School of Psychology, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
Department of Psychology, University of Southern California, 618 Seeley Mudd Building, University Park Campus, Los Angeles, CA 90089-1061, USA.
Behav Res Ther. 2017 Nov;98:19-38. doi: 10.1016/j.brat.2017.05.013. Epub 2017 May 26.
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.
本文回顾并提供了与临床和实验精神病理学研究人员相关的稳健统计学方法的教程。我们回顾了本期刊中最常应用的模型之一（一般线性模型，GLM）的假设以及违反这些假设的影响。然后，我们提出证据表明心理数据很可能违反这些假设。接下来，我们概述了一些纠正模型假设违反的方法。本文的最后一部分介绍了使用 R 进行的 8 个稳健统计方法教程，涵盖了 GLM 的多种变体（t 检验、方差分析、多元回归、多层模型、潜在增长模型）。最后，我们提出了一些建议，为提交给该期刊的研究人员应该应用哪些方法以及应该报告哪些内容设定了期望。
