关于肘部图方法在因子混合模型中进行类别枚举的应用
On the Use of Elbow Plot Method for Class Enumeration in Factor Mixture Models.
作者信息
Sen Sedat, Cohen Allan S
机构信息
Faculty of Education, Harran University, Türkiye.
Educational Psychology Department, University of Georgia, GA, USA.
出版信息
Appl Psychol Meas. 2025 May 20:01466216251344288. doi: 10.1177/01466216251344288.
Application of factor mixture models (FMMs) requires determining the correct number of latent classes. A number of studies have examined the performance of several information criterion (IC) indices, but as yet none have studied the effectiveness of the elbow plot method. In this study, therefore, the effectiveness of the elbow plot method was compared with the lowest value criterion and the difference method calculated from five commonly used IC indices. Results of a simulation study showed the elbow plot method to detect the generating model at least 90% of the time for two- and three-class FMMs. Results also showed the elbow plot method did not perform well for two-factor and four-class conditions. The performance of the elbow plot method was generally better than that of the lowest IC value criterion and difference method under two- and three-class conditions. For the four-latent class conditions, there were no meaningful differences between the results of the elbow plot method and the lowest value criterion method. On the other hand, the difference method outperformed the other two methods in conditions with two factors and four classes.
因子混合模型（FMMs）的应用需要确定潜在类别的正确数量。许多研究考察了几种信息准则（IC）指标的性能，但尚未有研究探讨折线图方法的有效性。因此，在本研究中，将折线图方法的有效性与最低值准则以及根据五个常用IC指标计算的差异方法进行了比较。一项模拟研究的结果表明，对于两类和三类FMMs，折线图方法在至少90%的情况下能够检测出生成模型。结果还表明，折线图方法在双因子和四类条件下表现不佳。在两类和三类条件下，折线图方法的性能总体上优于最低IC值准则和差异方法。对于四个潜在类别条件，折线图方法和最低值准则方法的结果之间没有显著差异。另一方面，在双因子和四类条件下，差异方法的表现优于其他两种方法。
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