Abstract
This article introduces two sophisticated statistical modeling techniques that allow researchers to analyze systematicity, individual variation, and nonlinearity in second language (L2) development. Generalized linear mixed-effects models can be used to quantify individual variation and examine systematic effects simultaneously, and generalized additive mixed models allow for the examination of systematicity, individuality, and nonlinearity within a single model. Based on a longitudinal learner corpus, this article illustrates the usefulness of these models in the context of L2 accuracy development of English grammatical morphemes. I discuss the strengths of each technique and the ways in which these techniques can benefit L2 acquisition research, further highlighting the importance of accounting for individual variation in modeling L2 development.
Original language | English |
---|---|
Pages (from-to) | 834-871 |
Number of pages | 38 |
Journal | Language Learning |
Volume | 66 |
Issue number | 4 |
Early online date | 17 Feb 2016 |
DOIs | |
Publication status | Published - Dec 2016 |
Keywords
- statistical modeling
- mixed-effects model
- generalized additive mixed model
- learner corpus
- individual variation
- grammatical morphemes