Gorman, Kyle

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2009 - 20102
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  • Publication
    Hierarchical regression modeling for language research
    (2009-11-01) Gorman, Kyle
    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.
  • Publication
    The Consequences of Multicollinearity among Socioeconomic Predictors of Negative Concord in Philadelphia
    (2010-01-01) Gorman, Kyle
    This study is a reanalysis of the external predictors of the use of negative concord in Philadelphia, using archival data from the Language Change and Variation survey. It is shown that the interpretation of the effects of the various socioeconomic measures reported by Labov (2001) was biased by their multicollinearity and by per-subject differences. A new mixed-effects model with residualized socioeconomic predictors and a per-subject random intercept shows the predictive role of all four socioeconomic measures, and the per-subject estimates are used to identify the nascent leaders of linguistic change.