R resources
We will be using the open-source free statistical software environment R in this course, and throughout the master programme. Due to its popularity, a large amount of learning material is available online, from reference manuals to entire MOOCS or learning platforms. We will also organize training sessions at the start of the course and computer practicals during the course.
R Core Team. (2018). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Textbook for the R component
Boehmke, B. (2016). Data wrangling in R. Use R! series, Springer. doi: 10.1007/978-3-319-45599-0
Selected readings
Data Science & Data Management
Breiman, L. (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231.
Donoho, D. (2017). 50 Years of Data Science. Journal of Computational and Graphical Statistics, 26(4), 745–766.
Ioannidis, J. P. A., Fanelli, D., Dunne, D. D., & Goodman, S. N. (2015). Meta-research: Evaluation and Improvement of Research Methods and Practices. PLOS Biology, 13(10), e1002264.
Meyer, M. N. (2018). Practical Tips for Ethical Data Sharing. Advances in Methods and Practices in Psychological Science, 2515245917747656.
Wickham, H. (2014). Tidy Data. Journal of Statistical Software, Articles, 59(10), 1–23.
Probability & Randomness
[Book] Gage, J., & Spiegelhalter, D. (2016). Teaching probability. Cambridge University Press.
Descriptive Statistics
DeCarlo, L. T. (1997). On the meaning and use of kurtosis. Psychological Methods, 2(3), 292–307.
Leemis, L. M., & McQueston, J. T. (2008). Univariate Distribution Relationships. The American Statistician, 62(1), 45–53.
Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105(1), 156–166.
Velleman, P. F., & Wilkinson, L. (1993). Nominal, Ordinal, Interval, and Ratio Typologies Are Misleading. The American Statistician, 47(1), 65–72.
Representations
Lane, D. M., & Sándor, A. (2009). Designing better graphs by including distributional information and integrating words, numbers, and images. Psychological Methods, 14(3), 239–257.
[Book] Tufte, R. (2001). The Visual Display of Quantitative Information (2nd edition edition). Cheshire, Conn: Graphics Press.
Inference
[Book] Dienes, Z. (2008). Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. Macmillan International Higher Education.
Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36–48.
Model
[Book] Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge.
Friedman, L., & Wall, M. (2005). Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression. The American Statistician, 59(2), 127–136.
Easy Reading
[ Not always as precise, but written in an accessible and “fun” fashion.]
[Book] Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage.
Grolemund, G., & Wickham, H. (2017). R for Data Science. Retrieved from http://r4ds.had.co.nz/
Further Reading
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning: with Applications in R. New York: Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2nd ed.). New York: Springer-Verlag.
Wickham, H. (2014). Advanced R. Retrieved from http://adv-r.had.co.nz/