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Part III - Perspectives on Multifactorial Methods

Published online by Cambridge University Press:  06 May 2022

Edited by
Ole Schützler  and
Julia Schlüter
Ole Schützler
Affiliation:
Universität Leipzig
Julia Schlüter
Affiliation:
Universität Bamberg
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Data and Methods in Corpus Linguistics
Comparative Approaches
 , pp. 161 - 288
Publisher: Cambridge University Press
Print publication year: 2022

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References

Further Reading

Levshina, Natalia. 2015. How to Do Linguistics with R: Data Exploration and Statistical Analysis. Amsterdam: John Benjamins. Chapter 14.Google Scholar
Gries, Stefan Th. 2020. On Classification Trees and Random Forests in Corpus Linguistics: Some Words of Caution and Suggestions for Improvement. Corpus Linguistics and Linguistic Theory 16(3). 617–47.Google Scholar
Field, Andy, Miles, Jeremy and Field, Zoe. 2012. Discovering Statistics Using R. London: Sage. Chapter 19.Google Scholar

References

Anthony, Lawrence. 2017. AntConc (Version 3.5.7). Computer software. Tokyo: Waseda University.Google Scholar
Bernaisch, Tobias. 2015. The Lexis and Lexicogrammar of Sri Lankan English. Amsterdam: John Benjamins.CrossRefGoogle Scholar
Bernaisch, Tobias, Gries, Stefan Th and Joybrato, Mukherjee. 2014. The Dative Alternation in South Asian Englishes: Modelling Predictors and Predicting Prototypes. English World-Wide 35(1). 7–31.Google Scholar
Breiman, Leo. 2001. Random Forests. Machine Learning 45. 5–32.Google Scholar
Clark, Herbert, and Fox Tree, Jean E.. 2002. Using Uh and Um in Spontaneous Speaking. Cognition 84. 73–111.CrossRefGoogle Scholar
Ephratt, Michal. 2008. The Functions of Silence. Journal of Pragmatics 40(11). 1909–38.CrossRefGoogle Scholar
Field, Andy, Miles, Jeremy and Field, Zoe. 2012. Discovering Statistics Using R. London: Sage.Google Scholar
Fokkema, Marjolein, Edbrooke-Childs, Julian and Wolpert, Miranda. 2021. Generalized Linear Mixed-Model (GLMM) Trees: A Flexible Decision-Tree Method for Multilevel and Longitudinal Data. Psychotherapy Research 31(3). 329–41.Google Scholar
Fokkema, Marjolein, Smits, Niels, Zeileis, Achim, Hothorn, Torsten and Kelderman, Henk. 2018. Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees. Behavior Research Methods 50(5). 2016–34.Google Scholar
Gilquin, Gaëtanelle. 2008. Hesitation Markers among EFL Learners: Pragmatic Deficiency or Difference. In Romero-Trillo, Jesús, ed. Pragmatics and Corpus Linguistics. Berlin: Mouton de Gruyter. 119–43.Google Scholar
Greenbaum, Sidney. 1991. The Development of the International Corpus of English. In Aijmer, Karin and Alternberg, Bengt, eds. English Corpus Linguistics: Studies in Honour of Jan Svartvik. London: Longman. 83–91.Google Scholar
Gries, Stefan Th. 2013. Statistics for Linguistics with R: A Practical Introduction. 2nd ed. Berlin: Mouton de Gruyter.CrossRefGoogle Scholar
Gries, Stefan Th. 2018. On Over- and Underuse in Learner Corpus Research and Multifactoriality in Corpus Linguistics More Generally. Journal of Second Language Studies 1(2). 276–308.Google Scholar
Gries, Stefan Th. 2020. On Classification Trees and Random Forests in Corpus Linguistics: Some Words of Caution and Suggestions for Improvement. Corpus Linguistics and Linguistic Theory 16(3). 617–47.Google Scholar
Gries, Stefan Th., and Deshors, Sandra C.. 2014. Using Regressions to Explore Deviations between Corpus Data and a Standard/Target: Two Suggestions. Corpora 9(1). 109–36.Google Scholar
Harrell, Frank. 2015. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. New York: Springer.Google Scholar
Heller, Benedikt. 2017. Stability and Fluidity in Syntactic Variation World-Wide: The Genitive Alternation across Varieties of English. Doctoral thesis. KU Leuven.Google Scholar
Heller, Benedikt, Bernaisch, Tobias and Gries, Stefan Th.. 2017. Empirical Perspectives on Two Potential Epicenters: The Genitive Alternation in Asian Englishes. ICAME Journal 41. 111–44.Google Scholar
Hothorn, Torsten and Zeileis, Achim. 2015. Partykit: A Modular Toolkit for Recursive Partytioning in R. Journal of Machine Learning Research 16. 3905–9.Google Scholar
James, Gareth, Witten, Daniela, Hastie, Trevor and Tibshirani, Robert. 2015. An Introduction to Statistical Learning with Applications in R. New York: Springer.Google Scholar
Kuhn, Max. 2008. Building Predictive Models in R Using the Caret Package. Journal of Statistical Software 28(5). 1–26.CrossRefGoogle Scholar
Kuhn, Max, and Johnson, Kjell. 2016. Applied Predictive Modeling. New York: Springer.Google Scholar
Kuhn, Max. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. 2018. Caret: Classification and regression training [R package version 6.0–81]. https://CRAN.R-project.org/package=caret.Google Scholar
Lange, Claudia. 2012. The Syntax of Spoken Indian English. Amsterdam: John Benjamins.Google Scholar
Maclay, Howard, and Osgood, Charles E.. 1959. Hesitation Phenomena in Spontaneous English Speech. Word 15(1). 19–44.Google Scholar
Mukherjee, Joybrato. 2000. Speech Is Silver, but Silence Is Golden: Some Remarks on the Function(s) of Pauses. Anglia 118(4). 571–84.Google Scholar
Mukherjee, Joybrato. 2010. The Development of English in India. In Kirkpatrick, Andy, ed. The Routledge Handbook of World Englishes. London: Routledge. 167–80.Google Scholar
Oviatt, Sharon. 1995. Predicting Spoken Disfluencies During Human-Computer Interaction. Computer Speech & Language 9(1). 19–35.CrossRefGoogle Scholar
Revis, Melanie, and Bernaisch, Tobias. 2020. The Pragmatic Nativisation of Pauses in Asian Englishes. World Englishes 39(1). 135–53.Google Scholar
Rayson, Paul, Leech, Geoffrey N. and Hodges, Mary. 1997. Social Differentiation in the Use of English Vocabulary: Some Analyses of the Conversational Component of the British National Corpus. International Journal of Corpus Linguistics 2(1). 133–52.CrossRefGoogle Scholar
Stenström, Anna-Brita. 1990. Pauses in Monologue and Dialogue. In Svartvik, Jan, ed. The London-Lund Corpus of Spoken English: Description and Research. Lund: Lund University Press. 211–52.Google Scholar
Strobl, Carolin, Malley, James D. and Tutz, Gerhard. 2009. An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychological Methods 14(4). 323–48.Google Scholar
Tottie, Gunnel. 2014a. On the Use of Uh and Um in American English. Functions of Language 21(1). 6–29.Google Scholar
Tottie, Gunnel. 2014b. Turn Management and the Fillers Uh and Um. In Aijmer, Karin and Rühlemann, Christoph, eds. Corpus Pragmatics: A Handbook. Cambridge: Cambridge University Press. 381–407.Google Scholar
Venables, William N., and Ripley, Brian D.. 2002. Modern Applied Statistics with S. 4th ed. New York: Springer.Google Scholar
Zuur, Alain F., Ieno, Elena N., Walker, Neil and Saveliev, Anatoly A.. 2009. Mixed Effects Models and Extensions in Ecology with R. Berlin: Springer.CrossRefGoogle Scholar

Further Reading

Agresti, Alan. 2013. Categorical Data Analysis. Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
James, Gareth, Daniela Witten, , Trevor Hastie, and Robert Tibshirani, . 2013. An Introduction to Statistical Learning with Applications in R. New York: Springer.Google Scholar
Vanderschueren, Clara, and Ludovic De Cuypere, . 2014. The Inflected/Non-Inflected Infinitive Alternation in Portuguese Adverbial Clauses. A Corpus Analysis. Language Sciences 41. 153–74.CrossRefGoogle Scholar

References

Agresti, Alan. 2013. Categorical Data Analysis. Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
Baayen, R. Harald. 2008. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Biber, Douglas, Johansson, Stig, Leech, Geoffrey, Conrad, Susan, and Finegan, Edward. 1999. Longman Grammar of Spoken and Written English. Harlow: Longman.Google Scholar
Breiman, Leo, Friedman, Jerome, Olshen, Richard and Stone, Charles. 1984. Classification and Regression Trees. Boca Raton, FL: Chapman & Hall.Google Scholar
Chipman, Hugh, George, Edward and Richard, McCulloch. 2010. BART: Bayesian Additive Regression Trees. The Annals of Applied Statistics 4(1). 266–98.Google Scholar
Feist, Jim. 2012. What Controls the “Genitive Variation” in Present-Day English? Studies in Language 36(2). 261–99.CrossRefGoogle Scholar
Fox, John, Weisberg, Sandford, Price, Brad, Friendly, Michael and Hong, Jangman. 2019. effects: Effect Displays for Linear, Generalized Linear, and Other Models. R package, version 4.1–4. https://CRAN.R-project.org/package=effects.Google Scholar
Gries, Stefan Th. 2013. Statistics for Linguistics with R: A Practical Introduction. Berlin: de Gruyter Mouton.Google Scholar
Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. 2015. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.Google Scholar
Hinrichs, Lars, Szmrecsanyi, Benedikt and Bohmann, Axel. 2015. Which-Hunting and the Standard English Relative Clause. Language 91(4). 806–36.Google Scholar
Kuhn, Max, and Johnson, Kjell. 2013. Applied Predictive Modeling. New York: Springer.Google Scholar
Labov, William. 1969. Contraction, Deletion, and Inherent Variability of the English Copula. Language 45(4). 715–62.Google Scholar
Labov, William 1982. Building on Empirical Foundations. In Lehmann, Winfred P. and Malkiel, Yakov, eds. Perspectives on Historical Linguistics. Amsterdam and Philadelphia: John Benjamins. 17–92.CrossRefGoogle Scholar
Liaw, Andy, and Wiener, Matthew. 2002. Classification and Regression by randomForest. R News 2(3). 18–22.Google Scholar
Rickford, John, Ball, Arnetha, Blake, Renee, Jackson, Raina and Martin, Nomi. 1991. Rappin on the Copula Coffin: Theoretical and Methodological Issues in the Analysis of Copula Variation in African-American Vernacular English. Language Variation and Change 3. 103–32.Google Scholar
Ripley, Brian. 2018. tree: Classification and Regression Trees. R package, version 1.0–39. https://CRAN.R-project.org/package=tree.Google Scholar
Rosenbach, Anette. 2014. English Genitive Variation: The State of the Art. English Language and Linguistics 18(2). 215–62.CrossRefGoogle Scholar
Röthlisberger, Melanie, Grafmiller, Jason and Szmrecsanyi, Benedikt. 2017. Cognitive Indigenization Effects in the English Dative Alternation. Cognitive Linguistics. 28(4). 673–710.CrossRefGoogle Scholar
Sankoff, David, and Rousseau, Pascale. 1989. Statistical Evidence for Rule Ordering. Language Variation and Change 1(1). 1–18.Google Scholar
Szmrecsanyi, Benedikt, Biber, Douglas, Egbert, Jesse and Franco, Karlien. 2016. Toward More Accountability: Modeling Ternary Genitive Variation in Late Modern English. Language Variation and Change 28. 1–29.Google Scholar
Tagliamonte, Sali. 2006. Analysing Sociolinguistic Variation. Cambridge: Cambridge University Press.Google Scholar
Venables, William, and Ripley, Brian. 2002. Modern Applied Statistics with S. New York: Springer.Google Scholar

Further Reading

Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.Google Scholar
Gelman, Andrew, Jakulin, Aleks, Pittau, Maria Grazia and Yu-Sung, Su. 2008. A Weakly Informative Prior Distribution for Logistic and Other Regression Models. The Annals of Applied Statistics 2(4). 1360–83. https://doi.org/10.1214/08-AOAS191.Google Scholar
Kruschke, John K. 2011a. Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Oxford: Elsevier.Google Scholar
McElreath, Richard. 2016. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Boca Raton, FL: CRC Press.Google Scholar
Nicenboim, Bruno, and Vasishth, Shravan. 2016. Statistical Methods for Linguistic Research: Foundational Ideas: Part II. Language and Linguistics Compass 10. 591–613. https://doi.org/10.1111/lnc3.12207.Google Scholar
van de Schoot, Rens, and Depaoli, Sarah. 2014. Bayesian Analyses: Where to Start and What to Report. The European Health Psychologist 16(2). 75–84.Google Scholar
van de Schoot, Rens, David Kaplan, Jaap J. Denissen, Jens B. Asendorpf, Franz J. Neyer, Marcel A. G. van Aken, . 2014. A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development 85. 842–860. https://doi.org/10.1111/cdev.12169Google Scholar

References

Bartoń, Kamil. 2018. MuMIn: Multi-Model Inference. R package version 1.42.1. https://CRAN.R-project.org/package=MuMIn.Google Scholar
Bates, Douglas, Maechler, Martin, Bolker, Ben and Walker, Steve. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67(1). 1–48. https://doi.org/10.18637/jss.v067.i01.Google Scholar
Biber, Douglas, Johansson, Stig, Leech, Geoffrey, Conrad, Susan and Finegan, Edward. 1999. Longman Grammar of Spoken and Written English. Harlow: Longman.Google Scholar
Bürkner, Paul-Christian. 2017. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software 80(1). 1–28. https://doi.org/10.18637/jss.v080.i01.Google Scholar
Davies, Mark. 2008–. The Corpus of Contemporary American English (COCA): 560 million words, 1990–present. https://corpus.byu.edu/coca/.Google Scholar
Dixon, R.M.W. 1991. A New Approach to English Grammar, on Semantic Principles. Oxford: Clarendon Press.Google Scholar
Fox, John. 2003. Effect Displays in R for Generalised Linear Models. Journal of Statistical Software 8(15). 1–27. www.jstatsoft.org/v08/i15.CrossRefGoogle Scholar
Gelman, Andrew, and Hill, Jennifer. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.Google Scholar
Gelman, Andrew, Jakulin, Aleks, Pittau, Maria Grazia and Yu-Sung, Su. 2008. A Weakly Informative Prior Distribution for Logistic and Other Regression Models. The Annals of Applied Statistics 2(4). 1360–83. https://doi.org/10.1214/08-AOAS191.CrossRefGoogle Scholar
Goodman, Steven. 2008. A Dirty Dozen: Twelve P-Value Misconceptions. Seminars in Hematology 45(3). 135–40. https://doi.org/10.1053/j.seminhematol.2008.04.003.Google Scholar
Goodman, Steven N., Fanelli, Daniele and John, P. A. Ioannidis. 2016. What Does Research Reproducibility Mean? Science Translational Medicine 8(341). 12. https://doi.org/10.1126/scitranslmed.aaf5027.Google Scholar
Huddleston, Rodney, and Pullum, Geoffrey K.. 2002. The Cambridge Grammar of the English Language. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316423530.Google Scholar
Kruschke, John K. 2011a. Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Oxford: Elsevier.Google Scholar
Kruschke, John K. 2011b. Introduction to Special Section on Bayesian Data Analysis. Perspectives on Psychological Science 6(3). 272–3. https://doi.org/10.1177/1745691611406926.Google Scholar
Levshina, Natalia. 2016. When Variables Align: A Bayesian Multinomial Mixed-Effects Model of English Permissive Constructions. Cognitive Linguistics 27(2). 235–68.Google Scholar
Levshina, Natalia. 2018. Probabilistic Grammar and Constructional Predictability: Bayesian Generalized Additive Models of Help + (To) Infinitive in Varieties of Web-Based English. Glossa 3(1). 55. 1–22. https://doi.org/10.5334/gjgl.294/.Google Scholar
Levshina, Natalia. In press. Communicative Efficiency: Language Structure and Use. Cambridge: Cambridge University Press.Google Scholar
Lind, Age. 1983. The Variant Forms of Help to/Help Ø. English Studies 64. 263–75. https://doi.org/10.1080/00138388308598255.Google Scholar
Lohmann, Arne. 2011. Help vs. Help to: A Multifactorial, Mixed-Effects Account of Infinitive Marker Omission. English Language and Linguistics 15(3). 499–521. https://doi.org/10.1017/S1360674311000141.Google Scholar
Lunn, David, Jackson, Christopher, Best, Nicky, Thomas, Andrew and Spiegelhalter, David. 2013. The BUGS Book: A Practical Introduction to Bayesian Analysis. Boca Raton, FL: CRC Press.Google Scholar
Mair, Christian. 2002. Three Changing Patterns of Verb Complementation in Late Modern English: A Real-Time Study Based on Matching Text Corpora. English Language and Linguistics 6(1). 105–31. https://doi.org/10.1017/S1360674302001065.Google Scholar
McElreath, Richard. 2016. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Boca Raton, FL: CRC Press.Google Scholar
McEnery, Anthony and Xiao, Zhonghua. 2005. HELP or HELP to: What Do Corpora Have to Say? English Studies 86(2). 161–87. https://doi.org/10.1080/0013838042000339880.Google Scholar
Nakagawa, Shinichi, Johnson, Paul C. D. and Schielzeth, Holger. 2017. The Coefficient of Determination R2 and Intra-Class Correlation Coefficient from Generalized Linear Mixed-Effects Models Revisited and Expanded. Journal of The Royal Society Interface 14(134). http://doi.org/10.1098/rsif.2017.0213.Google Scholar
Nicenboim, Bruno, and Vasishth, Shravan. 2016. Statistical Methods for Linguistic Research: Foundational Ideas: Part II. Language and Linguistics Compass 10. 591–613. https://doi.org/10.1111/lnc3.12207.Google Scholar
Rohdenburg, Günter. 1996. Cognitive Complexity and Increased Grammatical Explicitness in English. Cognitive Linguistics 7(2). 149–82. https://doi.org/10.1515/cogl.1996.7.2.149.Google Scholar
Rohdenburg, Günter. 2003. Horror Aequi and Cognitive Complexity as Factors Determining the Use of Interrogative Clause Linkers. In Rohdenburg, Günter and Mondorf, Britta, eds. Determinants of Grammatical Variation in English. Berlin: Mouton de Gruyter. 205–50. https://doi.org/10.1515/9783110900019.205.Google Scholar
Rohdenburg, Günter. 2009. Grammatical Divergence between British and American English in the Nineteenth and Early Twentieth Centuries. In Ingrid van Ostade, Tieken-Boon and van der Wurff, Wim, eds. Current Issues in Late Modern English. Linguistic Insights 77. Bern: Peter Lang. 301–30.Google Scholar
Schlüter, Julia. 2003. Phonological Determinants of Grammatical Variation in English: Chomsky’s Worst Possible Case. In Rohdenburg, Günter and Mondorf, Britta, eds. Determinants of Grammatical Variation in English. Berlin/New York, NY: Mouton de Gruyter. 69–118.Google Scholar
Scrivner, Olga B. 2015. A Probabilistic Approach in Historical Linguistics: Word Order Change in Infinitival Clauses: From Latin to Old French. Doctoral dissertation. Indiana University.Google Scholar
Straka, Milan, and Jana, Straková. 2017. Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe. Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Vancouver, Canada, August 2017.Google Scholar
Van De Schoot, Rens, and Depaoli, Sarah. 2014. Bayesian Analyses: Where to Start and What to Report. The European Health Psychologist 16(2). 75–84.Google Scholar
van de Schoot, Rens, David Kaplan, Jaap J. Denissen, Jens B. Asendorpf, Franz J. Neyer and Marcel A. G. van Aken. 2014. A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development 85. 842–60. https://doi.org/10.1111/cdev.12169.Google Scholar
Vasishth, Shravan, Chen, Zhong, Qiang, Li and Guo, Guelian. 2013. Processing Chinese Relative Clauses: Evidence for the Subject-Relative Advantage. PLoS ONE 8(10). 1–14. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0077006.Google Scholar
Wasow, Thomas, Levy, Roger, Melnick, Robin, Zhu, Hanzhi and Juzek, Tom. 2015. Processing, Prosody, and Optional to. In Frazier, Lyn and Gibson, Edward, eds. Explicit and Implicit Prosody in Sentence Processing. New York: Springer. 133–58. https://doi.org/10.1007/978–3–319–12961–7_8.Google Scholar

Further Reading

Borg, Ingwer, and Patrick, J. F. Groenen. 2005. Modern Multidimensional Scaling. New York: Springer.Google Scholar
Everitt, Brian S., Landau, Sabine, Leese, Morven and Stahl, Daniel. 2011. Cluster Analysis. Chichester, UK: John Wiley & Sons.Google Scholar
Moisl, Hermann. 2015. Cluster Analysis for Corpus Linguistics. Berlin: Mouton de Gruyter.Google Scholar

References

Anscombre, Jean-Claude. 1989. Théorie de l’argumentation, topoï, et structuration discursive. Revue Québécoise de Linguistique 18(1). 13–55.Google Scholar
Anthony, Laurence. 2018. AntConc. A Freeware Corpus Analysis Toolkit for Concordancing and Text Analysis. v. 3.5.7. www.laurenceanthony.net/software.html.Google Scholar
Azar, Moshe. 1997. Concessive Relations as Argumentations. Text 17(3). 301–16.Google Scholar
Bengtsson, Henrik, Bravo, Hector Corrada, Gentleman, Robert et al. 2019. Functions that Apply to Rows and Columns of Matrices (and to Vectors) (‘matrixStats’). R package. v. 0.55.0. https://cran.r-project.org/web/packages/matrixStats/matrixStats.pdf.Google Scholar
Biber, Douglas. 1988. Variation across Speech and Writing. Cambridge: Cambridge University Press.Google Scholar
Borg, Ingwer, and Groenen, Patrick J. F.. 2005. Modern Multidimensional Scaling. New York: Springer.Google Scholar
Bürkner, Paul. 2019. Bayesian Regression Models Using ‘Stan’ (‘brms’). R package v. 2.8.0. https://cran.r-project.org/web/packages/brms/brms.pdf.Google Scholar
Cormack, R. M. 1971. A Review of Classification. Journal of the Royal Statistical Society A 134. 321–67.Google Scholar
Di Meola, Claudio. 1998. Zur Definition einer logisch-semantischen Kategorie: Konzessivität als „versteckte Kausalität“. Linguistische Berichte 175. 329–52.Google Scholar
Diessel, Holger. 2005. Competing Motivations for the Ordering of Main and Adverbial Clauses. Linguistics 43(3). 449–70.Google Scholar
Dugard, Pat, Todman, John and Staines, Harry. 2010. Approaching Multivariate Analysis: A Practical Introduction. New York: Routledge.Google Scholar
Dunn, J. C. 1974. Well Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics 4(1). 95–104.CrossRefGoogle Scholar
Everitt, Brian S., Landau, Sabine, Leese, Morven and Stahl, Daniel. 2011. Cluster Analysis. Chichester, UK: John Wiley & Sons.Google Scholar
Gordon, Allan D. 1999. Classification. Boca Raton, FL: Chapman and Hall/CRC.CrossRefGoogle Scholar
Gries, Stefan Th. 2013. Statistics for Linguistics with R: A Practical Introduction. Berlin: Mouton de Gruyter.Google Scholar
Handl, Julia, Knowles, Joshua and Kell, Douglas B.. 2005. Computational Cluster Validation in Post-Genomic Data Analysis. Bioinformatics 21(15). 3201–212.Google Scholar
Hawkins, John A. 1994. A Performance Theory of Order and Constituency. Cambridge: Cambridge University Press.Google Scholar
Hermodsson, Lars. 1994. Der Begriff „konzessiv“: Terminologie und Analysen. Studia Neophilologica 66. 59–75.Google Scholar
Hilpert, Martin. 2011. Dynamic Visualizations of Language Change: Motion Charts on the Basis of Bivariate and Multivariate Data from Diachronic Corpora. International Journal of Corpus Linguistics 4(16). 435–61.Google Scholar
Hilpert, Martin. 2013. Constructional Change in English: Developments in Allomorphy, Word Formation, and Syntax. Cambridge: Cambridge University Press.Google Scholar
Huddleston, Rodney D. and Pullum, Geoffrey K.. 2002. The Cambridge Grammar of the English Language. Cambridge: Cambridge University Press.Google Scholar
Huson, Daniel H. and Bryant, David. 2017. SplitsTree4. Software for Computing Phylogenetic Networks. v. 4.16.6. www.splitstree.org/.Google Scholar
Kapp, Amy V. and Tibshirani, Robert. 2007. Are Clusters Found in One Dataset Present in Another Dataset? Biostatistics 8(1). 9–31.Google Scholar
König, Ekkehard. 2006. Concessive Clauses. In Brown, Keith, ed. Encyclopedia of Language and Linguistics. Vol. 2. Amsterdam: Elsevier. 820–4.Google Scholar
Kortmann, Bernd, and Wolk, Christoph. 2012. Morphosyntactic Variation in the Anglophone World: A Global Perspective. In Kortmann, Bernd & Lunkenheimer, Kerstin, eds. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter. 906–36.Google Scholar
Kortmann, Bernd, and Lunkenheimer, Kerstin, eds. 2012. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter.Google Scholar
Kruskal, Joseph B., and Wish, Myron. 1978. Multidimensional Scaling. Beverly Hills, CA/London: Sage Publications.Google Scholar
Moisl, Hermann. 2015. Cluster Analysis for Corpus Linguistics. Berlin: Mouton de Gruyter.Google Scholar
Nelson, Gerald. 1996. The Design of the Corpus. In Greenbaum, Sidney, ed. Comparing English Worldwide: The International Corpus of English. Oxford: Clarendon Press. 27–35.Google Scholar
Quirk, Randolph, Greenbaum, Sidney, Leech, Geoffrey and Svartvik, Jan. 1985. A Comprehensive Grammar of the English Language. London: Arnold.Google Scholar
R Development Core Team. 2018. R: A Language and Environment for Statistical Computing. Version 3.5.2. www.R-project.org.Google Scholar
RStudio Team. 2009–18. RStudio: Integrated Development for R. Version 1.1.453. Boston, MA: RStudio, Inc. www.rstudio.com.Google Scholar
Sarkar, Deepayan, and Andrews, Felix. 2016. Extra Graphical Utilities Based on Lattice (‘lattice extra’). R package, version 0.6–28. https://cran.r-project.org/web/packages/latticeExtra/latticeExtra.pdf.Google Scholar
Sarkar, Deepayan. 2008. Lattice. Multivariate Data Visualization with R. Berlin: Springer.Google Scholar
Sarkar, Deepayan. 2014. Lattice Graphics (‘lattice’). R package, version 0.20–29. http://cran.r-project.org/web/packages/lattice/lattice.pdf.Google Scholar
Schliep, Klaus. 2018. Phylogenetic Reconstruction and Analysis (‘phangorn’). R package version 2.4.0. https://cran.r-project.org/web/packages/phangorn/phangorn.pdf.Google Scholar
Schützler, Ole. 2017. A Corpus-Based Study of Concessive Conjunctions in Three L1-Varieties of English. In Buchstaller, Isabelle and Siebenhaar, Beat, eds. Language Variation – European Perspectives VI: Selected Papers from the Eighth International Conference on Language Variation in Europe (ICLaVE 8), Leipzig, May 2015. Amsterdam/Philadelphia: John Benjamins. 173–84.Google Scholar
Schützler, Ole. 2018. Concessive Conjunctions in Written American English: Diachronic and Genre-Related Changes in Frequency and Semantics. In Whitt, Richard J., ed. Diachronic Corpora, Genre, and Language Change. Amsterdam/Philadelphia: John Benjamins.Google Scholar
Sweetser, Eve E. 1990. From Etymology to Pragmatics: Metaphorical and Cultural Aspects of Semantic Structure. Cambridge: Cambridge University Press.Google Scholar
Szmrecsanyi, Benedikt. 2012. Typological Profile: L1 Varieties. In Kortmann, Bernd, and Lunkenheimer, Kerstin, eds. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter. 826–42.Google Scholar
Werner, Valentin. 2014. The Present Perfect in World Englishes: Charting Unity and Diversity. Bamberg: University of Bamberg Press.Google Scholar
Wheeler, Eric S. 2005. Multidimensional Scaling for Linguistics. In Altmann, Gabriel, Piotrowski, Rajmund G. and Köhler, Reinhard, eds. Quantitative Linguistics: An International Handbook. Berlin: Walter de Gruyter. 548–53.Google Scholar

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Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

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