Field, Andy P and Wilcox, Rand R (2017) Robust statistical methods: a primer for clinical psychology and experimental psychopathology researchers. Behaviour Research and Therapy, 98. pp. 19-38. ISSN 0005-7967

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Abstract

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.

Item Type:	Article
Keywords:	Keywords Robust statistical methods, assumptions, bias
Schools and Departments:	School of Psychology > Psychology
Depositing User:	Ellena Adams
Date Deposited:	23 May 2017 11:54
Last Modified:	01 Jul 2019 14:30
URI:	http://sro.sussex.ac.uk/id/eprint/68206

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