Planned Measurement-Missing Designs in Intensive Longitudinal Studies: How Well Do They Recover Power and Parameter Estimates?
Abstract
High participant burden in intensive longitudinal studies (ILS) often reduces data quality and quantity. Planned measurement missing designs (MMD) can alleviate this burden by lowering data collection frequency or duration while maintaining efficiency. This study proposed and evaluated the complete duration MMD (CD-MMD) and the reduced duration MMD (RD-MMD) within the dynamic structural equation modeling framework using Monte Carlo simulations. CD-MMD performed better for long-term cyclical processes, while RD-MMD was more efficient under stationary or short-cycle conditions. Both designs achieved satisfactory power and accuracy when appropriately implemented, offering flexible and practical options for ILS.
Fields of Study & Subjects
Psychometric Methodologies and Testing
Advanced Causal Inference Techniques
Statistical Methods and Bayesian Inference
Publication details
- Journal / source
- Structural Equation Modeling A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Volume
- 33
- Issue
- 4
- Pages
- 531–541
- Publication date
- 19 March 2026
- Language
- EN