Field & Wilcox (2016) robust estimation 2017.05.08 [revision] (1).pdf (1.34 MB)
Robust statistical methods: a primer for clinical psychology and experimental psychopathology researchers
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.
History
Publication status
- Published
File Version
- Accepted version
Journal
Behaviour Research and TherapyISSN
0005-7967Publisher
ElsevierExternal DOI
Volume
98Page range
19-38Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-05-23First Open Access (FOA) Date
2019-05-27First Compliant Deposit (FCD) Date
2017-05-23Usage metrics
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