Matching Items (5)
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- All Subjects: Psychology--Statistical methods.
- All Subjects: Internalizing
- Creators: Grimm, Kevin
Description
Clinically meaningful emotional and behavioral problems are thought to be present beginning in infancy, and may be reliably assessed in children as young as 12 months old. However, few studies have investigated early correlates of emotional and behavioral problems assessed in infancy. The current study investigates the direct and interactive contributions of early infant and caregiver characteristics thought to play an important role in the ontogeny of behavior problems. Specifically, the study examines: (1) the links between temperamental reactivity across the first year of life and behavior problems at 18 months, (2) whether children high in temperamental reactivity are differentially susceptible to variations in maternal sensitivity, (3) the extent to which child temperamental risk or susceptibility may further be explained by mothers’ experiences of stressful life events (SLEs) during and before pregnancy. Data were collected from 322 Mexican American families during prenatal, 6-, 12-, 18-, and 24-week home interviews, as well as during 12- and 18-month lab interviews. Mother reports of SLEs were obtained between 23-40 weeks gestation; temperamental negativity and surgency at 6 weeks and 12 months; and internalizing and externalizing behaviors at 18 months. Maternal sensitivity during structured mother-infant interaction tasks at the 6-, 12-, 18-, and 24-week visits was assessed by objective observer ratings. Study findings indicated that maternal SLEs before birth were associated with more infant negativity across the first year of life, and that negativity in turn was associated with more internalizing problems at 18 months. Ecological stressors thought to be associated with sociodemographic risk factors such as low-income and ethnic minority status may begin to exert cascades of influence on children’s developmental outcomes even before birth.
ContributorsLin, Betty (Author) / Crnic, Keith A (Thesis advisor) / Lemery-Chalfant, Kathryn S (Committee member) / Luecken, Linda J. (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2016
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
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
Description
Stress in individuals presents in various forms and may accumulate across development to predict maladaptive physical and psychological outcomes, including greater risk for the onset of internalizing symptoms. Early life stress, daily life experiences, and the stress response of the hypothalamic-pituitary-adrenal (HPA) axis have all been examined as potential predictors of the development of psychopathology, but rarely have researchers attempted to understand the covariation or interaction among these stress domains using a longitudinal design when looking at the influence of stress on internalizing psychopathology. Further, most research has examined these processes in adulthood or adolescence with much less attention given to the influence of these dynamic stress pathways in childhood. Guided by the biopsychosocial model of stress, this study explored early life stress, daily life stress, diurnal cortisol (cortisol AM slope), and internalizing symptoms in a racially/ethnically and socioeconomically diverse sample of twins participating in an ongoing longitudinal study (N=970 children; Arizona Twin Project; Lemery-Chalfant et al. 2013). An additive model of stress and a stress sensitization framework model were considered as potential pathways of stress to internalizing symptoms in middle childhood. Based on a thorough review of relevant literature, it was expected that each stress indicator would individually predict internalizing symptoms. It was also predicted that early life stress would moderate the associations between diurnal cortisol and internalizing symptoms, as well as daily life stress and internalizing symptoms. Multilevel modeling analyses showed that early life stress and cortisol AM slope, but not daily life stress, predicted internalizing symptoms. Early life stress did not moderate the associations between daily life stress and internalizing symptoms or cortisol AM slope and internalizing symptoms. Results support independent additive contributions of both physiological stress processes and early life parental stressors in the development of internalizing symptoms in middle childhood. Future investigation is needed to better understand the sensitizing effects of early parental life stress during this developmental stage.
ContributorsLecarie, Emma (Author) / Doane, Leah (Thesis advisor) / Davis, Mary (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2020