Hierarchical regression modeling for language research

I demonstrate the application of hierarchical regression modeling, a state-of-the-art technique for statistical inference, to language research. First, a stable sociolinguistic variable in Philadelphia (Labov, 2001) is reconsidered, with attention paid to the treatment of collinearities among socioeconomic predictors. I then demonstrate the use of hierarchical models to account for the random sampling of subjects and items in an experimental setting, using data from a study of word-learning in the face of tonal variation (Quam and Swingley, forthcoming). The results from these case studies demonstrate that modeling sampling from the population has empirical consequences. Disciplines Anthropological Linguistics and Sociolinguistics | Psycholinguistics and Neurolinguistics Comments University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-09-02 This technical report is available at ScholarlyCommons: http://repository.upenn.edu/ircs_reports/202 Hierarchical regression modeling for language research∗ Kyle Gorman Department of Linguistics Institute for Research in Cognitive Science University of Pennsylvania [email protected]