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Generalizability in mixed models: Lessons from corpus linguistics

Published online by Cambridge University Press:  10 February 2022

Freek Van de Velde
Affiliation:
Department of Linguistics, KU Leuven, Blijde Inkomststraat 21/3308, BE-3000Leuven, Belgium. [email protected] [email protected] [email protected]://www.arts.kuleuven.be/ling/qlvl/people/pages/00039016; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00102617; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00013279
Stefano De Pascale
Affiliation:
Department of Linguistics, KU Leuven, Blijde Inkomststraat 21/3308, BE-3000Leuven, Belgium. [email protected] [email protected] [email protected]://www.arts.kuleuven.be/ling/qlvl/people/pages/00039016; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00102617; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00013279
Dirk Speelman
Affiliation:
Department of Linguistics, KU Leuven, Blijde Inkomststraat 21/3308, BE-3000Leuven, Belgium. [email protected] [email protected] [email protected]://www.arts.kuleuven.be/ling/qlvl/people/pages/00039016; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00102617; https://www.arts.kuleuven.be/ling/qlvl/people/pages/00013279

Abstract

Part of the generalizability issues that haunt controlled lab experiment designs in psychology, and more particularly in psycholinguistics, can be alleviated by adopting corpus linguistic methods. These work with natural data. This advantage comes at a cost: in corpus studies, lexemes and language users can show different kinds of skew. We discuss a number of solutions to bolster the control.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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