Widaman Keith F, Grimm Kevin J, Early Dawnté R, Robins Richard W, Conger Rand D
University of California, Davis.
Struct Equ Modeling. 2013 Jul 1;20(3):384-408. doi: 10.1080/10705511.2013.797819.
Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.
如果特定的显变量在某些组中完全缺失，那么在因子不变性的多组评估中就会出现困难。在这种情况下，可以使用特殊的分析方法（例如，删除显变量），但一些常见的方法，如多重填补，是不可行的。这个问题至少有3种可行的解决方案：分析不同组的变量集、使用模式混合方法以及一种使用随机数生成的新方法。本文提出的后一种解决方案是，为给定样本中完全缺失的显变量的所有观测值生成伪随机正态偏差，然后以尊重这些值的随机性质的方式指定多组模型。详细给出了一个实证例子，比较了这3种方法。所提出的解决方案可以使用要求每组中显变量数量相同的程序，在潜变量水平上对组间进行定量比较。
