Jak Suzanne, Jorgensen Terrence D
Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, Netherlands.
Front Psychol. 2017 Oct 10;8:1640. doi: 10.3389/fpsyg.2017.01640. eCollection 2017.
Data often have a nested, multilevel structure, for example when data are collected from children in classrooms. This kind of data complicate the evaluation of reliability and measurement invariance, because several properties can be evaluated at both the individual level and the cluster level, as well as across levels. For example, cross-level invariance implies equal factor loadings across levels, which is needed to give latent variables at the two levels a similar interpretation. Reliability at a specific level refers to the ratio of true score variance over total variance at that level. This paper aims to shine light on the relation between reliability, cross-level invariance, and strong factorial invariance across clusters in multilevel data. Specifically, we will illustrate how strong factorial invariance across clusters implies cross-level invariance and perfect reliability at the between level in multilevel factor models.
数据通常具有嵌套的多层次结构，例如从教室里的儿童那里收集数据时。这类数据使可靠性评估和测量不变性变得复杂，因为可以在个体层面、聚类层面以及跨层面评估多种属性。例如，跨层面不变性意味着各层面的因子载荷相等，这是赋予两个层面的潜在变量相似解释所必需的。特定层面的可靠性是指该层面真分数方差与总方差的比率。本文旨在阐明多层次数据中聚类间的可靠性、跨层面不变性和强因子不变性之间的关系。具体而言，我们将说明聚类间的强因子不变性如何意味着多层次因子模型中层间的跨层面不变性和完美可靠性。
