PROBABILITY, STATISTICS AND MODELLING II 2018/2019

This is the companion website for the 2018-2019 module for 2nd year undergraduate students of the BSc in Crime Science at UCL.

Resources

The module handbook provides you with all information around assessment, learning outcomes, timetables, and a general overview of the module. Use the module handbook as your go-to guide throughout the module.

Week 1

• Lecture 1: Recap PSM 1 (slides), (pdf)
• Homework 1: Getting ready for R

• Homework 2: R in 12 Steps

• Suggested reading:
  • Dalgaard, P. (2008). Probability and distributions. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 55–65). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_3

No tutorial.

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Week 2

• Tutorial: Refresher of PSM I with R + GLM, Solutions

• Lecture 2: Regression + GLM (slides), (pdf)

• Homework: Regression in R, SOLUTIONS

• Required reading
  • Dalgaard, P. (2008). Regression and correlation. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 109–125). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_6

Week 3

• No tutorial.

• Lecture 3: GLM 2 (slides)

• Homework: Logistic regression in R, SOLUTIONS, SOLUTIONS pdf

• Required reading
  • Dalgaard, P. (2008). Multiple regression. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 185–194). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_11
  • Dalgaard, P. (2008). Linear models. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 195–225). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_12

Week 4

• Tutorial: Advanced GLM + ANOVA, SOLUTIONS

• Lecture 4: GLM for the analysis of variance (slides), (pdf)

• Homework: -

• Required reading
  • Dalgaard, P. (2008). Analysis of variance and the Kruskal–Wallis test. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 127–143). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_7
• Suggested reading
  • Dalgaard, P. (2008). One- and two-sample tests. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 95–107). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_5

Week 5

• Lecture 5: Non-parametric methods + discrete data analysis (slides), (pdf)
• Homework: Nonparametrics analyses and discrete data in R, SOLUTIONS

No tutorial.

• Required reading
  • Loglinear Models for Contingency Tables. (2006). In An Introduction to Categorical Data Analysis (pp. 204–243). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470114759.ch7
  • Dalgaard, P. (2008). Tabular data. In P. Dalgaard (Ed.), Introductory Statistics with R (pp. 145–154). New York, NY: Springer New York. https://doi.org/10.1007/978-0-387-79054-1_8

Week 6

• Lecture: Open Science/Reporting and Assessing Statistical Evidence 1, guest lecture Dr Sandy Schumann [slides on moodle]

• Tutorial: Open Science Lab (slides)

• Required reading:
  • Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
  • Sullivan, G. M., & Feinn, R. (2012). Using Effect Size—or Why the p Value Is Not Enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1
• Recommend reading:
  • Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLOS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124

Week 7

• Lecture: Reporting and Assessing Statistical Evidence 2 (slides), (pdf)

Homework: revision of week 1-5.

• Required reading:
  • Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., … Zwaan, R. A. (2018). Justify your alpha. Nature Human Behaviour, 2(3), 168. https://doi.org/10.1038/s41562-018-0311-x
  • Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., … Johnson, V. E. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6. https://doi.org/10.1038/s41562-017-0189-z
• Recommended reading:
  • Statistical Guidelines - Psychonomic Society, https://www.psychonomic.org/page/statisticalguideline
  • Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251). https://doi.org/10.1126/science.aac4716

Week 8

• Lecture: Bayesian Hypothesis Testing (slides), (pdf)
• Tutorial: Bayesian hypothesis testing in R and JASP, SOLUTIONS

Required reading:

• Ortega, A., & Navarrete, G. (2017). Bayesian Hypothesis Testing: An Alternative to Null Hypothesis Significance Testing (NHST) in Psychology and Social Sciences. Bayesian Inference. https://doi.org/10.5772/intechopen.70230
• Faulkenberry, T. J. (2018). A Simple Method for Teaching Bayesian Hypothesis Testing in the Brain and Behavioral Sciences. Journal of Undergraduate Neuroscience Education, 16(2), A126–A130. https://www.ncbi.nlm.nih.gov/pubmed/30057494
• Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. https://doi.org/10.3758/PBR.16.2.225

Recommended reading:

• Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method. Cognitive Psychology, 60(3), 158–189. https://doi.org/10.1016/j.cogpsych.2009.12.001
• Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E.-J. (2011). Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests. Perspectives on Psychological Science, 6(3), 291–298. https://doi.org/10.1177/1745691611406923

Week 9

• Lecture: Recap + Q&A (slides), (pdf)
• No homework (prepare for the exam).
• No tutorial.

Week 10

CLASS TEST

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Module convenor and author: Bennett Kleinberg (bennett.kleinberg@ucl.ac.uk)

Department of Security and Crime Science, UCL

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