To supplement the lecture material, we will assign selected readings and indicate recommended reference works for further reading. Focus is on statistical literacy and conceptual understanding supplemented by computer labs to put things into practice. Specifics readings pre and post lecture will be assigned through the UiO - CANVAS learning platform to facilitate class discussion.
Most of the texts on the reading list are available electronically through the Univeristy of Oslo Library. You have to be logged on to the UiO network in order to access them. Information about how to gain access from home can be found on this webpage.
The books that are listed are available in the library.
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, tons of learning material is available online, going from reference manuals to entire MOOCS or learning platforms. We will also be organizing 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.
Grolemund, G., & Wickham, H. (2017). R for Data Science.
Reference work: Wickham, H. (2014). Advanced R.
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.
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 201218772.
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.
Stevens, S. S. (1946). On the Theory of Scales of Measurement. Science, 103(2684), 677–680.
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.
Tukey, J. W. (1980). We Need Both Exploratory and Confirmatory. The American Statistician, 34(1), 23–25.
Wickham, H., Cook, D., Hofmann, H., & Buja, A. (2010). Graphical inference for Infovis. IEEE Transactions on Visualization and Computer Graphics, 16(6), 973–979.
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.
Design
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.
Gelman, A., & Carlin, J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspectives on Psychological Science, 9(6), 641–651.
Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation. Annual Review of Psychology, 59(1), 537–563.
[Book] Shadish, W., Cook, T., & Campbel, D. (2002). Randomized experiments: Rationale, design, conditions conducive to doing them. In Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.
Questionable Research Practices
Duncan, G. J., Engel, M., Claessens, A., & Dowsett, C. J. (2014). Replication and robustness in developmental research. Developmental Psychology, 50(11), 2417–2425.
Fiedler, K., & Schwarz, N. (2016). Questionable Research Practices Revisited. Social Psychological and Personality Science, 7(1), 45–52.
Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLOS Medicine, 2(8), e124.
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science, 23(5), 524–532.
Nuijten, M. B., Hartgerink, C. H. J., Assen, M. A. L. M. van, Epskamp, S., & Wicherts, J. M. (2016). The prevalence of statistical reporting errors in psychology (1985–2013). Behavior Research Methods, 48(4), 1205–1226.
Peterson, D. (2016). The Baby Factory: Difficult Research Objects, Disciplinary Standards, and the Production of Statistical Significance. Socius, 2, 2378023115625071.
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.