Despite the strong link between pain and depressive symptoms, the mechanisms by which they are connected in the everyday lives of individuals with chronic pain are not well understood. In addition, previous investigations have tended to ignore biopsychosocial individual difference factors, assuming that all individuals respond to pain-related experiences and affect in the same manner. The present study tried to address these gaps in the existing literature. Two hundred twenty individuals with Fibromyalgia completed daily diaries during the morning, afternoon, and evening for 21 days. Findings were generally consistent with the hypotheses. Multilevel structural equation modeling revealed that morning pain and positive and negative affect are uniquely associated with morning negative pain appraisal, which in turn, is positively related to pain’s activity interference in the afternoon. Pain’s activity interference was the strongest predictor of evening depressive symptoms. Latent profile analysis using biopsychosocial measures identified three theoretically and clinically important subgroups (i.e., Low Functioning, Normative, and High Functioning groups). Although the daily pain-depressive symptoms link was not significantly moderated by these subgroups, individuals in the High Functioning group reported the lowest levels of average morning pain, negative affect, negative pain appraisal, afternoon pain’s activity interference, and evening depressive symptoms, and the highest levels of average morning positive affect across 21 days relative to the other two groups. The Normative group fared better on all measures than did the Low Functioning group. The findings of the present study suggest the importance of promoting morning positive affect and decreasing negative affect in disconnecting the within-day pain-depressive symptoms link, as well as the potential value of tailoring chronic pain interventions to those individuals who are in the greatest need.

Socioeconomic status (SES) is linked with poorer health outcomes across the range of SES. The Reserve Capacity Model (RCM) proposes that low SES fuels repeated and/or chronic exposure to elevated levels of stress, producing deleterious emotional, psychological, social, and physiological changes that result in development of disease over time. The RCM further asserts that a relative lack of social and psychological resources, including efficacy and social support, among low SES individuals accounts for their greater vulnerability to the effects of stress. Although the links between stress, reserve capacity, and health outcomes are framed in the RCM as an ongoing process that produces disease, the majority of investigations testing the model have not examined its utility in explaining 1) coping with daily stressors or 2) symptom flares among individuals managing a chronic illness. This study investigated the effects of SES, reflected in income level, on the: 1) levels of daily financial events and financial worry; 2) relations between daily financial worry and symptoms of pain and fatigue; and 3) extent to which daily coping efficacy and social support mediated the daily financial worry-symptom relation across 21 daily diary reports collected from 220 individuals with fibromyalgia (FM). Simple correlations showed that income was inversely related to frequency of financial events and level of financial worry across 21 days. Results from multilevel models indicated that daily increases in financial worry were unrelated to pain regardless of income level, but were related to increased fatigue among individuals with lower relative to higher income. Daily efficacy and support mediated the relations between financial worry and pain and fatigue, but the extent of mediation did not differ based on high versus low income level. Taken together, the findings suggest that individuals of low versus high income encounter more frequent financial stress and experience greater daily fatigue exacerbation related to that stress, in line with the RCM. Over time, the greater exposure and reactivity to financial strain may account for the inverse relation between income and disability among those with chronic pain.

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.

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.

The current study examined effects of representations of relationships with parents on young adults’ representations of romantic relationships and self-esteem, with particular attention paid to the role of fathers, instability of representations, and bidirectional effects. Data were obtained from two waves (Waves 4 and 5) of a five-wave study. At wave 4, 287 young adults (mean age = 20) participated, and at Wave 5, 276 young adults (mean age = 22) participated. One-time interviews (Behavioral Systems Questionnaires; BSQ) were conducted to measure the level of representations of relationships with parents. Nightly diary checklists (7 nights at Wave 4, and 5 nights at Wave 5) were used to measure the level and instability of representations of romantic relationships (BSQ) and self-esteem (Rosenberg Self Esteem Scale). Two styles of representations, including secure versus dismissing (e.g., relying on parents or romantic partners when distressed versus not relying on them) and preoccupied (e.g., worrying about rejection and excessive dependency) were measured for relationships with parents and romantic partners. The results showed evidence for unique roles of fathers, instability of representations, and bidirectional effects. Relationships with fathers affected young adults’ self-esteem. More nightly fluctuations in security with romantic partners predicted higher levels of security with romantic partners, but only in the context of more secure relationships. More nightly fluctuations in self-esteem predicted more dismissive representations of fathers. Bidirectional effects involved young adults’ representations of both romantic relationships and self-esteem, and their representations of relationships with parents. The relation between instability of representations of romantic relationships and later security in romantic relationships might represent learning about romantic relationships. The relation between instability of self-esteem and later dismissive styles with fathers (e.g., not relying on fathers when distressed) at this age might be an indication of learning to become autonomous from fathers. Finally, I also hypothesize that during emerging adulthood, fathers tend to encourage children to solve their stress or problems by themselves, while mothers tend to still provide help when children are distressed. These suggested hypotheses should be examined in future research.

The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.

A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.

ABSTRACT

This cross-sectional study examined whether the temperament dimensions of negative emotionality, positive emotionality, and impulsivity moderated the relation between interparental conflict and children’s internalizing and externalizing problems. The sample consisted of 355 divorced mothers and their children (9-12 years old) who participated in a randomized controlled trial of a preventive parenting intervention for divorcing families. Children provided reports of their experiences of interparental conflict and internalizing and externalizing problems; mothers provided reports of children’s temperament and internalizing and externalizing problems. The relations were examined separately for child report and mother report of outcomes using multiple regression analyses. Results found no support for the interactive effect of interparental conflict and temperament dimensions on children’s internalizing or externalizing problems. Consistent with an additive model of their effects, interparental conflict and temperament dimensions were directly and independently related to the outcomes. There was a significant, positive effect of interparental conflict and negative emotionality on children’s internalizing and externalizing problems. Positive emotionality was significantly, negatively related to internalizing and externalizing problems. Impulsivity was significantly, positively related to externalizing problems only. The patterns of results varied somewhat across mother and child report of interparental conflict on externalizing problems and positive emotionality on internalizing problems. The results of this study are consistent with the previous research on the significant main effects of interparental conflict and temperament dimensions on children’s internalizing and externalizing problems. These findings suggest that children’s environment and intrapersonal characteristics, represented by children’s experiences of interparental conflict and temperament, both uniquely contribute to children’s post-divorce internalizing and externalizing problems.

Measurement invariance exists when a scale functions equivalently across people and is therefore essential for making meaningful group comparisons. Often, measurement invariance is examined with independent and identically distributed data; however, there are times when the participants are clustered within units, creating dependency in the data. Researchers have taken different approaches to address this dependency when studying measurement invariance (e.g., Kim, Kwok, & Yoon, 2012; Ryu, 2014; Kim, Yoon, Wen, Luo, & Kwok, 2015), but there are no comparisons of the various approaches. The purpose of this master's thesis was to investigate measurement invariance in multilevel data when the grouping variable was a level-1 variable using five different approaches. Publicly available data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K) was used as an illustrative example. The construct of early behavior, which was made up of four teacher-rated behavior scales, was evaluated for measurement invariance in relation to gender. In the specific case of this illustrative example, the statistical conclusions of the five approaches were in agreement (i.e., the loading of the externalizing item and the intercept of the approaches to learning item were not invariant). Simulation work should be done to investigate in which situations the conclusions of these approaches diverge.