Week 2

Robust Methods

Before Class

Articles

• Fox.J. (2008). Applied Regression Analysis and Generalized Linear Models. SAGE Publications Inc. Chapter 21: Bootstrapping Regression Models.

  • When do you use a bootstrapping procedure? What purpose does it serve?

  • How is a bootstrapped sampling distribution generated?

  • How many bootstrap samples should you use?

  • When should you bootstrap your actual data (“cases”) versus your prediction error (“residuals”)?

• Wilcox, R. R. (1998). How many discoveries have been lost by ignoring modern statistical methods?. American Psychologist, 53, 300—314.

  • What are the statistical and theoretical problems associated with removing outliers?
  • How does skew affect your test?
  • What are the solutions to these problems?

• LaFleur, B. J., & Greevy, R. A. (2009). Introduction to Permutation and Resampling-Based Hypothesis Tests. Journal of Clinical Child & Adolescent Psychology, 38(2), 286–294. https://doi.org/10.1080/15374410902740411

  • How do permutation tests differ from traditional statistical hypothesis tests?

  • What assumptions underlie permutation tests?

  • What steps underlie permutation tests?

  • How does the number of permutations affect the power and type I error rate of a permutation test?

Video

During Class

• Will go through this paper in class

  • Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2021). The Percentile Bootstrap: A Primer With Step-by-Step Instructions in R. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920911881. https://doi.org/10.1177/2515245920911881

After Class

• Kabacoff, R. I. (2022). *R* in Action*. Chapter 12.