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This paper has benefited from helpful comments from Daniel Ezra Johnson, Rena Torres Cacoullos, and three anonymous reviewers. We would also like to thank Sali Tagliamonte and the audience of the workshop on using statistical tools to explain linguistic variation at New Ways of Analyzing Variation (NWAV) 38.
In the study of language variation and change, there is a long tradition of clustering individuals into structured groups, based on social factors such as age, gender, and social class. Across these groups, we observe the productions of (often binary) variables in order to make inferences about the underlying social patterns. Logistic regression models, such as those implemented in Varbrul, have been the method of choice for binary data, and for continuous data, simple linear regression models have been used.
One statistical critique of regression models without random effects is that outliers can affect reported trends. In contrast to simple regression models, mixed effects modeling allows individual speakers to vary in the model as "random effects." As such, we can test whether there are differences among groups that are robustly present across the dataset, and we can be more confident that the trends are not carried by one or two individuals. This increase in statistical robustness is the primary reason why the field should move beyond simple regression modeling (Baayen, Davidson, & Bates, 2008; Johnson, 2009; Quené & van den Bergh, 2008). But there is an additional reason why the mixed effects model is a useful tool for the sociolinguist, and it is this second benefit that we focus on in this paper.1
Simple regression models group individuals together into stratified groups; the models - by their very design - provide no information about individual variation. Yet studies are increasingly focused on the speech of a single individual; speaker style is emerging at the core of the sociolinguistic enterprise (Eckert, 2008; Podesva, 2007; Zhang, 2005). Mixed effects models provide a way of studying group patterns, while also investigating variation at the individual level. That is, we do not have to choose between regression modeling on the one hand (dispensing with the study of individuals) and qualitative analysis on the other (dispensing with statistical rigor). A mixed effects model is a...