Matching Items (4)
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- All Subjects: Middle-aged persons--Psychology.
- All Subjects: Multilevel models (Statistics)
- Creators: Grimm, Kevin J.
- Creators: Mistretta, Erin
- Member of: ASU Electronic Theses and Dissertations
- Member of: Theses and Dissertations
- Resource Type: Text
Description
Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data.
ContributorsFine, Kimberly L (Author) / Grimm, Kevin J. (Thesis advisor) / Edward, Mike (Committee member) / O'Rourke, Holly (Committee member) / McNeish, Dan (Committee member) / Arizona State University (Publisher)
Created2019
Description
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.
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.
ContributorsWurpts, Ingrid Carlson (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016
Description
Interpersonal strain is linked with depressive symptoms in middle-aged adults. Self-compassion is an emerging resilience construct that may be advantageous in navigating relationship strain by helping individuals respond to emotions in a kind and nonjudgmental way. Although theory and empirical evidence suggests that self-compassion is protective against the impact of stress on mental health outcomes, many studies have not investigated how self-compassion operates in the context of relationship strain. In addition, few studies have examined psychological or physiological mechanisms by which self-compassion protects against mental health outcomes, depression in particular. Thus, this study examined 1) the extent to which trait self-compassion buffers the relation between family strain and depressive symptoms, and 2) whether these buffering effects are mediated by hope and inflammatory processes (IL-6) in a sample of 762 middle-aged, community-dwelling adults. Results from structural equation models indicated that family strain was unrelated to depressive symptoms and the relation was not moderated by self-compassion. Hope, but not IL-6, mediated the relation between family strain and depressive symptoms and the indirect effect was not conditional on levels of self-compassion. Taken together, the findings suggest that family strain may lead individuals to experience less hope and subsequent increases in depressive symptoms, and further, that a self-compassionate attitude does not affect this relation. Implications for future self-compassion interventions are discussed.
ContributorsMistretta, Erin (Author) / Davis, Mary C. (Thesis advisor) / Karoly, Paul (Committee member) / Infurna, Frank (Committee member) / Arizona State University (Publisher)
Created2019
Description
Lifespan psychological perspectives have long suggested the context in which individuals live having the potential to shape the course of development across the adult lifespan. Thus, it is imperative to examine the role of both the objective and subjective neighborhood context in mitigating the consequences of lifetime adversity on mental and physical health. To address the research questions, data was used from a sample of 362 individuals in midlife who were assessed on lifetime adversity, multiple outcomes of mental and physical health and aspects of the objective and subjective neighborhood. Results showed that reporting more lifetime adversity was associated with poorer mental and physical health. Aspects of the objective and subjective neighborhood, such as green spaces moderated these relationships. The discussion focuses on potential mechanisms underlying why objective and subjective indicators of the neighborhood are protective against lifetime adversity.
ContributorsStaben, Omar E (Author) / Infurna, Frank J. (Thesis advisor) / Luthar, Suniya S. (Committee member) / Grimm, Kevin J. (Committee member) / Arizona State University (Publisher)
Created2019