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Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion

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Mixed-Effects Regression Models in Linguistics

Abstract

We explore methods for evaluating logistic mixed-effects models of both corpus and experimental data types through simulations. We suggest that the fit of the model should be evaluated by examining the variance explained by the fixed effects alone, rather than both fixed and random effects put together. Nonetheless, for corpus data, in which frequent items contribute more observations, coefficient estimates for fixed effects should be derived from a model that includes the random effects. Including random effects in the model with such datasets allows for better estimates of the fixed-effects predictor coefficients. Not having random effects in the model can cause fixed-effects coefficients to be overly influenced by frequent items, which are often exceptional in linguistic data due to lexical diffusion of ongoing changes.

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Acknowledgments

We would like to thank the two helpful anonymous reviewers of this article, as well as Daniel Johnson for bringing the Nakagawa and Schielzeth’s [51] paper to our attention. We further thank the audience of the LSD 2012 for helpful comments and questions.

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Authors and Affiliations

  1. University of Oregon, Eugene, OR, USA

    Danielle Barth & Vsevolod Kapatsinski

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  1. Danielle Barth
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  2. Vsevolod Kapatsinski
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Correspondence to Vsevolod Kapatsinski .

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Editors and Affiliations

  1. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Dirk Speelman

  2. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Kris Heylen

  3. Faculty of Arts, Research Group QLVL, KU Leuven, Belgium

    Dirk Geeraerts

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Barth, D., Kapatsinski, V. (2018). Evaluating Logistic Mixed-Effects Models of Corpus-Linguistic Data in Light of Lexical Diffusion. In: Speelman, D., Heylen, K., Geeraerts, D. (eds) Mixed-Effects Regression Models in Linguistics. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-69830-4_6

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