2012
DOI: 10.1017/s0954394512000014
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Exploiting random intercepts: Two case studies in sociophonetics

Abstract: An increasing number of sociolinguists are using mixed effects models, models which allow for the inclusion of both fixed and random predicting variables. In most analyses, random effect intercepts are treated as a by-product of the model; they are viewed simply as a way to fit a more accurate model. This paper presents additional uses for random effect intercepts within the context of two case studies. Specifically, this paper demonstrates how random intercepts can be exploited to assist studies of speaker st… Show more

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Cited by 57 publications
(47 citation statements)
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“…A larger coefficient means that a speaker was more likely to produce the /k/ in the quotative than in the discourse particle; the more negative the coefficient, the more likely a speaker was to exhibit a strong CR trend in production. These results are also presented in Drager & Hay (2012).…”
Section: Variation At the Individual Levelsupporting
confidence: 56%
“…A larger coefficient means that a speaker was more likely to produce the /k/ in the quotative than in the discourse particle; the more negative the coefficient, the more likely a speaker was to exhibit a strong CR trend in production. These results are also presented in Drager & Hay (2012).…”
Section: Variation At the Individual Levelsupporting
confidence: 56%
“…SPEAKER and PROMPT were included as random factors to prevent extremes of variation in the behavior of particular speakers or extremes of variation in the production of particular prompts having an undue effect on statistical results, see Drager and Hay (2012) and Hay (2011). The inclusion of SPEAKER as a random factor was particularly important in the construction of models for the formant data, as it helped to remove formant variation that might be attributable to variation in vocal-tract length.…”
Section: H Statistical Analysismentioning
confidence: 99%
“…The idea is to assess the effects of vocal tract length in a different data set, and then statistically remove these effects in our data set of interest. This statistical technique is discussed in d etail, and with reference to the dataset presented here, in Drager and Hay (2012).…”
Section: F3 Of Linking /R/mentioning
confidence: 99%