Repeated measure data design has been used extensively in a wide range of fields, such as brain aging or developmental psychology, to answer important research questions exploring relationships between trajectory of change and external variables. In many cases, such data may be collected from multiple study cohorts and harmonized, with the intention of gaining higher statistical power and enhanced external validity. When psychological constructs are measured using survey scales, a fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across studies. Traditional analysis may fit a unidimensional item response theory model to data from one time point and one cohort to obtain item parameters and fix the same parameters in subsequent analyses. Such a simplified approach ignores item residual dependencies in the repeated measure design on one hand, and on the other hand, it does not exploit accumulated information from different cohorts. Instead, two alternative approaches should serve such data designs much better: an integrative approach using multiple-group two-tier model via concurrent calibration, and if such calibration fails to converge, a Bayesian sequential calibration approach that uses informative priors on common items to establish the scale. Both approaches use a Markov chain Monte Carlo algorithm that handles computational complexity well. Through a simulation study and an empirical study using Alzheimer's diseases neuroimage initiative cognitive battery data (i.e., language and executive functioning), we conclude that latent change scores obtained from these two alternative approaches are more precisely recovered. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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使用贝叶斯项目响应理论进行多队列重复测量设计来估计个体潜在变化分数。


重复测量数据设计已广泛应用于脑衰老或发展心理学等广泛领域，以回答探索变化轨迹与外部变量之间关系的重要研究问题。在许多情况下，此类数据可以从多个研究队列中收集并进行协调，目的是获得更高的统计功效和增强的外部有效性。当使用调查量表测量心理结构时，数据协调的一个基本心理测量挑战是为跨研究的兴趣结构创建相应的测量方法。传统分析可能会将一维项目反应理论模型与一个时间点和一个队列的数据进行拟合，以获得项目参数并在后续分析中固定相同的参数。这种简化的方法一方面忽略了重复测量设计中的项目残留依赖性，另一方面，它没有利用来自不同群体的累积信息。相反，两种替代方法应该更好地服务于此类数据设计：一种通过并发校准使用多组两层模型的综合方法，如果这种校准无法收敛，则使用贝叶斯顺序校准方法，该方法使用常见项目的信息先验来建立规模。两种方法都使用马尔可夫链蒙特卡罗算法，可以很好地处理计算复杂性。通过模拟研究和使用阿尔茨海默病神经图像主动认知电池数据（即语言和执行功能）的实证研究，我们得出结论，从这两种替代方法获得的潜在变化分数可以更精确地恢复。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。