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

ABSTRACT: 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 s

SUBMITTER: Wang C 

PROVIDER: S-EPMC12916115 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature