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On the Development of a Comprehensive Model of English as a Foreign Language Proficiency with Learner Background Characteristics using Structure Equation Modeling Approach

利用结构方程建模方法开发具有学习者背景特征的英语作为外语能力的综合模型

基本信息

批准号：
06808025
负责人：
KINOSHITA Toru
金额：
$ 1.15万
依托单位：
NAGOYA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

The last year of the project focused mainly on the reanalyzes of the whole data using the technique of multiple regression imputation of the missing values. Consequently, about one fifth of the cases that were formerly treated as missing was able to be saved from the archive and entered into the data set. However, the results obtained from this procedure did not show any noticeable difference compared with the previous results in terms of its best model selection, and the estimated parameters for the best model. Thus, the data set without such imputation was used for the final analyzes including exploring the prossibility of the improvement of the selected best model in order to avoid potential problems causedby data imputation, such as linear dependency, estimates being fixed to lower and/or upper boundaries.One important finding of the current study was that the Equal Influence Model, which assumed mutual correlational links among all the learner background factors including Monitor turned out to be significantly better than other models, which presumed Monitor was not an independent but a dependent factor generated by formal, explicit rule oriented instructions. This result supported the models assuming the interface between Krashen's Learning and Acquisition. In particular, Monitor and other factors, especially, Informal Exposure to English had significant correlations. This result suggests the possibility of a new and theoretically interesting model, which might be called "Reversed Interface Model". In this model, Monitor could affect the Informal Exposure to English as well as the other way round

该项目的最后一年主要侧重于使用缺失值的多元回归估算技术重新分析整个数据。因此，大约五分之一以前被视为失踪的案件得以从档案中保存下来，并输入数据集。然而，从这个过程中得到的结果并没有表现出任何显着的差异与以前的结果相比，其最佳模型的选择，估计参数的最佳模型。因此，未进行此类插补的数据集用于最终分析，包括探索所选最佳模型改进的可能性，以避免数据插补引起的潜在问题，例如线性相关性，估计值固定到下限和/或上限。假设包括监控在内的所有学习者背景因素之间存在相互关联的模型的结果显著优于其他模型，后者认为监控不是一个独立的因素，而是一个依赖于正式的、明确的规则导向的教学产生的因素。这一结果支持了Krashen的学习和习得之间的接口假设模型。尤其是监控与其他因素，尤其是非正式接触英语有显著相关。这一结果表明了一个新的和理论上有趣的模型，这可能是所谓的“反向界面模型”的可能性。在这个模型中，监控语既可以影响非正式接触英语，也可以影响非正式接触英语