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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


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 …

Contributors
Moore, Shannon Victoria, Davis, Mary C, Luecken, Linda J, et al.
Created Date
2017

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 …

Contributors
Ward, Kimberly, Suk, Hye Won, Aiken, Leona, et al.
Created Date
2016

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 …

Contributors
Suh, Go Woon, Fabricius, William, Cookston, Jeffrey, et al.
Created Date
2016

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 …

Contributors
Wurpts, Ingrid Carlson, MacKinnon, David P, West, Stephen G, et al.
Created Date
2016