Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no ‘best method’ in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.

中文翻译：

---

应用稳健方法概述

社会科学中的数据通常是非正态分布的，并以重尾为特征。然而，社会科学中最广泛使用的方法仍然是基于样本均值和样本协方差的分析。虽然这些传统方法继续用于解决新的实质性问题，但得出的结论可能不准确或具有误导性。尽管在实践中没有“最佳方法”，但考虑数据分布的稳健方法的性能明显优于传统方法。本文概述了稳健的程序，强调了一些已反复证明适用于广泛用于社会和行为科学的模型的方法。真实数据示例展示了当回归模型包含分类协变量和乘积项时，如何将稳健方法用于潜在变量模型和调节中介分析。结果和逻辑分析表明，在进行重复研究时，稳健的方法会产生更有效的参数估计、更可靠的模型评估、更可靠的模型/数据诊断以及更可靠的结论。R 和 SAS 程序用于推荐的稳健方法的常规应用。