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- Creators: Atkinson, Robert
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
ABSTRACT The phenomenon of cyberbullying has captured the attention of educators and researchers alike as it has been associated with multiple aversive outcomes including suicide. Young people today have easy access to computer mediated communication (CMC) and frequently use it to harass one another -- a practice that many researchers have equated to cyberbullying. However, there is great disagreement among researchers whether intentional harmful actions carried out by way of CMC constitute cyberbullying, and some authors have argued that "cyber-aggression" is a more accurate term to describe this phenomenon. Disagreement in terms of cyberbullying's definition and methodological inconsistencies including choice of questionnaire items has resulted in highly variable results across cyberbullying studies. Researchers are in agreement however, that cyber and traditional forms of aggression are closely related phenomena, and have suggested that they may be extensions of one another. This research developed a comprehensive set of items to span cyber-aggression's content domain in order to 1) fully address all types of cyber-aggression, and 2) assess the interrelated nature of cyber and traditional aggression. These items were administered to 553 middle school students located in a central Illinois school district. Results from confirmatory factor analyses suggested that cyber-aggression is best conceptualized as integrated with traditional aggression, and that cyber and traditional aggression share two dimensions: direct-verbal and relational aggression. Additionally, results indicated that all forms of aggression are a function of general aggressive tendencies. This research identified two synthesized models combining cyber and traditional aggression into a shared framework that demonstrated excellent fit to the item data.
ContributorsLerner, David (Author) / Green, Samuel B (Thesis advisor) / Caterino, Linda (Committee member) / Atkinson, Robert (Committee member) / Nakagawa, Kathryn (Committee member) / Arizona State University (Publisher)
Created2013