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Article published In: Journal of Second Language Studies
Vol. 1:2 (2018) ► pp.277–309

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On over- and underuse in learner corpus research and multifactoriality in corpus linguistics more generally

 | University of California, Santa Barbara | Justus Liebig University Giessen
Published online: 27 August 2018
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https://doi.org/10.1075/jsls.00005.gri
Abstract
This paper critically discusses how corpus linguistics in general, but learner corpus research in particular, has been dealing with all sorts of frequency data in general, but over- and underuse frequencies in particular. I demonstrate on the basis of learner corpus data the pitfalls of using aggregate data and lacking statistical control that much work is unfortunately characterized by. In fact, I will demonstrate that monofactorial methods have very little to offer at all to research on observational data. While this paper is admittedly very didactic and methodological, I think the discussion of the empirical data offered here – a reanalysis of previously published work – shows how misleading many studies potentially and provides far-reaching implications for much of corpus linguistics and learner corpus research. Ideally/maximally, this paper together with Paquot & Plonsky (Paquot, M. & Plonsky, L. (2017). Quantitative research methods and study quality in learner corpus research. International Journal of Learner Corpus Research, 3(1), 61–94. Google Scholar logo with link to Google Scholar, Intntl. J. of Learner Corpus Research) would lead to a complete revision of how learner corpus linguists use quantitative methods and study over-/underuse; minimally, this paper would stimulate a much-needed discussion of currently lacking methodological sophistication.
Article outline
  • 1.Introduction
    • 1.1General introduction
    • 1.2The two goals of this paper
  • 2.A regression-modeling approach to over- and underuse
    • 2.1The data: quite in learner and native-speaker data
    • 2.2A generalized linear model on the Hasselgård and Johansson data
    • 2.3Why a generalized linear model is not enough
      • 2.3.1Which files to include?
      • 2.3.2The role of individual speakers
    • 2.4A generalized linear mixed-effects model on the Hasselgård and Johansson data
  • 3.Multifactoriality: What it means for over- and underuse and in general
    • 3.1Why virtually every corpus study, every one, needs to be multifactorial
    • 3.2The role of other predictors
      • 3.2.1Traditional multifactorial regression modeling
      • 3.2.2Multifactorial Prediction and Deviation Analysis Using Regressions (or other classifiers)
      • 3.2.3MupDAR: A very brief example
  • 4.Concluding remarks
  • Acknowledgements
  • Notes
  • References
References (26)
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