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Master of Science / Department of Psychological Sciences / Patrick Knight / The current study examined attributions of blame for workplace errors through the lens of Kelley’s (1967) ANOVA model of attribution-making, which addresses the consensus, consistency, and distinctiveness of a behavior. Consensus and distinctiveness information were manipulated in the description of a workplace accident. It was expected that participants would make different attributions regarding the cause of the event due to these manipulations. This study further attempted to determine if an individual’s core self-evaluations (CSE) impact how she or he evaluates a workplace accident and attributes blame, either from the perspective of the employee who made the error or that of a co-worker. Because CSE are fundamental beliefs about an individual’s success, ability, and self-worth, they may contribute to how the individual attributes blame for a workplace accident. It was found that CSE were positively related to participants’ inclination to make internal attributions of blame for a workplace error. Contrary to expectations, manipulations of the consensus and distinctiveness of the workplace error did not moderate participants’ attributions of blame. Explanations for these findings are discussed, as are possible applications of this research.

This study uses a predominantly quantitative approach to explore the effect of different error correction feedback mechanisms on students’ English as a Second Language writing (narrative and descriptive) amongst high school students in Botswana. A longitudinal, quasi-experimental design is used, with a control group that received no correction feedback while the experimental groups received direct, coded and uncoded feedback. Three hypotheses define the study in terms of fluency, correction success and accuracy development over time. No significant increases in fluency were found between the pretests and posttests. Correction success achieved by the three treatment groups when rewriting texts reflected the explicitness of the feedback, with the direct group highest, followed by the coded and uncoded groups. Findings were mixed on the important issue of accuracy development, although they strongly suggest that for spelling, any type of feedback is significantly better than none and that coded feedback is better than direct feedback despite the latter being more explicit. Students from all the treatment groups expressed similarly positive opinions on correction feedback. / Applied Language / M.A. (Spec. in Applied Linguistics)

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In medical diagnostic studies, the area under the Receiver Operating Characteristic (ROC) curve (AUC) and Youden index are two summary measures widely used in the evaluation of the diagnostic accuracy of a medical test with continuous test results. The first half of this dissertation will highlight ROC analysis including extension of Youden index to the partial Youden index as well as novel confidence interval estimation for AUC and Youden index in the presence of covariates in induced linear regression models. Extensive simulation results show that the proposed methods perform well with small to moderate sized samples. In addition, some real examples will be presented to illustrate the methods. The latter half focuses on the application of empirical likelihood method in economics and finance. Two models draw our attention. The first one is the predictive regression model with independent and identically distributed errors. Some uniform tests have been proposed in the literature without distinguishing whether the predicting variable is stationary or nearly integrated. Here, we extend the empirical likelihood methods in Zhu, Cai and Peng (2014) with independent errors to the case of an AR error process. The proposed new tests do not need to know whether the predicting variable is stationary or nearly integrated, and whether it has a finite variance or an infinite variance. Another model we considered is a GARCH(1,1) sequence or an AR(1) model with ARCH(1) errors. It is known that the observations have a heavy tail and the tail index is determined by an estimating equation. Therefore, one can estimate the tail index by solving the estimating equation with unknown parameters replaced by Quasi Maximum Likelihood Estimation (QMLE), and profile empirical likelihood method can be employed to effectively construct a confidence interval for the tail index. However, this requires that the errors of such a model have at least finite fourth moment to ensure asymptotic normality with n1/2 rate of convergence and Wilk's Theorem. We show that the finite fourth moment can be relaxed by employing some Least Absolute Deviations Estimate (LADE) instead of QMLE for the unknown parameters by noting that the estimating equation for determining the tail index is invariant to a scale transformation of the underlying model. Furthermore, the proposed tail index estimators have a normal limit with n1/2 rate of convergence under minimal moment condition, which may have an infinite fourth moment, and Wilk's theorem holds for the proposed profile empirical likelihood methods. Hence a confidence interval for the tail index can be obtained without estimating any additional quantities such as asymptotic variance.

published_or_final_version / abstract / Linguistics / Master / Master of Arts in Applied Linguistics

published_or_final_version / abstract / Linguistics / Master / Master of Arts

published_or_final_version / abstract / Linguistics / Master / Master of Arts

published_or_final_version / Law / Doctoral / Doctor of Philosophy