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Exploring Individual Variation in Learner Corpus Research: Methodological Suggestions

Published online by Cambridge University Press:  29 December 2020

By
Stefanie Wulff  and
Stefan Th. Gries
Edited by
Bert Le Bruyn  and
Magali Paquot
Bert Le Bruyn
Affiliation:
UIL-OTS, Utrecht University
Magali Paquot
Affiliation:
FNRS – Centre for English Corpus Linguistics, UCLouvain
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Summary

Second Language Acquisition is a complex process, and Learner Corpus Research is increasingly turning to complex statistical methods. While traditional approaches mostly relied on simple frequency counts and monofactorial analyses, some recent studies employ more sophisticated statistics that permit the inclusion of more than one predictor variable as well as more varied kinds of probabilistic/distributional information such as association strengths and dispersion values. One such recent approach is called MuPDAR (Multifactorial Prediction and Deviation Analysis Using Regression; Gries & Adelman 2014, Gries & Deshors 2014). One intriguing aspect of MuPDAR is its extensibility: in its current form, the exploration of the learner data can be L1-specific, but in the present paper we extend this approach towards also including speaker-specific effects. As such, this method should appeal to the growing number of researchers who are interested in investigating individual variation. We present a case study of genitive alternation in the Chinese and German sections of the International Corpus of Learner English alongside English native speaker data obtained from the International Corpus of English, and we illustrate different ways in which the MuPDAR approach could be extended to obtain models that license deeper interpretation at the individual speaker level.

Keywords

Chinese learner Englishgenitive alternationGerman learner Englishindividual variationInternational Corpus of Learner English (ICLE)MuPDAR

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Learner Corpus Research Meets Second Language Acquisition , pp. 191 - 213
Publisher: Cambridge University Press
Print publication year: 2021

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