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- Member of: ASU Electronic Theses and Dissertations
Description
Type 1 Diabetes Mellitus (T1DM) is a chronic disease that requires maintaining tight metabolic control through complex behavioral and pharmaceutical regimens. Subtle cognitive impairments and stress response dysregulation may partially account for problems negotiating life changes and maintaining treatment adherence among emerging adults. The current study examined whether young adults with T1DM physiologically respond to psychological stress in a dysregulated manner compared to non-diabetic peers, and if such individuals also demonstrated greater cognitive declines following psychological stress. Participants included 23 young adults with T1DM and 52 non-diabetic controls yoked to T1DM participants based on age, gender, ethnicity, participant education, and maternal education. Participants completed a laboratory-based social stressor, pre- and post-stressor neurocognitive testing, provided fingerstick blood spots (for glucose levels) and salivary samples (for cortisol levels) at five points across the protocol, and completed psychosocial questionnaires. Related measures ANOVAs were conducted to assess differences between T1DM participants and the average of yoked controls on cortisol and cognitive outcomes. Results demonstrated that differences in cortisol reactivity were dependent on T1DM participants' use of insulin pump therapy (IPT). T1DM participants not using IPT demonstrated elevated cortisol reactivity compared to matched controls. There was no difference in cortisol reactivity between the T1DM participants on IPT and matched controls. On the Stroop task, performance patterns did not differ between participants with T1DM not on IPT and matched controls. The performance of participants with T1DM on IPT slightly improved following the stressor and matched controls slightly worsened. On the Trail Making Test, the performance of participants with T1DM was not different following the stressor whereas participants without T1DM demonstrated a decline following the stressor. Participants with and without T1DM did not differ in patterns of performance on the Rey Verbal Learning Task, Sustained Attention Allocation Task, Controlled Oral Word Association Task, or overall cortisol output across participation. The results of this study are suggestive of an exaggerated cortisol response to psychological stress in T1DM and indicate potential direct and indirect protective influences of IPT.
ContributorsMarreiro, Catherine (Author) / Luecken, Linda (Thesis advisor) / Doane, Leah (Thesis advisor) / Barrera, Manuel (Committee member) / Aiken, Leona (Committee member) / Arizona State University (Publisher)
Created2013
Description
Methods to test hypotheses of mediated effects in the pretest-posttest control group design are understudied in the behavioral sciences (MacKinnon, 2008). Because many studies aim to answer questions about mediating processes in the pretest-posttest control group design, there is a need to determine which model is most appropriate to test hypotheses about mediating processes and what happens to estimates of the mediated effect when model assumptions are violated in this design. The goal of this project was to outline estimator characteristics of four longitudinal mediation models and the cross-sectional mediation model. Models were compared on type 1 error rates, statistical power, accuracy of confidence interval coverage, and bias of parameter estimates. Four traditional longitudinal models and the cross-sectional model were assessed. The four longitudinal models were analysis of covariance (ANCOVA) using pretest scores as a covariate, path analysis, difference scores, and residualized change scores. A Monte Carlo simulation study was conducted to evaluate the different models across a wide range of sample sizes and effect sizes. All models performed well in terms of type 1 error rates and the ANCOVA and path analysis models performed best in terms of bias and empirical power. The difference score, residualized change score, and cross-sectional models all performed well given certain conditions held about the pretest measures. These conditions and future directions are discussed.
ContributorsValente, Matthew John (Author) / MacKinnon, David (Thesis advisor) / West, Stephen (Committee member) / Aiken, Leona (Committee member) / Enders, Craig (Committee member) / Arizona State University (Publisher)
Created2015
Description
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.
ContributorsMistler, Stephen (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth guidelines regarding the minimum sample size and ideal number and quality of indicators. In particular, we studied to which extent including a larger number of high quality indicators can compensate for a small sample size in LCA. The results suggest that in general, larger sample size, more indicators, higher quality of indicators, and a larger covariate effect correspond to more converged and proper replications, as well as fewer boundary estimates and less parameter bias. Based on the results, it is not recommended to use LCA with sample sizes lower than N = 100, and to use many high quality indicators and at least one strong covariate when using sample sizes less than N = 500.
ContributorsWurpts, Ingrid Carlson (Author) / Geiser, Christian (Thesis advisor) / Aiken, Leona (Thesis advisor) / West, Stephen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Family plays an important yet understudied role in the development of psychopathology during childhood, particularly for children at developmental risk. Indeed, much of the research on families has actually concentrated more on risk processes in individual family members or within-family subsystems. In general, important and complex associations have been found among family-related constructs such as marital conflict, parent-child relationships, parental depression, and parenting stress, which have in turn been found to contribute to the emergence of children's behavioral problems. Research has begun to emerge that certain family system constructs, such as cohesion, organization, and control may influence children's development, but this research has been limited by a focus on parent-reports of family functioning, rather than utilizing observational methods. With notable exceptions, there is almost no observational research examining families of children at developmental risk. This study examined the longitudinal relations among family risk and family system constructs, as well as how family systems constructs mediated the relations between family risk and child outcome. Further, the study examined how developmental risk moderated these relations. The sample followed 242 families of children with and without developmental risk across the transition-to-school period. Family risk factors were assessed at 5 years, using parental reports of symptomatology, parenting stress, and marital adjustment, and observational assessments of the parent-child relationship. Family system constructs (cohesion, warmth, conflict, organization, control) were measured at age 6 using structured observations of the entire family playing a board game. Child behavior problems and social competence were assessed at age 7. Results indicated that families of children with developmental delays did not differ from families of typically developing children on the majority of family system attributes. Cohesion and organization mediated the relations between specific family risk factors and social competence for all families. For families of typically developing children only, higher levels of control were associated with more behavior problems and less social competence. These findings underscore the importance of family-level assessment in understanding the development of psychopathology. Important family effects on children's social competence were found, although the pathways among family risk and family systems attributes are complex.
ContributorsGerstein, Emily Davis (Author) / Crnic, Keith A (Thesis advisor) / Aiken, Leona (Committee member) / Bradley, Robert (Committee member) / Gonzales, Nancy (Committee member) / Arizona State University (Publisher)
Created2012
Description
There are significant and wide-ranging health benefits of physical activity, yet the majority of adolescents in the United States do not engage in the recommended amount. This poses a significant public health challenge. Parents have a substantial influence on adolescents' levels of activity, indicating that parenting may be an especially salient target of interventions designed to promote physical activity. The current study tested the hypothesis that a family intervention to promote effective parenting would have a positive collateral effect on adolescent physical activity. This study also tested whether the increase in activity was mediated by changes in parental monitoring and family relationship quality. Furthermore, the current study assessed whether adolescent gender moderated the relationship between parental monitoring and physical activity, such that increased parental monitoring predicted increases in physical activity for girls, but not for boys. Participants were 232 adolescents at risk for behavior problems drawn from a larger randomized controlled trial of the Family Check-Up. Adolescents completed questionnaires and participated in a family assessment with their caregivers in the 6th through 9th grades. Youth randomized to the intervention reported significantly more physical activity at follow-up relative to controls. Results failed to confirm the role of family factors as mediators of the effect of the intervention on physical activity. When gender was considered as a moderator, it appeared that parental monitoring was strongly and positively correlated with physical activity for girls, but not for boys. While the mechanism by which the Family Check-Up leads to increased physical activity remains unclear, its robust effects suggest that family intervention can be used to promote physical activity and might therefore have further-reaching health benefits.
ContributorsRudo-Stern, Jenna (Author) / Dishion, Thomas J (Thesis advisor) / Wolchik, Sharlene (Committee member) / Aiken, Leona (Committee member) / Arizona State University (Publisher)
Created2015
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
Given the major investment young people make in earning and maintaining a peer reputation, our goal in this study was to explore the association between dimensions of negative and positive peer reputation in middle school and adjustment several years later, by the end of high school, among upper middle class youth. Prior research has shown negative reputations such as aggressive-disruptive and sensitive-isolated to be associated with maladjustment later in life, whereas reputations like popular and prosocial-leader have been related to positive future outcomes. However, there are contrary findings that reveal a more complex relationship between peer reputation and adjustment, showing certain “negative” reputations to be tied with better outcomes in some domains and the converse in others. Using a sample of middle school students, a confirmatory factor analysis (CFA) was performed to test a four-factor model of the Revised Class Play, a peer report measure on peer reputations. CFA findings supported the four-factor model with the following reputations: popular, prosocial, aggressive, and isolated. Structural equation models were used to predict 12th grade adjustment outcomes (academic achievement, psychopathology, substance use) from middle school peer reputation. Prosocial reputation in middle school was connected to higher academic achievement and fewer externalizing symptoms in 12th grade. Both prosocial and isolated peer reputation were negatively associated with alcohol, cigarette, and marijuana use, whereas a popular reputation was related to higher levels of alcohol use. Middle school reputation did not predict internalizing symptoms in 12th grade. Findings are discussed in terms of adaptive and maladaptive adjustment outcomes associated with each peer reputation and implications for future research.
ContributorsCurlee, Alexandria (Author) / Luthar, Suniya (Thesis advisor) / Aiken, Leona (Committee member) / Infurna, Frank (Committee member) / Arizona State University (Publisher)
Created2016
Description
Purpose: Over-identification of Navajo Head Start children into special education on the Navajo Reservation has come to the attention of Tribal leaders, Educational leaders, and parents due to the use of invalid assessment measures. Dynamic assessment (DA) of narratives may be a tool for distinguishing language differences from language disorders. The purpose of this study is to determine whether the Predictive Early Assessment of Reading and Language (PEARL), a dynamic assessment of narratives, accurately classifies Navajo Head Start students with typically developing (TD) language or with language impairment (LI), and to examine which measures best predict children’s overall performances on the PEARL.
Method: Ninety, 4- and 5-year-old Navajo preschoolers with LI and with TD language were selected. Children completed the PEARL, which measured both language comprehension and production using pretest and posttest scores, and a modifiability scale. In addition, children completed the Clinical Evaluation of Language Fundamental, Preschool, Second Edition (CELF – Preschool 2) and language samples. A Navajo Speech Language Pathologist confirmed the diagnosis of the participants. Research assistants pretested, briefly taught the principles of narrative structure (story grammar, language complexity and episode) and evaluated response to learning using an index of modifiability.
Results: Results of discriminant analysis indicated that PEARL pretest differentiated both ability groups with 89% accuracy. In addition, posttest scores discriminated with 89% accuracy and modifiability scores with 100% accuracy. Further, the subtest story grammar was the best predictor at pretest and posttest, although modifiability scores were better predictors of both ability groups.
Conclusion: Findings indicate that the PEARL is a promising assessment for accurately differentiating Navajo preschool children with LI from Navajo preschool children with TD language. The PEARL’s recommended pretest cut score over-identified Navajo children with TD language; therefore, a new recommended cut score was determined.
Method: Ninety, 4- and 5-year-old Navajo preschoolers with LI and with TD language were selected. Children completed the PEARL, which measured both language comprehension and production using pretest and posttest scores, and a modifiability scale. In addition, children completed the Clinical Evaluation of Language Fundamental, Preschool, Second Edition (CELF – Preschool 2) and language samples. A Navajo Speech Language Pathologist confirmed the diagnosis of the participants. Research assistants pretested, briefly taught the principles of narrative structure (story grammar, language complexity and episode) and evaluated response to learning using an index of modifiability.
Results: Results of discriminant analysis indicated that PEARL pretest differentiated both ability groups with 89% accuracy. In addition, posttest scores discriminated with 89% accuracy and modifiability scores with 100% accuracy. Further, the subtest story grammar was the best predictor at pretest and posttest, although modifiability scores were better predictors of both ability groups.
Conclusion: Findings indicate that the PEARL is a promising assessment for accurately differentiating Navajo preschool children with LI from Navajo preschool children with TD language. The PEARL’s recommended pretest cut score over-identified Navajo children with TD language; therefore, a new recommended cut score was determined.
ContributorsHenderson, Davis E (Author) / Restrepo, Maria Adelaida (Thesis advisor) / Aiken, Leona (Committee member) / Petersen, Douglas (Committee member) / Romero-Little, Mary Eunice (Committee member) / Kleinsasser, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since randomization of the mediator may not be plausible for most studies (i.e., the mediator status is not randomly assigned, but self-selected by participants), both the direct and indirect effects may be biased by confounding variables. The purpose of this dissertation is (1) to investigate the extent to which traditional mediation methods are affected by confounding variables and (2) to assess the statistical performance of several modern methods to address confounding variable effects in mediation analysis. This dissertation first reviewed the theoretical foundations of causal inference in statistical mediation analysis, modern statistical analysis for causal inference, and then described different methods to estimate causal direct and indirect effects in the presence of two post-treatment confounders. A large simulation study was designed to evaluate the extent to which ordinary regression and modern causal inference methods are able to obtain correct estimates of the direct and indirect effects when confounding variables that are present in the population are not included in the analysis. Five methods were compared in terms of bias, relative bias, mean square error, statistical power, Type I error rates, and confidence interval coverage to test how robust the methods are to the violation of the no unmeasured confounders assumption and confounder effect sizes. The methods explored were linear regression with adjustment, inverse propensity weighting, inverse propensity weighting with truncated weights, sequential g-estimation, and a doubly robust sequential g-estimation. Results showed that in estimating the direct and indirect effects, in general, sequential g-estimation performed the best in terms of bias, Type I error rates, power, and coverage across different confounder effect, direct effect, and sample sizes when all confounders were included in the estimation. When one of the two confounders were omitted from the estimation process, in general, none of the methods had acceptable relative bias in the simulation study. Omitting one of the confounders from estimation corresponded to the common case in mediation studies where no measure of a confounder is available but a confounder may affect the analysis. Failing to measure potential post-treatment confounder variables in a mediation model leads to biased estimates regardless of the analysis method used and emphasizes the importance of sensitivity analysis for causal mediation analysis.
ContributorsKisbu Sakarya, Yasemin (Author) / Mackinnon, David Peter (Thesis advisor) / Aiken, Leona (Committee member) / West, Stephen (Committee member) / Millsap, Roger (Committee member) / Arizona State University (Publisher)
Created2013