从分布形状､趋中度以及离散程度来看,反应时都具有区别于其他数据类型的非常鲜明的特点。 因此,反应时数据的统计分析处理往往也有不同的技术要求和门槛。基于R 语言的混合效应模型为反应时分布上的正偏斜､各数据点之间强关联以及异常值等问题提供了很好的解决方案。本文在回顾传统的反应时数据分析方法后,以一项具体的研究为实例介绍了使用“混合效应模型”来拟合反应时数据的基本原理､概念内涵以及如何拟合最佳模型等问题｡

Compared with other types of data, reaction time data are distinctive in that they show unique patterns of distribution, central tendency, and variability. Hence, the statistical analysis of these data calls for special techniques and knowledge. We believe that the mixed-effects model using R provides a good solution to the common problems found in RT data analysis, such as positive skewness, strong temporal auto-dependency, and extreme outliers. This article first reviews traditional methods used for the statistical analyses of RT data. Then, using examples from real studies, it introduces some useful principles and practices for employing the mixed-effects model to fit the data and obtain the model of best fit.