Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers

- Assumptions about commonly used statistical methods are poorly understood.
- Psychological data are likely to violate these assumptions.
- Consequently, inappropriate statistical methods are often applied.
- The result is poor power and inaccurate confidence intervals and effect sizes.
- A range of robust statistical methods are available that deal effectively with known concerns.

This paper reviews and offers tutorials on robust statistical methods relevant to clinical and experimental psychopathology researchers. We review the assumptions of one of the most commonly applied models in this journal (the general linear model, GLM) and the effects of violating them. We then present evidence that psychological data are more likely than not to violate these assumptions. Next, we overview some methods for correcting for violations of model assumptions. The final part of the paper presents 8 tutorials of robust statistical methods using R that cover a range of variants of the GLM (t-tests, ANOVA, multiple regression, multilevel models, latent growth models). We conclude with recommendations that set the expectations for what methods researchers submitting to the journal should apply and what they should report.