Appendix A — Next-step resources
In the hope that this textbook has inspired you to dive deeper into the wonderful world of quantitative linguistics, data analysis, statistics, data visualisation, and coding in R, here is a (work-in-progress) curated list of further resources to continue your learning journey! 🚀✨
As with the rest of this textbook (see ?sec-Preface), this list is very much work in progress. Full bibliographic references will be added in due course. Do drop me a line to let me know about any great resources that I have missed!
A.1 Recommended resources specific to the language sciences (in alphabetical order)
Brezina, Vaclav. 2018. Statistics in Corpus Linguistics: A Practical Guide. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316410899.
Desagulier, Guillaume. 2017. Corpus Linguistics and Statistics with R: Introduction to Quantitative Methods in Linguistics (Quantitative Methods in the Humanities and Social Sciences). Cham: Springer International Publishing.
Gries, Stefan Thomas. 2021. Statistics for linguistics with R: a practical introduction (De Gruyter Mouton Textbook). 3rd revised edition. Berlin Boston: de Gruyter Mouton.
LADAL contributors. Tutorials of the Language Technology and Data Analysis Laboratory. https://ladal.edu.au/tutorials.html
Open Educational Resource.Levshina, Natalia. 2015. How to do linguistics with R: Data exploration and statistical analysis. Amsterdam: John Benjamins.
Schneider, Dr Gerold & Max Lauber. 2020. Statistics for Linguists. https://dlf.uzh.ch/openbooks/statisticsforlinguists/
Open Educational Resource.Speelman, Dirk, Kris Heylen & Dirk Geeraerts (eds.). 2018. Mixed-Effects Regression Models in Linguistics (Quantitative Methods in the Humanities and Social Sciences). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-69830-4.
Winter, Bodo. 2019. Statistics for Linguists: An Introduction Using R. New York: Routledge. https://doi.org/10.4324/9781315165547.
A.2 Further Open Educational Resources (currently in no particular order)
- Mine Çetinkaya-Rundel and Johanna Hardin: Introduction to Modern Statistics (2024), Second Edition. https://openintro-ims.netlify.app/
- Guide to Effect Sizes and Confidence Intervals: https://matthewbjane.quarto.pub/guide-to-effect-sizes-and-confidence-intervals/
- Happy Git and GitHub for the useR: https://happygitwithr.com/
- Quarto & reproducibility: https://ucsbcarpentry.github.io/Reproducible-Publications-with-RStudio-Quarto/index.html
- Modern Data Visualization with R: https://rkabacoff.github.io/datavis
- Building reproducible analytical pipelines with R: https://raps-with-r.dev/
- Modern Plain Text Computing: https://mptc.io/content/01-content.html
- https://www.data-to-viz.com/
- Interpreting data visualisation: https://pressbooks.library.torontomu.ca/criticaldataliteracy/
- Improve your statistical inferences: https://lakens.github.io/statistical_inferences/
- What they forgot to teach you about R: https://rstats.wtf/
- Introduction to Data Science: https://florian-huber.github.io/data_science_course/book/cover.html
- Data Science in Education Using R: https://datascienceineducation.com/
- Models Demystified: A Practical Guide from t-tests to Deep Learning https://m-clark.github.io/book-of-models/
- Data Visualization in R https://datavizf23.classes.andrewheiss.com/
- R for Data Science https://r4ds.hadley.nz/intro
- Dauber, Daniel. 2024. R for Non-Programmers: A Guide for Social Scientists. https://bookdown.org/daniel_dauber_io/r4np_book/.
- Bayes Rules! An Introduction to Applied Bayesian Modeling https://www.bayesrulesbook.com/
- Fundamentals of Data Visualization by Claus O. Wilke https://clauswilke.com/dataviz/
- Learning statistics with R https://learningstatisticswithr.com
- Quarto for scientists https://qmd4sci.njtierney.com/
- The Version Control Book: Track, organize and share your work: An introduction to Git for research https://lennartwittkuhn.com/version-control-book/
- Intermediate Statistics with R https://greenwood-stat.github.io/GreenwoodBookHTML/