A growing body of research has investigated bilingual sentence processing. How to account for differences in native (L1) and non-native (L2) processing is controversial. Some explain L1/L2 differences in terms of different parsing mechanisms, and the hypothesis that L2 learners adopt ‘shallow’ parsing has received considerable attention. Others assume L1/L2 processing is similar, and explain L1/L2 differences in terms of capacity-based limitations being exceeded during L2 processing. More generally, the role that working memory plays in language acquisition and processing has garnered increasing interest. Based on research investigating L2 sentence processing, I claim that a primary source of L1/L2 differences lies in the ability to retrieve information constructed during sentence processing from memory. In contrast to describing L1/L2 differences in terms of shallow parsing or capacity limitations, I argue that L2 speakers are more susceptible to retrieval interference when successful comprehension requires access to information from memory.

As in any field of scientific inquiry, advancements in the field of second language acquisition (SLA) rely in part on the interpretation and generalizability of study findings using quantitative data analysis and inferential statistics. While statistical techniques such as ANOVA and t-tests are widely used in second language research, this review article provides a review of a class of newer statistical models that have not yet been widely adopted in the field, but have garnered interest in other fields of language research. The class of statistical models called mixed-effects models are introduced, and the potential benefits of these models for the second language researcher are discussed. A simple example of mixed-effects data analysis using the statistical software package R (R Development Core Team, 2011) is provided as an introduction to the use of these statistical techniques, and to exemplify how such analyses can be reported in research articles. It is concluded that mixed-effects models provide the second language researcher with a powerful tool for the analysis of a variety of types of second language acquisition data.

Second language acquisition researchers often face particular challenges when attempting to generalize study findings to the wider learner population. For example, language learners constitute a heterogeneous group, and it is not always clear how a study's findings may generalize to other individuals who may differ in terms of language background and proficiency, among many other factors. In this paper, we provide an overview of how mixed‐effects models can be used to help overcome these and other issues in the field of second language acquisition. We provide an overview of the benefits of mixed‐effects models and a practical example of how mixed‐effects analyses can be conducted. Mixed‐effects models provide second language researchers with a powerful statistical tool in the analysis of a variety of different types of data.

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