jats:titleAbstract</jats:title>jats:sec<jats:label />jats:pThis 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.</jats:p></jats:sec>jats:secjats:titleOpen Practices</jats:title>jats:p<jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/lang12166-gra-0001.png" xlink:title="Image" /></jats:p>jats:pThis article has been awarded an Open Data badge. All data are publicly accessible via the Open Science Framework at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://osf.io/dbuh4">https://osf.io/dbuh4</jats:ext-link>. Learn more about the Open Practices badges from the Center for Open Science:<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://osf.io/tvyxz/wiki">https://osf.io/tvyxz/wiki</jats:ext-link>.</jats:p></jats:sec>