Estimating test reliability of intensive longitudinal studies: Perspectives on multilevel structure and dynamic nature

Abstract: With the widespread use of intensive longitudinal studies in psychology and other social sciences, reliability estimation of tests in intensive longitudinal studies has received increasing attention. Earlier reliability estimation methods drawn from cross-sectional studies or based on generalizability theory have many limitations and are not applicable to intensive longitudinal studies. Considering the two main characteristics of intensive longitudinal data, multilevel structure and dynamic nature, the reliability of tests in intensive longitudinal studies can be estimated based on multilevel confirmatory factor analysis, dynamic factor analysis, and dynamic structural equation models. The main features and applicable contexts of these three reliability estimation methods are demonstrated with empirical data. Future research could explore the reliability estimation methods based on other models, and should also pay more attention to the testing and reporting of test reliability in intensive longitudinal studies.