An exploratory comparison of the inferential ability of EFL and ESL students : a thesis presented in partial fulfilment of the requirements for the degree of Master of Management at Massey University

The ability to access and interpret information is a very important component in generating knowledge. However, people are not always able to discover information, quickly evaluate the importance of the information and access it (Tichenor, Donohue & Olien, 1970; Chatman, 1991; Sligo & Williams, 2002). Especially in a tertiary academic setting, the ability to access information and integrate information from various sources to infer what is not overtly stated in a text is an essential skill during the reading process (Kintsch, 1994; Barnes, Dennis, & Haefele-Kalvaitis, 1996; Cain, Oakhill, Barnes, & Bryant, 2001). Because of differences among people's educational background, existing pools of knowledge and communication abilities, the ability to access information will affect their inferential ability in the reading process (Alexander, 1994; Ericsson, 1996; Mckoon & Ratcliff, 1992). Although inferential ability is to be of consequence for academic functioning, very little research has been done on the comparison of inferential ability among students with English as their first language and those with English as their second language. This study examines the relative extent of text inferential ability among students with English as a first language (EFL) and students to whom English is a second language (ESL), employing the knowledge gap hypothesis, and assesses its implications. Using a procedure to assess inferential ability, this thesis compares the differences in inferential ability demonstrated by EFL and ESL students, employing cloze tests. This study found that EFL students' performance on the inferential ability and cloze item completion task is significantly better than that of their ESL counterparts via the first two scoring methods (Methods A and B). However, the inferential ability of ESL students is almost as good as their EFL counterparts when assessed by the third scoring method (Method C). The research findings suggest that Sligo and Williams (2002) are right in terming the knowledge gap as an amalgam of knowledge, comprehension and inference (p.6). Subsidiary analyses of the source of inference failures revealed different underlying sources of difficulty for both EFL and ESL students. The results of the research provide insights into the nature of gaps in accessing information and inference making. Education in a tertiary institution may or may not reduce gaps. Though both EFL and ESL students improved from their original starting level, the gaps of inferential ability between EFL and ESL students in the two tests, especially via Methods A and B, widened. In the second test, both EFL and ESL students made progress in inferential ability. Yet there still remained a gap between the two groups of students in test two as the knowledge rich individuals improved at a similar rate as the knowledge poor. The present study supports the contention of Sligo and Williams (2002) that there is an unexamined area at the heart of the knowledge gap hypothesis literature. The findings of the present study suggest the correctness of the proposal by Sligo and Williams (2002) that what knowledge gap hypothesis researchers call knowledge gaps should in fact be better described as some amalgam of gaps in knowledge, and/or inferential ability. This is the most significant finding of the present research

Gaussian Graphical Model Estimation with False Discovery Rate Control

This paper studies the estimation of high dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship between the regularized parameter and the number of false edges in GGM estimation is unclear. Hence, it is impossible to evaluate their performance rigorously. In this paper, we propose an alternative method by a multiple testing procedure. Based on our new test statistics for conditional dependence, we propose a simultaneous testing procedure for conditional dependence in GGM. Our method can control the false discovery rate (FDR) asymptotically. The numerical performance of the proposed method shows that our method works quite well

A Direct Estimation Approach to Sparse Linear Discriminant Analysis

This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix \O and the difference \de of the mean vectors, we introduce a simple and effective classifier by estimating the product \O\de directly through constrained $\ell_1$ minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule. The LPD rule is shown to have desirable theoretical and numerical properties. It exploits the approximate sparsity of \O\de and as a consequence allows cases where it can still perform well even when \O and/or \de cannot be estimated consistently. Asymptotic properties of the LPD rule are investigated and consistency and rate of convergence results are given. The LPD classifier has superior finite sample performance and significant computational advantages over the existing methods that require separate estimation of \O and \de. The LPD rule is also applied to analyze real datasets from lung cancer and leukemia studies. The classifier performs favorably in comparison to existing methods.Comment: 39 pages.To appear in Journal of the American Statistical Associatio