Department of Economics, BI Norwegian Business School, 0484, Oslo, Norway.
Psychometrika. 2019 Jun;84(2):554-561. doi: 10.1007/s11336-019-09663-8. Epub 2019 Mar 5.
Previous influential simulation studies investigate the effect of underlying non-normality in ordinal data using the Vale-Maurelli (VM) simulation method. We show that discretized data stemming from the VM method with a prescribed target covariance matrix are usually numerically equal to data stemming from discretizing a multivariate normal vector. This normal vector has, however, a different covariance matrix than the target. It follows that these simulation studies have in fact studied data stemming from normal data with a possibly misspecified covariance structure. This observation affects the interpretation of previous simulation studies.
先前有影响力的仿真研究使用 Vale-Maurelli（VM）仿真方法研究了有序数据中潜在的非正态性的影响。我们表明，源自规定目标协方差矩阵的 VM 方法的离散数据通常在数值上等于源自离散化多元正态向量的数据。然而，这个正态向量的协方差矩阵与目标不同。因此，这些仿真研究实际上研究了源自具有可能指定不当协方差结构的正态数据的数据。这一观察结果影响了先前仿真研究的解释。
