An empirical examination of the integration of causal attribution and social learning theories in achievement situations

Using path analysis, the present investigation sought to clarify possible operational linkages among constructs from social learning and attribution theories within the context of a self-esteem system. Subjects were 300 undergraduate university students who completed a measure of self-esteem and indicated expectancies for success and minimal goal levels for an experimental task. After completing the task and receiving feedback about their performance, subjects completed causal attribution and self-esteem questionnaires. Results revealed gender differences in the degree and strength of the proposed relations, but not in the mean levels of the variables studied. Results suggested that the integration of social learning and attribution theories within a single conceptual model provides a better understanding of students' behaviors and self-esteem in achievement situations.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.