Holtmann Jana, Koch Tobias, Bohn Johannes, Eid Michael
Department of Education and Psychology, Free University Berlin, Germany.
Center of Methods, Leuphana University Lüneburg, Germany.
Br J Math Stat Psychol. 2017 Feb;70(1):42-80. doi: 10.1111/bmsp.12081. Epub 2017 Jan 24.
A new multilevel latent state graded response model for longitudinal multitrait-multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between- and within-level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well-being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high-school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.
提出了一种新的多水平潜在状态分级反应模型，用于纵向多特质-多方法（MTMM）测量设计，该设计结合了结构不同且可互换的方法。该模型使研究人员能够随时间检验结构效度，并基于有序反应变量研究结构和方法效应的变化与稳定性。我们展示了贝叶斯估计技术如何解决纵向多水平MTMM研究中通常出现的一些重要问题，并促进所提出模型的估计。在一项蒙特卡罗模拟研究中，研究了估计准确性以及组间和组内样本量以及不同先验规范对参数恢复的影响。研究结果表明，在所提出模型的参数估计中，在收敛效度较低的情况下，使用贝叶斯估计方法，当每个聚类中只有250个聚类和超过两个观测值时，也能准确估计。该模型应用于纵向MTMM研究的幸福感数据，评估高中毕业后年轻人生活满意度和主观幸福感的变化与稳定性。提供了实证应用指南，并讨论了贝叶斯方法估计纵向多水平MTMM模型的优缺点。
