Linguistic Data: Quantitative Analysis and Visualisation
- Within this course you will: ● learn about the principal steps of a quantitative research in linguistics; ● learn about the possibilities and limitations of quantitative approaches as applied to different research questions; ● learn to formulate research questions and develop them into testable hypotheses; ● explore the possibilities of data collection and different approaches to sampling; ● learn to evaluate the quality of a quantitative approach; ● study the most common corpus, experimental, and mixed design of the linguistic studies and learn to evaluate research plans, discover and prevent the associated threats to data validity; ● practice in preparing your quantitative data for analysis, evaluating the quality of your data; treating missing data; ● learn about the possibilities and limitations of conventional statistical techniques and criteria, as well as some popular contemporary multivariate statistical methods; ● learn to choose and apply in practice a set of appropriate statistical tests for your research question.
- are able to account for basic types of data used in linguistic research
- are able to apply basic quantitative methods for analysing linguistic data
- are able to critically discuss the limitations of commonly used methods for answering research questions about language
- are able to reason on how to interpret linguistic results, including how to evaluate what kind of information a given method can offer and how to estimate the potential range of variables that can affect results in linguistic research
- are able to critically evaluate linguistic data presented in previous research
- are able to apply different techniques for presenting both qualitative and quantitative linguistic data in scholarly writing
- №1Introduction to R. Types of data. Dataframe. Functions and arguments.
- №2Descriptive statistics. Basic visualizations.
- №3.Dplyr style in R, pipes. Visualizing data with ggplot2.
- №4.Hypothesis testing. Types of distribution. P-values. Exact binomial test, t-test, ANOVA. Confidence intervals. Chi-squared and Fisher exact test.
- №6.Regressionsː linear and polynomial.
- №7.Logistic regression.
- №8.Fixed and random effects. Mixed-effects models.
- №9.Bootstrap. Decision trees. Decision forests.
- №10.Distance matrices. Clusterization.
- №11.Dimension reduction, visualisations using MDS, PCA, CA, MCA.
- №12.Bayesian statistics.
- homeworksWritten assignments includes theoretical tests and practical problem-solving. The assignments are published online. The assignments should be submitted via an electronic form.