People prefer symmetric over asymmetric patterns (Reber, Schwarz, & Winkielman, 2004). According to the fluency attribution perspective, this preference reflects differences in processing speed where increased processing efficiency leads to increased pattern preference. To test the account, in Experiment 1, participants’ speed of response to a pattern predicted the relationship between pattern symmetry and pattern preference. Experiment 2 expanded this account and found that a second measure of processing efficiency, recognition accuracy for patterns, predicted the relationship between pattern symmetry and pattern preference. Experiment 3 tested the attribution account of the fluency attribution hypothesis. Participants made a judgment of pattern mood rather than pattern preference. Despite a change of judgment task to an unintuitive judgment of pattern mood, participants attributed increased processing efficiency for patterns to increased pattern happiness. The three experiments provide an integration of the information processing and fluency attribution perspectives to account for symmetry preference judgments.

People prefer symmetric over asymmetric patterns (Reber, Schwarz, & Winkielman, 2004). According to the fluency attribution perspective, this preference reflects differences in processing speed where increased processing efficiency leads to increased pattern preference. To test the account, in Experiment 1, participants’ speed of response to a pattern predicted the relationship between pattern symmetry and pattern preference. Experiment 2 expanded this account and found that a second measure of processing efficiency, recognition accuracy for patterns, predicted the relationship between pattern symmetry and pattern preference. Experiment 3 tested the attribution account of the fluency attribution hypothesis. Participants made a judgment of pattern mood rather than pattern preference. Despite a change of judgment task to an unintuitive judgment of pattern mood, participants attributed increased processing efficiency for patterns to increased pattern happiness. The three experiments provide an integration of the information processing and fluency attribution perspectives to account for symmetry preference judgments.

Partially clustered design is common in medicine, social sciences, intervention and psychological research. With some participants clustered and others not, the structure of partially clustering data is not parallel. Despite its common occurrence in practice, limited attention has been given regarding the evaluation of intervention effects in partially clustered data. Mediation analysis is used to identify the mechanism underlying the relationship between an independent variable and a dependent variable via a mediator variable. While most of the literature is focused on conventional frequentist mediation models, no research has studied a Bayesian mediation model in the context of a partially clustered design yet. Therefore, the primary objectives of this paper are to address conceptual considerations in estimating the mediation effects in the partially clustered randomized designs, and to examine the performances of the proposed model using both simulated data and real data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K). A small-scale simulation study was also conducted and the results indicate that under large sample sizes, negligible relative parameter bias was found in the Bayesian estimates of the indirect effects and of covariance between the components of the indirect effect. Coverage rates for the 95% credible interval for these two estimates were found to be close to the nominal level. These results supported use of the proposed Bayesian model for partially clustered mediation in conditions when the sample size is moderately large. / text

abstract: 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. / Dissertation/Thesis / Doctoral Dissertation Psychology 2017

Thesis advisor: Thomas O'Hare / With implications for chronic disease and mortality, alcohol-related problems represent a threat to population health. Among U.S. Latinos, the process of acculturation has traditionally been identified as a predictor of drinking outcomes. However, past research on the relationship between acculturation and drinking has varied widely, leaving uncertainties regarding the circumstances under which the relationship operates or the reasons why the relationship is observed. The present study therefore explored the intricacies of the relationship between acculturation-related measures and drinking outcomes among U.S. Latinos, highlighting within-group variation based on sex and heritage country/region and the importance of examining mediators. Using a population-based probability sample of U.S. adults (NESARC-III, 2012-2013), the present study examined data from 7,037 self-identified Latinos. Using multivariable regression analyses, the study tested relationships between various conventionally-used acculturation measures (including proxy measures and an acculturation scale) and a range of drinking outcomes: drinking status, average daily ethanol intake, and DSM-5 alcohol use disorder. Moderation analyses examined the role of sex and heritage country/region. Finally, the study employed mediation analysis to test the hypothesized role of self-reported perceived ethnic discrimination as a mediator in the relationship between acculturation-related measures and drinking outcomes. Results indicated a significant and positive, albeit modest, relationship between acculturation-related measures and a range of drinking outcomes. Many of these relationships varied by sex or heritage country/region, depending on the specific acculturation-related measure and drinking outcome examined. Notably, the link between acculturation-related measures and DSM-5 alcohol use disorder was consistent for men and women. For Latino men, results of mediation analyses indicated that self-reported perceived ethnic discrimination acted as a partial mediator in the relationship between two acculturation-related measures and past-year DSM-5 alcohol use disorder. This finding lends credence to the notion that ethnic discrimination and experiences of “othering”—which can accompany the process of acculturation—may help explain problem drinking in U.S. Latino adult men. Further research is needed to uncover the variety of experiences or structures of discrimination involved in problem drinking among U.S. Latinos. Mediators in the relationship between acculturation and problem drinking may provide opportunities for intervention to weaken this detrimental relationship. / Thesis (PhD) — Boston College, 2019. / Submitted to: Boston College. Graduate School of Social Work. / Discipline: Social Work.

Interest in mediation analysis has increased over time, with particular excitement in the social and behavioral sciences. A mediator is defined as an intermediate in the causal sequence between an independent and dependent variable. Previous research has demonstrated that the cross-sectional form of mediation analysis is inherently flawed, evidenced by the inability of the cross-sectional mediation model to account for temporal precedence and estimation of the indirect effect being biased in nearly all situations. For these reasons, a longitudinal model is recommended. However, a method for determining the exact time points to measure the variables used in mediation analysis has not been adequately examined. In this study, we examined methods for determining an appropriate time lag when designing a mediation study. The methods implemented include correlation analysis, the quadratic and exponential forms of the lag as a moderator approach, and knot estimation using basis splines. The data for the study was simulated for three distinct trends generated using a linear piecewise model, a sigmoid model, and a sigmoid piecewise model. Additionally, two sampling approaches, an intense sampling approach and a three-measure approach, were examined as well as six sample sizes and three effect sizes for the total effect on the outcome. The estimation methods were additionally compared by considering different types of error structures used in data generation as well as by examining equal and unequal time lag lengths between the predictor and mediator, and the mediator and outcome. The intent of the study is to provide methods so that researchers can estimate the best time to evaluate mediator and outcome measurements that will be used in mediation analysis. The results from this study showed that the best estimation method varied depending on the lag being estimated, the sampling approach, and the length of the lag. However, the knot estimation approach worked reasonably well in most scenarios considered even with small sample sizes of 5 or 10 per group. The findings from this study have the potential to improve study design for research implementing longitudinal mediation analysis by reducing bias in the estimate of the indirect effect when adequate time points are used.

Background: Smartphone addiction can have negative consequences such as anxiety, depression, insomnia, and a loss of social connectivity. Understanding smartphone addiction is still in its early stages, but self-regulation and attention deficit hyperactivity disorder (ADHD) symptoms are two established risk factors. Exploring these risk factors and their impact on individuals’ well-being may help prevent smartphone addiction. Objective: This study aims to (1) explore the relationship between smartphone addiction and psychological and social well-being (e.g., friendship validation and caring, and friendship and intimate exchange) among adolescents. (2) Examine whether self-regulation mediates the relationship between smartphone addiction and psychological well-being and social well-being. (3) Examine whether attention mediates the relationship between smartphone addiction and psychological well-being and social well-being. Methods: This was a cross-sectional study conducted in middle school in Victoria, British Columbia, Canada. Students (Grade 6-8) completed an online survey that measured smartphone addiction, attention, self-regulation, and psychological and social well-being. A bivariate correlational analysis was used to examine the relationship between smartphone addiction, self-regulation, attention psychological well-being, and social well-being. Multiple mediation analyses were used to perform the mediation between smartphone addiction, attention, self-regulation, and psychological and social well-being. Results: The bivariate correlation showed significant negative associations between smartphone addiction and attention, self-regulation, psychological well-being, and friendship validation and caring. Smartphone addiction did not have a significant relationship with friendship intimate exchange. The mediation analysis showed that attention was a significant mediator between smartphone addiction and psychological well-being (indirect effect= -.102; 95% CI -.142, -.066) and between smartphone addiction and friendship validation and caring (indirect effect= -.056; 95% CI -.093, -.024; direct effect= -.071; 95% CI -.155, .013). Attention did not significantly mediate the relationship between smartphone addiction and the friendship intimate exchange aspect of social well-being (indirect effect= -.005; 95% CI -.026, .016). Self-regulation showed a significant partial mediation between smartphone addiction and psychological well-being (indirect effect= -.016; 95% CI -.034, -.002). Self-regulation did not significantly mediate the relationship between smartphone addiction and friendship validation and caring (indirect effect=-.014; 95% CI -.034, .001) and friendship intimate exchange (indirect effect=-.001; 95% CI -.007, .007). Conclusion: The results indicated that the negative relationship between smartphone addiction and psychological well-being can be partially explained by adolescents’ attention and self-regulation abilities. The negative relationship between smartphone addiction and social well-being (validation and caring) can be partially explained by adolescents’ attention. However, both aspects of social well-being (validation and caring and intimate exchange) were not impacted by self-regulation. This study identified potential mediators that may be used for future interventions to prevent smartphone addiction and promote wellbeing. / Graduate

Mediation analysis is built to answer not only if one variable affects another, but how the effect takes place. However, it lacks interpretable effect size estimates in situations where the mediator (an intermediate variable) and/or the outcome is categorical or otherwise non-normally distributed. By integrating a powerful approach known as average marginal effects within mediation analysis—termed Marginal Mediation Analysis (MMA)—the issues regarding categorical mediators and/or outcomes are, in large part, resolved. This new approach allows the estimation of the indirect effects (those effects of the predictor that affect the outcome through the mediator) that are interpreted in the same way as mediation analysis with continuous, normally-distributed mediators and outcomes. This also, in turn, resolves the troubling situation wherein the indirect plus the direct effect does not equal the total effect (i.e., the total effect does not equal the total effect). By offering this information in mediation, interventionists and lawmakers can better understand where efforts and resources can make the greatest impact. This project presents the development and the software of MMA, describes the evaluation of its performance, and reports an application of MMA to health data. The approach is successful in several aspects: 1) the software works across a wide variety of situations as the MarginalMediation R package; 2) MMA performed well and was statistically powered much like other mediation analysis approaches; and 3) the application demonstrated the increased amount of interpretable information that is provided in contrast to other approaches.

Congenital heart disease (CHD) is the most common congenital anomaly, with single ventricle (SV) defects accounting for nearly 10% of all CHD. SV defects tend to be the most severe forms of CHD: all patients born with SV require multiple open heart surgeries, often beginning in the neonatal period, ultimately leading to the Fontan procedure. Due to improvements in surgical procedures and medical care, more patients are surviving into adolescence and adulthood. Brain imaging and pathology studies have shown that patients with SV have differences in brain structure and metabolism even before the first surgery, and as early as in utero. Furthermore, a significant number of patients have new or more severe lesions after the initial surgery, and many still have brain abnormalities into early childhood. However, there are no detailed brain structural data of SV patients in adolescence. Our group recruited a large cohort of post-Fontan SV patients aged 10-19 years. Separate analyses of neuropsychological and behavioral outcomes in these patients show deficits in multiple areas of cognition, increased rates of attention deficit-hyperactivity disorder (ADHD), and increased use of remedial and/or special education services compared to a control group. Post-Fontan adolescents have more gross brain abnormalities, including evidence of chronic ischemic stroke. Furthermore, there are widespread reductions in cortical and subcortical gray matter volume and cortical thickness, some of which are associated with medical and surgical variables. Diffusion tensor imaging (DTI) analyses show widespread areas of altered white matter microstructure in deep subcortical and cerebellar white matter. In this dissertation, I use graph theory methods to characterize structural connectivity based on gray matter (cortical thickness covariance) and white matter (DTI tractography), and examine associations between brain structure and neurodevelopment. I found that brain network connectivity differs in post-Fontan patients compared with controls, both at the global and regional level. Additionally, deficits in overall network structure were associated with impaired neurodevelopment in several domains, including general intelligence, executive function, and visuospatial skills. These data suggest that early neuroprotection should be a major focus in the care of SV patients, with the goal of improving long-term neurodevelopmental outcomes.

abstract: Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model. / Dissertation/Thesis / Doctoral Dissertation Psychology 2017