Matching Items (2)
Filtering by
- All Subjects: Business Administration
- All Subjects: Propensity Score
- Genre: Doctoral Dissertation
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
Accountability has been commonly referred to in the literature as a person’s expectation about others’ evaluations. However, in this study, I develop an alternative perspective of leader accountability by defining it as an individual’s degree of ownership regarding good or poor performance and acceptance of associated rewards or disciplinary actions. Based on attribution theory, leaders can have internal and external ownership regarding good and poor performance. I propose that accountability can be categorized into two correlated but distinct aspects: self-benefitting and other-benefitting. Leader self-benefitting accountability refers to leaders’ attributions towards their own benefits (i.e., internal attribution of good performance and external attribution of poor performance). Leader other-benefitting accountability reflects leaders’ attributions towards others’ interests (i.e., internal attribution of poor performance and external attribution of good performance). Using multiple samples, I develop and validate a leader accountability scale, and then test a theoretical model with a focus on leader accountability and collective accountability (i.e., a group of individuals’ degree of ownership) by collecting data from 57 leaders and 162 followers in three Chinese companies. The findings show that leader humility is positively related to leader other-benefitting accountability. Both leader self-benefitting and other-benefitting accountability are associated with collective self-benefitting and other-benefitting accountability, respectively. Moreover, the relationship between leader self-benefitting and collective self-benefitting accountability is enhanced when the leader has high organization prototypicality. Furthermore, collective self-benefitting accountability decreases leader effectiveness and team effectiveness, while collective other-benefitting accountability increases leader effectiveness.
ContributorsWang, Danni (Author) / Waldman, David (Thesis advisor) / Zhang, Zhen (Thesis advisor) / Balthazard, Pierre (Committee member) / Arizona State University (Publisher)
Created2016