Erceg-Hurn David M, Mirosevich Vikki M
School of Psychology, University of Western Australia, Crawley, Western Australia, Australia.
Am Psychol. 2008 Oct;63(7):591-601. doi: 10.1037/0003-066X.63.7.591.
Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices.
经典的参数统计显著性检验，如方差分析和最小二乘回归，被包括心理学在内的许多学科的研究人员广泛使用。为了使经典参数检验产生准确的结果，其背后的假设（如正态性和同方差性）必须得到满足。在分析实际数据时，这些假设很少能得到满足。使用违反假设的经典参数方法可能会导致p值、效应量和置信区间的计算不准确。这可能会在数据解释中导致实质性错误。许多现代稳健统计方法减轻了使用违反假设的参数方法所固有的问题，但研究人员很少使用现代方法。作者研究了为什么会出现这种情况，认为大多数研究人员没有意识到经典方法的严重局限性，并且不熟悉现代替代方法。介绍了一系列适用于分析各种设计的现代稳健和基于秩的显著性检验。提供了使用SPSS、SAS和R等软件进行现代分析的实用建议。作者最后讨论了稳健效应量指标。
