Department of Methodology and Statistics, 7899Tilburg University, The Netherlands.
Erasmus School of Social and Behavioural Sciences; Department of Psychology, Education & Child Studies/Clinical Child and Family Studies, Erasmus University Rotterdam, The Netherlands.
Eval Health Prof. 2021 Mar;44(1):61-76. doi: 10.1177/0163278720976762. Epub 2020 Dec 11.
Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present "latent Markov latent trait analysis" (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents' affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.
从密集的纵向数据中推断心理结构的动态需要测量模型（MM）——表明项目与结构的关系——在主体和时间点上保持不变。然而，当在日常生活中评估主体时，可能存在多个 MM，例如，因为主体在项目解释上存在差异，或者（一些）主体的反应方式随时间而变化。最近提出的“潜在马尔可夫因子分析”（LMFA）通过根据这些观察背后的 MM 将观察结果分类到潜在的“状态”来评估（违反）测量不变性，从而在状态之间存在 MM 差异，但在一个状态内保持不变。然而，LMFA 仅限于正态分布的连续数据，并且当将该方法应用于具有偏斜响应或较少响应类别的有序数据（例如来自李克特项目）时，估计可能不准确。为了使具有有序数据的研究人员和健康专业人员能够评估测量不变性，我们提出了“潜在马尔可夫潜在特质分析”（LMLTA），它基于 LMFA，但将响应视为有序。我们的应用表明，在不同的社会环境中，青少年情感幸福感的 MM 存在差异，突出了研究测量不变性对于为心理科学和实践做出准确推断以及进一步理解心理结构动态的重要性。
