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- Creators: Cham, Hei Ning
- Creators: Doane, Leah
Description
Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
Description
The transition from high school to college is associated with considerable life strain for adolescents, including higher reported levels of daily stress and negative affect (NA), and alterations in stress physiology have been linked to poor health. The purpose of this thesis was to use an ecological momentary assessment design to study associations between momentary experiences of negative affect and cortisol levels in a sample of adolescents transitioning to college. I also examined the potential moderating effects of two potential vulnerability or protective factors, alone status and perceived social support from friends. Adolescents provided salivary samples and completed paper-and-pencil diary reports of socioemotional experiences and alone status five times per day for three consecutive weekdays, as well as completed self-report questionnaires on perceived social support from friends. Within-person increases in momentary negative affect were associated with momentary cortisol reactivity. Alone status significantly moderated this association such that the association between momentary negative affect and momentary cortisol levels was only significant when individuals were with others and not when they were alone. Perceived social support from friends did not significantly moderate the within-person associations between negative affect and momentary cortisol levels. The findings add to our understanding of physiological correlates of socioemotional experiences, as well as contexts in which these associations may be exaggerated or attenuated. The findings inform our understanding of potential pathways by which physiological reactivity to socioemotional experiences may affect the health of adolescents as well as how prevention efforts could reduce potential poor health outcomes associated with heightened stress reactivity.
ContributorsKomarnisky, Sydney-Paige Milan (Author) / Doane, Leah (Thesis director) / Knight, George (Committee member) / Luecken, Linda (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Department of Psychology (Contributor)
Created2014-12
Description
For this thesis a Monte Carlo simulation was conducted to investigate the robustness of three latent interaction modeling approaches (constrained product indicator, generalized appended product indicator (GAPI), and latent moderated structural equations (LMS)) under high degrees of nonnormality of the exogenous indicators, which have not been investigated in previous literature. Results showed that the constrained product indicator and LMS approaches yielded biased estimates of the interaction effect when the exogenous indicators were highly nonnormal. When the violation of nonnormality was not severe (symmetric with excess kurtosis < 1), the LMS approach with ML estimation yielded the most precise latent interaction effect estimates. The LMS approach with ML estimation also had the highest statistical power among the three approaches, given that the actual Type-I error rates of the Wald and likelihood ratio test of interaction effect were acceptable. In highly nonnormal conditions, only the GAPI approach with ML estimation yielded unbiased latent interaction effect estimates, with an acceptable actual Type-I error rate of both the Wald test and likelihood ratio test of interaction effect. No support for the use of the Satorra-Bentler or Yuan-Bentler ML corrections was found across all three methods.
ContributorsCham, Hei Ning (Author) / West, Stephen G. (Thesis advisor) / Aiken, Leona S. (Committee member) / Enders, Craig K. (Committee member) / Arizona State University (Publisher)
Created2010