Matching Items (5)
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- All Subjects: Psychology--Statistical methods.
- All Subjects: Emotion socialization
- 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
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
The first half-year of infancy represents a salient time in which emotion expression assumes a more psychological character as opposed to a predominantly physiological one. Although previous research has demonstrated the relations between early parenting and later emotional competencies, there has been less of a focus on differentiating positive and negative emotion expression across the early infancy period. Thus, the current study investigates the growth of positive and negative emotion expression across early infancy in a low-income, Mexican-American sample, and examines the development of emotion expression as a function of early maternal emotion socialization and prenatal stress. Participants included 322 mothers and their infants. Data were collected in participants' homes prenatally and when the infants were 12-, 18-, and 24-weeks old. Mothers were asked to interact with their infants in a semi-structured teaching task, and video-taped interactions of mother and infant behaviors were then coded. Data for mothers was collected at the prenatal and 12-week visits and data for infants was collected at the 12-, 18-, and 24-week visits. Prenatal stress was measured via two questionnaires (Daily Hassles Questionnaire and Perceived Stress Scale). Maternal socialization at 12 weeks was represented as a composite of four observational codes from the Coding Interactive Behavior coding system. Infant emotion expression was also globally rated across the 5-minute teaching task. Findings suggest that the normative development of emotion expression across early infancy is complex. Positive emotion expression may increase across the early infancy period whereas negative emotion expression decreases. Further, at 12 weeks, greater maternal emotion socialization relates to more infant positivity and less negativity, in line with current conceptualization of parenting. However, across time, greater early socialization predicted decreased positivity and was unrelated to negative emotion expression. Findings also suggest that prenatal stress does not relate to socialization efforts or to infant emotion expression. A better understanding of the nuanced development of positive and negative emotion development as a function of early parenting may have implications for early intervention and prevention in this high-risk population.
ContributorsRoss, Emily (Author) / Crnic, Keith (Thesis advisor) / Grimm, Kevin (Committee member) / Eisenberg, Nancy (Committee member) / Arizona State University (Publisher)
Created2015
Description
The construct of adult emotional intelligence has gained increasing attention over the last 15 years given its significant socioemotional implications for the ability to label, understand, and regulate emotions. There is a gap, however, in understanding how emotional intelligence develops in children. Parenting is one of the most salient predictors of children’s behavior and the current study investigated its prospective link to children’s emotional intelligence. More preceisely, this study took a differentiated approach to parenting by examining the distinct contributions of maternal sensitivity and emotion socialization to children’s emotional intelligence. In addition, executive function, considered a “conductor” of higher-order skills and a neurocognitive correlate of emotional intelligence, was examined as a possible mechanism by which parenting influences emotional intelligence. Data were collected from 269 Mexican-American mother-child dyads during 2-year (parenting), 4.5-year (executive function), and 6-year (emotional intelligence) laboratory visits. Both parenting variables were assessed by objective observer ratings. Exeutive function and emotional intelligence were examined as latent constructs comprised of relevant parent-reported and objective measures. Due to a lack of adequate fit, the emotional intelligence variable was separated into two distinct latent constructs, emotion knowledge/understanding and emotion dysregulation. Results indicated that neither dimension of parenting was predictive of dimensions of emotional intelligence. On the other hand, children’s executive function was positively related to emotion knowledge. Finally, executive function did not emerge as a mediator of the relation between parenting and dimensions of emotional intelligence. Taken together, these findings highlight the need for a nuanced developmental and bioecological framework in the study of childen’s executive function and emotional intelligence.
ContributorsRoss, Emily (Author) / Crnic, Keith (Thesis advisor) / Luecken, Linda (Committee member) / Bradley, Robert (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2020