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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

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
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Description
Anderies (2015); Anderies et al. (2016), informed by Ostrom (2005), aim to employ robust

feedback control models of social-ecological systems (SESs), to inform policy and the

design of institutions guiding resilient resource use. Cote and Nightingale (2012) note that

the main assumptions of resilience research downplay culture and social power. Addressing

the epistemic ga

Anderies (2015); Anderies et al. (2016), informed by Ostrom (2005), aim to employ robust

feedback control models of social-ecological systems (SESs), to inform policy and the

design of institutions guiding resilient resource use. Cote and Nightingale (2012) note that

the main assumptions of resilience research downplay culture and social power. Addressing

the epistemic gap between positivism and interpretation (Rosenberg 2016), this dissertation

argues that power and culture indeed are of primary interest in SES research.

Human use of symbols is seen as an evolved semiotic capacity. First, representation is

argued to arise as matter achieves semiotic closure (Pattee 1969; Rocha 2001) at the onset

of natural selection. Guided by models by Kauffman (1993), the evolution of a symbolic

code in genes is examined, and thereon the origin of representations other than genetic

in evolutionary transitions (Maynard Smith and Szathmáry 1995; Beach 2003). Human

symbolic interaction is proposed as one that can support its own evolutionary dynamics.

The model offered for wider dynamics in society are “flywheels,” mutually reinforcing

networks of relations. They arise as interactions in a domain of social activity intensify, e.g.

due to interplay of infrastructures, mediating built, social, and ecological affordances (An-

deries et al. 2016). Flywheels manifest as entities facilitated by the simplified interactions

(e.g. organizations) and as cycles maintaining the infrastructures (e.g. supply chains). They

manifest internal specialization as well as distributed intention, and so can favor certain

groups’ interests, and reinforce cultural blind spots to social exclusion (Mills 2007).

The perspective is applied to research of resilience in SESs, considering flywheels a

semiotic extension of feedback control. Closer attention to representations of potentially

excluded groups is justified on epistemic in addition to ethical grounds, as patterns in cul-

tural text and social relations reflect the functioning of wider social processes. Participatory

methods are suggested to aid in building capacity for institutional learning.
ContributorsBožičević, Miran (Author) / Anderies, John M (Thesis advisor) / Bolin, Robert (Committee member) / BurnSilver, Shauna (Committee member) / Arizona State University (Publisher)
Created2017
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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.

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