Institute of Positive Psychology and Education, Australian Catholic University.
Hector Research Institute of Education Sciences and Psychology, University of Tübingen.
Psychol Methods. 2018 Sep;23(3):524-545. doi: 10.1037/met0000113. Epub 2017 Jan 12.
Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance (CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data. (PsycINFO Database Record
标量不变性是一种无法实现的理想，实际上只能近似实现；通常使用潜在有问题的方法，例如基于参数估计的逐步选择和具有较大修正指数的部分不变性。研究 1 展示了对齐方法在大规模研究中比较潜在因子均值的能力和灵活性的扩展（30 个经合组织国家，8 个因子，44 个项目，N = 249,840），在传统的测量不变性验证性因素分析（CFA-MI）方法中，通常不支持标量不变性。重要的是，我们引入了一种在 CFA 内对齐（AwC）的方法，将对齐从主要的探索性工具转变为验证性工具，并能够进行以前无法使用对齐（检验独特性和因子方差/协方差的不变性、多组模拟模型、潜在均值的对比）和结构方程模型的分析。具体来说，它还允许在 30 个国家的模拟 AwC（即，具有性别作为协变量的 SEM）和 60 个组的 AwC CFA（即，30 个国家×2 个性别）分析中比较性别差异。研究 2 是对研究 1 中提出的问题进行的一项模拟研究，结果表明，与标量 CFA-MI 相比，特别是与基于广受批评的逐步选择策略的部分不变性标量模型相比，对齐更准确地估计了潜在均值。总之，通过 AwC 增强的对齐为来自不同学科的应用研究人员提供了相当大的灵活性，以解决传统 CFA-MI 标量模型不适用于数据时的实质性重要问题。
