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.
Included in
Anthropological Linguistics and Sociolinguistics Commons, Psycholinguistics and Neurolinguistics Commons
Date Posted: 12 November 2009
Comments
University of Pennsylvania Institute for Research in Cognitive Science Technical Report No. IRCS-09-02