Quantitative perspectives in Cognitive Linguistics
As a usage-based 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 chi-square test, Fisher test, Binomial test, t-test, ANOVA, correlation, regression, classification and regression
trees, naïve discriminative learning, cluster analysis, multi-dimensional 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.
- 2.What brought about the quantitative turn?
- 2.1A usage-based model of language is data-friendly
- 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? Chi-square test, Fisher test, Binomial test, t-test, 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, multi-dimensional scaling, correspondence analysis
- 3.2Role of introspection
- 4.Where does the quantitative turn lead us?