AN IMPLICATIONAL MODEL FOR SECOND LANGUAGE RESEARCH
AN IMPLICATIONAL MODEL FOR SECOND LANGUAGE RESEARCH
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第二语言研究的蕴含模型
DOI：
10.1111/j.1467-1770.1978.tb00134.x
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发表时间：
1978
影响因子：
4.4
通讯作者：
R. Andersen
中科院分区：
文献类型：
--
作者：
R. Andersen
本文提出了一个处理二语研究中个体和群体、变异性和系统性的模型。这一模式由排序理论(Bart and Krus1973；Dulay and Burt 1974b)的修订版和社会语言学中使用的隐含分析组成。在简要介绍了隐含分析之后，我用89名西班牙语学习者在英语中使用13个语法语素的数据来描述和说明本文提出的模型。在对数据的分析中，我遵循了克拉申和他的同事们的观点，将语素分为V和NP；自由语素和约束语素，以及德坎普(1973年：144年)的观点，即一般的含意等级是‘…由简单的语法相关的底层标度同时操作产生的表面复合体。本研究的结论支持Krashen(1977)关于语法语素习得的自然顺序，Larsen-Freeman(1976)关于频率的研究，Rosansky(1976)关于频率作为语素顺序的解释，Rosansky(1976a，b)批评横断法未能处理数据中的个体差异，但Rosansky声称她对她的数据的横断式分析与她的一个受试者的纵向分析不一致，从而使她反对横断法的最有力的论点无效。
This paper offers a model for dealing with individuals as well as groups, variability as well as systematicity in L2 research. This model consists of a revised version of the Ordering-Theoretic Method (Bart and Krus 1973; Dulay and Burt 1974b) in conjunction with implicational analysis as used in sociolinguistics. After a brief introduction to implicational analysis, I describe and illustrate the model presented here with data on the use of 13 grammatical morphemes in English by 89 Spanish-speaking learners. In the analysis of the data I follow Krashen and his colleagues in separating morphemes into V and NP; free and bound morphemes, as well as DeCamp's (1973:144) view that general implicational scales are ‘…surface composites resulting from simultaneous operation of simple grammatically-relevant underlying scales.’ The conclusions of this study support Krashen's (1977)‘Natural Order’for the acquisition of grammatical morphemes, Larsen-Freeman's (1976) work on frequency as an explanation for morpheme orders, and Rosansky's (1976) work on frequency as an explanation for morpheme orders, and Rosansky's (1976a,b) criticism of cross-sectional methodology for failing to deal with individual variation in the data, but rejects Rosansky's claim that her cross-sectional analysis of her data does not agree with the longitudinal analysis of one of her subjects and thus invalidates her strongest argument against cross-sectional methodology.