Abstract Invariance of surveys across different groups means that the respondents interpret the items in the same way, as reflected in similar factor loadings, for example. Invariance can be assessed using various statistical procedures, such as Multi-Group Confirmatory Factor Analysis. However, these analyses require access to raw data. Here, we introduce a meta-analytic method that requires only the factor correlation matrices of samples as input. It compares the structures of intercorrelations of factors by correlating these values across two samples, yielding a value of overall similarity for how the factors intercorrelate in different samples. This method was tested in three different ways. We conclude that the method yields useful results and can assess invariance when raw data are not available.

中文翻译：

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跨样本不变性的元分析分析：介绍一种不需要原始数据的方法

摘要 不同组间调查的不变性意味着受访者以相同的方式解释项目，例如反映在相似的因子载荷中。可以使用各种统计程序评估不变性，例如多组验证性因子分析。但是，这些分析需要访问原始数据。在这里，我们引入了一种元分析方法，它只需要样本的因子相关矩阵作为输入。它通过将两个样本中的这些值相关联来比较因子的相关结构，从而得出不同样本中因子如何相互关联的整体相似性值。该方法以三种不同的方式进行了测试。我们得出结论，该方法产生有用的结果，并且可以在原始数据不可用时评估不变性。