Psychological research often focuses on examining group differences in a set of numeric variables for which normality is doubtful. Longitudinal studies enable the investigation of developmental trends. For instance, a recent study (Voormolen et al (2020), https: //doi.org/10.3390/jcm9051525) examined the relation of complicated and uncomplicated mild traumatic brain injury (mTBI) with multidimensional outcomes measured at three- and six-months after mTBI. The data were analyzed using robust repeated measures multivariate analysis of variance (MANOVA), resulting in significant differences between groups and across time points, then followed up by univariate ANOVAs per variable as is typically done. However, this approach ignores the multivariate aspect of the original analyses. We propose descriptive discriminant analysis (DDA) as an alternative, which is a robust multivariate technique recommended for examining significant MANOVA results and has not yet been applied to multivariate repeated measures data. We provide a tutorial with annotated R code demonstrating its application to these empirical data.

翻译：心理学研究常关注一组数值变量上的组间差异，而此类数据的正态性往往存疑。纵向研究能够揭示发展变化趋势。例如，近期一项研究（Voormolen等，2020，https://doi.org/10.3390/jcm9051525）探讨了复杂性与非复杂性轻度创伤性脑损伤（mTBI）与伤后三个月及六个月时多维结局指标的关系。该研究采用稳健重复测量多变量方差分析（MANOVA）对数据进行分析，发现组间及时间点间存在显著差异，随后按常规方法对各变量进行单变量方差分析（ANOVA）作为后续检验。然而，这种处理方法忽略了原始分析的多变量特性。我们提出描述性判别分析（DDA）作为替代方案——该多变量稳健技术适用于检验显著的MANOVA结果，且尚未被应用于多变量重复测量数据。我们提供附有注释R代码的教程，演示该方法在实证数据中的应用。