Quantitative perspectives in Cognitive Linguistics
As a usagebased approach to the study of language, cognitive linguistics is theoretically well poised to apply
quantitative methods to the analysis of corpus and experimental data. In this article, I review the historical circumstances that
led to the quantitative turn in cognitive linguistics and give an overview of statistical models used by cognitive linguists,
including chisquare test, Fisher test, Binomial test, ttest, ANOVA, correlation, regression, classification and regression
trees, naïve discriminative learning, cluster analysis, multidimensional scaling, and correspondence analysis. I stress the
essential role of introspection in the design and interpretation of linguistic studies, and assess the pros and cons of the
quantitative turn. I also make a case for open access science and appropriate archiving of linguistic data.
Article outline
 1.Introduction
 2.What brought about the quantitative turn?
 2.1A usagebased model of language is datafriendly
 2.2Advent of electronic language resources
 2.3Advent of analytical tools
 3.What does the quantitative turn bring us?
 3.1Quantitative methods in cognitive linguistics
 3.1.1Is A different from B? Chisquare test, Fisher test, Binomial test, ttest, ANOVA
 3.1.2What factors are associated with A? Correlation, regression, mixed effects regression, classification and regression trees, naïve discriminative learning
 3.1.3What is the structure of relationships among a group of items? Cluster analysis, multidimensional scaling, correspondence analysis
 3.2Role of introspection
 4.Where does the quantitative turn lead us?
 4.1Opportunities
 4.2Dangers
 5.Conclusion

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