Using Bayesian item response theory for multicohort repeated measure design to estimate individual latent change scores.

项目反应理论 统计能力 贝叶斯概率 计算机科学 心理信息 马尔科夫蒙特卡洛 潜变量 潜变量模型 数据挖掘 计量经济学 心理学 统计 机器学习 心理测量学 人工智能 数学 政治学 法学 梅德林
作者
Chun Wang,Ruoyi Zhu,Paul K. Crane,Seo‐Eun Choi,Richard N. Jones,Douglas Tommet
出处
期刊:Psychological Methods [American Psychological Association]
标识
DOI:10.1037/met0000635
摘要

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).
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