Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation good enough principle, based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and good enough approach. Our results show the approach has potential in combining educational data.

复杂的研究问题往往需要大样本才能获得准确的参数估计和足够的功效。将现存的数据集组合成一个大的、合并的数据集是一种无需消耗资源即可实现的方法。测量不变性 (MI) 建模是一种既定的方法，可确保参与者的分数在同一尺度上。通过 MI 合并独立的数据集时有两个主要问题。首先，样本量通常很大，导致小差异变得不可变。其次，并非所有数据集都可能包含相同的措施组合。在本文中，我们提出了一种可以解决这两个问题并且用户友好的方法。足够好原则，基于组间差异不为零而是很小的假设。我们通过跨八个独立数据集检查 MI 来演示该方法，并比较传统方法和足够好方法的 MI 决策。我们的结果表明该方法具有结合教育数据的潜力。