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Individual effects in variation analysis: Model, software, and research design

Published online by Cambridge University Press:  22 March 2013

John C. Paolillo*
Affiliation:
Indiana University

Abstract

Individual-level variation is a recurrent issue in variationist sociolinguistics. One current approach recommends addressing this via mixed-effects modeling. This paper shows that a closely related model with fixed effects for individual speakers can be directly estimated using Goldvarb. The consequences of employing different approaches to speaker variation are explored by using different model selection criteria. We conclude by discussing the relation of the statistical model to the assumptions of the research design, pointing out that nonrandom selection of speakers potentially violates the assumptions of models with random effects for speaker, and suggesting that a model with fixed effects for speakers may be a better alternative in these cases.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2013

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References

REFERENCES

Agresti, Alan. (1996). An introduction to categorical data analysis. New York: Wiley.Google Scholar
Bates, Douglas, & Maechler, Martin. (2009). Lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-32.Google Scholar
Baayen, R. Harald, Davidson, D. J., & Bates, Douglas M. (2008). Mixed-effects modeling with crossed random effects for subjects and terms. Journal of Memory and Language 59:390412.CrossRefGoogle Scholar
Bishop, Yvonne, Fienberg, Stephen, & Holland, Paul W. (1975). Discrete multivariate analysis: Theory and practice. Cambridge: MIT Press.Google Scholar
Bresnan, Joan, Cueni, Anna, Nikitina, Tatiana, & Baayen, R. Harald. (2007). Predicting the dative alternation. In Boume, G., Kramer, I., and Zwarts, J. (eds.), Cognitive foundations of interpretation. Amsterdam: Royal Netherlands Academy of Science. 6994.Google Scholar
Bresnan, Joan, & Ford, Marilyn. (2010). Predicting syntax: Processing dative constructions in American and Australian varieties of English. Language 86(1):168213.CrossRefGoogle Scholar
Bucholtz, Mary, & Hall, Kira. (2004). Theorizing identity in language and sexuality research. Language in Society 33(4):501547.CrossRefGoogle Scholar
Bucholtz, Mary, & Hall, Kira. (2005). Identity and interaction: A sociocultural linguistic approach. Discourse Studies 7(4):585614.CrossRefGoogle Scholar
Chambers, Jack K. (2009). Sociolinguistic theory: Linguistic variation and its social significance. 3rd ed.Oxford: Blackwell.Google Scholar
Clark, Herb. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior 12:335359.CrossRefGoogle Scholar
D'Arcy, Alexandra. (2005). The development of linguistic constraints: Phonological innovations in St. John's English. Language Variation and Change 17:327355.CrossRefGoogle Scholar
Drager, Katie, & Hay, Jennifer. (2012). Exploiting random intercepts: Two case studies in sociophonetics. Language Variation and Change 24(1):5978.CrossRefGoogle Scholar
Eckert, Penelope, & McConnell-Ginet, Sally. (1999). New generalizations and explanations in language and gender research. Language in Society 28(2):185201.CrossRefGoogle Scholar
Gelman, Andrew, & Hill, Jennifer. (2007). Data analysis using regression and multi-level/hierarchical models. New York: Cambridge University Press.Google Scholar
Guy, Gregory R. (1980). Variation in the group and in the individual: The case of final stop deletion. In Labov, W. (ed.), Locating language in time and space. New York: Academic Press. 136.Google Scholar
Guy, Gregory R.. (1991). Explanation in variable phonology: An exponential model of morphological constraints. Language Variation and Change 3:122.CrossRefGoogle Scholar
Guy, Gregory R., & Boyd, Sally. (1990). The development of a morphological class. Language Variation and Change 2:118.CrossRefGoogle Scholar
Guy, Gregory R., & Cutler, Cecelia. (2011). Speech style and authenticity: Quantitative evidence for the performance of identity. Language Variation and Change 23:139162.CrossRefGoogle Scholar
Jaeger, Florian. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language 59:434446.CrossRefGoogle ScholarPubMed
Johnson, Daniel Ezra. (2009). Getting off the GoldVarb standard: Introducing Rbrul for mixed-effects variable rule analysis. Language and Linguistics Compass 3(1):359383.CrossRefGoogle Scholar
Kreft, Ita, & De Leeuw, Jan. (1998). Introducing multi-level modeling. Thousand Oaks: Sage.CrossRefGoogle Scholar
Labov, William. (1972). The social stratification of (r) in New York City department stores. In Labov, W., Sociolinguistic patterns. Philadelphia: University of Pennsylvania Press. 4369.Google Scholar
Long, J. Scott. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks: Sage.Google Scholar
Nevalainen, Teertu, Ramoulin-Brunberg, H., & Mannila, H. (2011). The diffusion of language change in real time: Progressive and conservative individuals and the time depth of change. Language Variation and Change 23:143.CrossRefGoogle Scholar
Paolillo, John C. (2002). Analyzing linguistic variation: Statistical models and methods. Stanford: Center for the Study of Language and Information.Google Scholar
Pinheiro, Jose, Bates, Douglas, DebRoy, Sakitat, Sarkar, Deepayan, & the R Core Team. (2009). nlme: Linear and nonlinear mixed effects models. R package version 3. 1–93.Google Scholar
Rand, David, & Sankoff, David. (1988). GoldVarb manual. Montreal: Université de Montréal.Google Scholar
R Core Development Team. (2010). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Sankoff, David. (1978). Linguistic variation: Models and methods. New York: Academic Press.Google Scholar
Sankoff, David, Tagliamonte, Sali, & Smith, Eric. (2012). Goldvarb Lion: A multivariate analysis application. Department of Linguistics, University of Toronto.Google Scholar
Sigley, Robert. (2003). The importance of interaction effects. Language Variation and Change 15(2):227253.CrossRefGoogle Scholar
Tagliamonte, Sali. (1998). Was/were variation across the generations: View from the city of York. Language Variation and Change 10(2):153191.CrossRefGoogle Scholar
Tagliamonte, Sali. (2006). Analysing sociolinguistic variation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Tagliamonte, Sali, & Baayen, R. Harald. (2012). Models, forests and trees of York English: Was/were variation as a case study of statistical practice. Language Variation and Change 24:135178.CrossRefGoogle Scholar
Van de Velde, Hans, & van Hout., Roeland. (1998). Dangerous aggregations: A case study of Dutch (n) deletion. In Paradis, C., Vincent, D., Deshaies, D., & Laforest, M. (eds.), Papers in sociolinguistics—NWAVE 26 à l'Université Laval. Québec: Éditions Nota Bene. 137147.Google Scholar
Wolfram, Walter. (1993). Identifying and interpreting variables. In Preston, D. (ed.), American dialect research. Amsterdam: Benjamins. 193221.CrossRefGoogle Scholar