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Abstract As a usage-based approach to the study 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.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 5 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |