Cleland C M, Rothschild L, Haslam N
University of Medicine and Dentistry of New Jersey, USA.
Psychol Rep. 2000 Aug;87(1):37-47. doi: 10.2466/pr0.2000.87.1.37.
A Monte Carlo evaluation of four procedures for detecting taxonicity was conducted using artificial data sets that were either taxonic or nontaxonic. The data sets were analyzed using two of Meehl's taxometric procedures, MAXCOV and MAMBAC, Ward's method for cluster analysis in concert with the cubic clustering criterion and a latent variable mixture modeling technique. Performance of the taxometric procedures and latent variable mixture modeling were clearly superior to that of cluster analysis in detecting taxonicity. Applied researchers are urged to select from the better procedures and to perform consistency tests.
使用分类或非分类的人工数据集对四种检测分类性的程序进行了蒙特卡洛评估。这些数据集使用了米尔的两种分类分析程序MAXCOV和MAMBAC、与立方聚类标准协同使用的沃德聚类分析方法以及一种潜在变量混合建模技术进行分析。在检测分类性方面，分类分析程序和潜在变量混合建模的性能明显优于聚类分析。敦促应用研究人员从更好的程序中进行选择并进行一致性测试。
