leona S. (Committee member) / Tein, Jenn-Yun (Committee member) / Arizona State University (Publisher)
Created2015
Description
The parent-child relationship is one of the earliest and most formative experiences for social and emotional development. Synchrony, defined as the rhythmic patterning and quality of mutual affect, engagement, and physiological attunement, has been identified as a critical quality of a healthy mother-infant relationship. Although the salience of the quality of family interaction has been well-established, clinical and developmental research has varied widely in methods for observing and identifying influential aspects of synchrony. In addition, modern dynamic perspectives presume multiple factors converge in a complex system influenced by both nature and nurture, in which individual traits, behavior, and environment are inextricably intertwined within the system of dyadic relational units.
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.
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.
ContributorsCoburn, Shayna Skelley (Author) / Crnic, Keith A (Thesis advisor) / Dishion, Thomas J (Committee member) / Mackinnon, David P (Committee member) / Luecken, Linda J. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Time metric is an important consideration for all longitudinal models because it can influence the interpretation of estimates, parameter estimate accuracy, and model convergence in longitudinal models with latent variables. Currently, the literature on latent difference score (LDS) models does not discuss the importance of time metric. Furthermore, there is little research using simulations to investigate LDS models. This study examined the influence of time metric on model estimation, interpretation, parameter estimate accuracy, and convergence in LDS models using empirical simulations. Results indicated that for a time structure with a true time metric where participants had different starting points and unequally spaced intervals, LDS models fit with a restructured and less informative time metric resulted in biased parameter estimates. However, models examined using the true time metric were less likely to converge than models using the restructured time metric, likely due to missing data. Where participants had different starting points but equally spaced intervals, LDS models fit with a restructured time metric resulted in biased estimates of intercept means, but all other parameter estimates were unbiased, and models examined using the true time metric had less convergence than the restructured time metric as well due to missing data. The findings of this study support prior research on time metric in longitudinal models, and further research should examine these findings under alternative conditions. The importance of these findings for substantive researchers is discussed.
ContributorsO'Rourke, Holly P (Author) / Grimm, Kevin J. (Thesis advisor) / Mackinnon, David P (Thesis advisor) / Chassin, Laurie (Committee member) / Aiken, Leona S. (Committee member) / Arizona State University (Publisher)
Created2016