Matching Items (5)
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- All Subjects: Psychology--Statistical methods.
- All Subjects: Social skills in children
- Creators: Grimm, Kevin
Description
This paper investigates a relatively new analysis method for longitudinal data in the framework of functional data analysis. This approach treats longitudinal data as so-called sparse functional data. The first section of the paper introduces functional data and the general ideas of functional data analysis. The second section discusses the analysis of longitudinal data in the context of functional data analysis, while considering the unique characteristics of longitudinal data such, in particular sparseness and missing data. The third section introduces functional mixed-effects models that can handle these unique characteristics of sparseness and missingness. The next section discusses a preliminary simulation study conducted to examine the performance of a functional mixed-effects model under various conditions. An extended simulation study was carried out to evaluate the estimation accuracy of a functional mixed-effects model. Specifically, the accuracy of the estimated trajectories was examined under various conditions including different types of missing data and varying levels of sparseness.
ContributorsWard, Kimberly l (Author) / Suk, Hye Won (Thesis advisor) / Aiken, Leona (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2016
Description
This dissertation examined how anxiety levels and social competence change across the course of early elementary school, as well as how individual differences at the transition to kindergarten may influence these trajectories. Previous research has supported unidirectional relations among anxiety and social competence, but few studies explore how inter- and intra-individual changes in social competence and anxiety may be related across time. From a developmental perspective, studying these trajectories following the transition to kindergarten is important, as cognitive and emotion regulation capacities increase markedly across kindergarten, and the relative success with which children navigate this transition can have a bearing on future social and emotional functioning across elementary school. In addition, given gender differences in anxiety manifestation and social competence development broadly, gender differences were also examined in an exploratory manner. Data from parent and teacher reports of a community sample of 291 children across kindergarten, 1st, and 2nd grades were analyzed. Results from bivariate growth models revealed steeper increases in anxiety, relative to peers in the sample, were associated with steeper decreases in social competence across time. This finding held after controlling for externalizing behavior problems at each time point, which suggests that relations among anxiety and social competence may be independent of other behavior problems commonly associated with poor social adjustment. Temperament variables were associated with changes in social competence, such that purportedly "risky" temperament traits of higher negative emotionality and lower attention control were associated with concurrently lower social competence in kindergarten, but with relatively steeper increases in social competence across time. Temperament variables in kindergarten were unrelated with changes in anxiety across time. Gender differences in relations among anxiety in kindergarten and growth in social competence also were revealed. Findings for teacher and parent reports of child behavior varied. Results are discussed with respect to contexts that may drive differences between parent and teacher reports of child behavior, as well as key developmental considerations that may help to explain why kindergarten temperament variables examined herein appear to predict changes in social competence but not changes in anxiety levels.
ContributorsParker, Julia Humphrey (Author) / Pina, Armando A. (Thesis advisor) / Grimm, Kevin (Committee member) / Doane, Leah D. (Committee member) / Valiente, Carlos (Committee member) / Arizona State University (Publisher)
Created2016
Description
This study examined whether social support available to parents moderated the heritability of parent-reported social approach at 12 months (N = 286 twin pairs, 52.00% female) and social competence at 30 months (N = 259 twin pairs, 53.30% female). Genetic and environmental covariance across age is also reported. Social support consistently moderated genetic influences on children’s social approach and competence, such that heritability was highest when parents reported low social support. Shared environment was not moderated by social support and explained continuity across age. Findings provide further evidence that genetic and environmental influences on development vary across context. When parents are supported, environmental influences on children’s social competence are larger, perhaps because support helps parents provide a broadly promotive environment.
ContributorsClifford, Sierra (Author) / Lemery-Chalfant, Kathryn (Thesis advisor) / Doane, Leah (Committee member) / Shiota, Michelle (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2017
Description
The process of combining data is one in which information from disjoint datasets sharing at least a number of common variables is merged. This process is commonly referred to as data fusion, with the main objective of creating a new dataset permitting more flexible analyses than the separate analysis of each individual dataset. Many data fusion methods have been proposed in the literature, although most utilize the frequentist framework. This dissertation investigates a new approach called Bayesian Synthesis in which information obtained from one dataset acts as priors for the next analysis. This process continues sequentially until a single posterior distribution is created using all available data. These informative augmented data-dependent priors provide an extra source of information that may aid in the accuracy of estimation. To examine the performance of the proposed Bayesian Synthesis approach, first, results of simulated data with known population values under a variety of conditions were examined. Next, these results were compared to those from the traditional maximum likelihood approach to data fusion, as well as the data fusion approach analyzed via Bayes. The assessment of parameter recovery based on the proposed Bayesian Synthesis approach was evaluated using four criteria to reflect measures of raw bias, relative bias, accuracy, and efficiency. Subsequently, empirical analyses with real data were conducted. For this purpose, the fusion of real data from five longitudinal studies of mathematics ability varying in their assessment of ability and in the timing of measurement occasions was used. Results from the Bayesian Synthesis and data fusion approaches with combined data using Bayesian and maximum likelihood estimation methods were reported. The results illustrate that Bayesian Synthesis with data driven priors is a highly effective approach, provided that the sample sizes for the fused data are large enough to provide unbiased estimates. Bayesian Synthesis provides another beneficial approach to data fusion that can effectively be used to enhance the validity of conclusions obtained from the merging of data from different studies.
ContributorsMarcoulides, Katerina M (Author) / Grimm, Kevin (Thesis advisor) / Levy, Roy (Thesis advisor) / MacKinnon, David (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2017
Description
Statistical mediation analysis has been widely used in the social sciences in order to examine the indirect effects of an independent variable on a dependent variable. The statistical properties of the single mediator model with manifest and latent variables have been studied using simulation studies. However, the single mediator model with latent variables in the Bayesian framework with various accurate and inaccurate priors for structural and measurement model parameters has yet to be evaluated in a statistical simulation. This dissertation outlines the steps in the estimation of a single mediator model with latent variables as a Bayesian structural equation model (SEM). A Monte Carlo study is carried out in order to examine the statistical properties of point and interval summaries for the mediated effect in the Bayesian latent variable single mediator model with prior distributions with varying degrees of accuracy and informativeness. Bayesian methods with diffuse priors have equally good statistical properties as Maximum Likelihood (ML) and the distribution of the product. With accurate informative priors Bayesian methods can increase power up to 25% and decrease interval width up to 24%. With inaccurate informative priors the point summaries of the mediated effect are more biased than ML estimates, and the bias is higher if the inaccuracy occurs in priors for structural parameters than in priors for measurement model parameters. Findings from the Monte Carlo study are generalizable to Bayesian analyses with priors of the same distributional forms that have comparable amounts of (in)accuracy and informativeness to priors evaluated in the Monte Carlo study.
ContributorsMiočević, Milica (Author) / Mackinnon, David P. (Thesis advisor) / Levy, Roy (Thesis advisor) / Grimm, Kevin (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2017