IRCS Technical Reports Series
Document Type
Technical Report
Date of this Version
11-1-2009
Abstract
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.
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Date Posted: 12 November 2009
Comments
University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-09-02