Matching Items (11)
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- All Subjects: Quantitative psychology
- All Subjects: Anxiety
- Creators: Grimm, Kevin
Description
Mediation analysis is integral to psychology, investigating human behavior’s causal mechanisms. The diversity of explanations for human behavior has implications for the estimation and interpretation of statistical mediation models. Individuals can have similar observed outcomes while undergoing different causal processes or different observed outcomes while receiving the same treatment. Researchers can employ diverse strategies when studying individual differences in multiple mediation pathways, including individual fit measures and analysis of residuals. This dissertation investigates the use of individual residuals and fit measures to identify individual differences in multiple mediation pathways. More specifically, this study focuses on mediation model residuals in a heterogeneous population in which some people experience indirect effects through one mediator and others experience indirect effects through a different mediator. A simulation study investigates 162 conditions defined by effect size and sample size for three proposed methods: residual differences, delta z, and generalized Cook’s distance. Results indicate that analogs of Type 1 error rates are generally acceptable for the method of residual differences, but statistical power is limited. Likewise, neither delta z nor gCd could reliably distinguish between contrasts that had true effects and those that did not. The outcomes of this study reveal the potential for statistical measures of individual mediation. However, limitations related to unequal subpopulation variances, multiple dependent variables, the inherent relationship between direct effects and unestimated indirect effects, and minimal contrast effects require more research to develop a simple method that researchers can use on single data sets.
ContributorsSmyth, Heather Lynn (Author) / MacKinnon, David (Thesis advisor) / Tein, Jenn-Yun (Committee member) / McNeish, Daniel (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Guided by the Risky Families model and Daily Process methods, the present study examined how daily stressors are related to emotional well-being at the between- and within-person levels among adolescent grandchildren raised by grandmothers. This study also examined whether risk (i.e., adverse childhood experiences/ACES) and resilience (i.e., socio-emotional skills) factors were linked to differences in daily well-being, stressor exposure, and emotional reactivity, and evaluated the efficacy of an online social intelligence training (SIT) program on daily stressor-emotion dynamics. Data came from a subsample (n = 188) of custodial adolescents who participated in an attention-controlled randomized clinical trial and completed 14-day daily surveys prior to and following intervention. Analyses were conducted with dynamic structural equation modeling. Daily stressors, on average, and experiencing above average stressors, were associated with higher negative emotions and lower positive emotions and social connection. Those with more ACEs, on average, reported higher daily stressors and worse well-being, whereas those with higher socio-emotional skills, on average, reported lower daily stressors and better well-being. At the within-person level, more ACEs were associated with higher daily negative emotions. Nonverbal processing was linked to higher daily positive emotions and social connection. Conversational skills were associated with higher daily positive emotions and social connection, and lower, more inert daily negative emotions. Neither ACEs nor socio-emotional skills were associated with within-person reactivity to stressors. Also, the SIT program did not demonstrate efficacy for any outcome. My discussion focused on how findings extend the literature on custodial adolescents by showing that daily stressors impact well-being, offer knowledge of how ACEs and socio-emotional skills shape daily stressor-emotion dynamics, and considers reasons why the online, self-guided SIT program failed to show efficacy on key outcomes.
ContributorsCastro, Saul (Author) / Infurna, Frank (Thesis advisor) / Doane, Leah (Committee member) / Davis, Mary (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Depression and anxiety are among the most prevalent psychiatric disorders for adults and adolescents and can be intergenerationally transmitted from parents to their children. Moreover, depressive and anxiety disorders often develop during adolescence. Additionally, family environment and the parent-child relationship are significant predictors of mental health among adolescents. Yet, few studies have considered how adolescent depression and anxiety problems may influence the family environment and mental health of parents. Moreover, even fewer studies have examined how depressive and anxious intergenerational pathways may vary by racial/ethnic status. As such, bidirectional effects of parent and adolescent depressive and anxiety problems were investigated using data from the Adolescent Brain and Cognitive Development (ABCD) study at Time 1 (T1)(Mage = 9.92, n=11,861), Time 2 (T2), and Time 3 (T3). Each follow-up was approximately one-year apart. Multiple path analysis models were used to examined bidirectional associations between parent and adolescent A) depressive problems B) anxiety problems and C) depressive and anxiety problems from T1 to T3 and how family conflict and adolescent-reported parental acceptance at T2 mediated these associations. Measurement invariance testing and multigroup analyses were conducted across non-Hispanic White, Hispanic, and non-Hispanic Black participants to examine if depressive and anxious pathways or measurement differed by racial-ethnic status. Findings revealed that both adolescent and parent depression problems at T1 predicted increases in depression at T3. Greater adolescent or parent anxiety problems at T1 predicted increases in adolescent and parent anxiety problems at T3. Greater family conflict and lower perceived parental acceptance at T2 predicted increases in adolescent depressive problems but did not predict adolescent anxiety problems over time. Parental depressive and anxiety problems at T1 did not predict adolescent-reported parental acceptance at T2 but did predict greater family conflict. Measurement noninvariance was found for family conflict and adolescent depressive problems. Multigroup analyses revealed that the association between both depressive and anxiety problems from T1 to T3 was weaker among Black adolescents compared to White and Hispanic adolescents. In summary, this research contributes valuable insights into the measurement of and relationship between parent and adolescent mental health, family dynamics, and adolescent perceived parental acceptance.
ContributorsJamil, Belal (Author) / Su, Jinni (Thesis advisor) / Doane, Leah (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2024
Description
Anxiety disorder diagnosis is a risk factor for alcohol use disorders (AUDs), but mechanisms of risk are not well understood. Studies show that anxious individuals receive greater negative reinforcement from alcohol when consumed prior to a stressor, but few studies have examined whether anxious individuals receive greater negative (or positive) reinforcement from alcohol in a general drinking context (i.e., no imminent stressor). Previous studies have also failed to examine possible moderating effects of specific drinking contexts (e.g., drinking in a group or alone). Finally, no studies have investigated mediating variables that might explain the relationship between anxiety and reinforcement from alcohol, such as physiological response to alcohol (e.g., cortisol response). Data for this study were drawn from a large alcohol administration study (N = 447) wherein participants were randomized to receive alcohol (target peak BAC: .08 g%) or placebo in one of four contexts: group simulated bar, solitary simulated bar, group sterile laboratory, solitary sterile laboratory. It was hypothesized that anxiety would be associated with positive subjective response (SR) under alcohol (above and beyond placebo), indicating stronger reinforcement from alcohol. It was also hypothesized that social and physical drinking context would moderate this relationship. Finally, it was hypothesized that anxiety would be associated with a blunted cortisol response to alcohol (compared to placebo) and this blunted cortisol response would be associated with stronger positive SR and weaker negative SR. Results showed that anxiety was not associated with positive SR in the full sample, but drinking context did moderate the anxiety/SR relationship in most cases (e.g., anxiety was significantly associated with positive SR (stimulation) under placebo in solitary contexts only). There was no evidence that cortisol response to alcohol mediated the relationship between anxiety and SR. This study provides evidence that anxious drinkers expect stronger positive reinforcement from alcohol in solitary contexts, which has implications for intervention (e.g., modification of existing interventions like expectancy challenge). Null findings regarding cortisol response suggest alcohol’s effect on cortisol response to stress (rather than cortisol response to alcohol consumption) may be more relevant for SR and drinking behavior among anxious individuals.
ContributorsMenary, Kyle Robert (Author) / Corbin, William (Thesis advisor) / Chassin, Laurie (Committee member) / Meier, Madeline (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
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
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
Anxiety is one of the most common psychiatric disorders among children yet characterized by lower use of mental health services. Preventive efforts have demonstrated promise in the ability to reduce anxiety symptoms. However, as evidence-based interventions move into real-world settings, there is a need to systematically examine potential implementation factors that may affect program outcomes. The current study investigates the relations between different aspects of implementation and their effect on outcomes of a school-based preventive intervention targeting anxiety symptoms. Specifically, the study examines: (1) the measurement of quality of delivery, (2) specific relations among implementation components, (3) relations between these facets and anxiety program outcomes. Implementation data were collected from nine school-based mental health staff and observer ratings. Program outcomes (pretest and immediate posttest) were measured from 59 participants and their parents (mostly mothers) in the intervention condition. Implementation components included adherence, quality of delivery, time spent, participant responsiveness, and perceived usefulness of program materials. Program outcomes included child-reported emotional expressivity, physiological hyperarousal, negative cognitions, social skills, self-efficacy, and child and parent reported levels of child anxiety. Study findings indicated that quality of delivery was best captured as two facets: skillful presentation and positive engagement. Adherence and quality of delivery were associated with greater participant responsiveness, although time spent was not. Significant relations were found between some implementation components and some program outcomes. Further efforts can be used to optimize the translation of evidence-based programs into real-world settings.
ContributorsChiapa, Amanda (Author) / Pina, Armando (Thesis advisor) / Dishion, Thomas (Committee member) / Wolchik, Sharlene (Committee member) / Grimm, Kevin (Committee member) / Berkel, Cady (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