| Title: |
Model Estimation Approaches for Fully-Latent Principal Stratification with Small Samples |
| Language: |
English |
| Authors: |
Sooyong Lee (ORCID 0000-0002-7964-4508); Adam Sales (ORCID 0000-0003-0416-0610); Hyeon-Ah Kang (ORCID 0000-0003-4496-6467); Tiffany A. Whittaker (ORCID 0000-0003-1557-0199) |
| Source: |
Structural Equation Modeling: A Multidisciplinary Journal. 2025 32(2):251-263. |
| Availability: |
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: |
Y |
| Page Count: |
13 |
| Publication Date: |
2025 |
| Sponsoring Agency: |
Institute of Education Sciences (ED) |
| Contract Number: |
R305D210036 |
| Document Type: |
Journal Articles; Reports - Research |
| Descriptors: |
Computation; Models; Sample Size; Bayesian Statistics; Statistical Analysis; Randomized Controlled Trials; Robustness (Statistics); Maximum Likelihood Statistics |
| DOI: |
10.1080/10705511.2024.2402331 |
| ISSN: |
1070-5511; 1532-8007 |
| Abstract: |
This study investigated the performance of Bayesian fully-latent principal stratification (FLPS) models in estimating causal and principal effects in small-sample randomized control trials (RCTs) and compared their robustness with three maximum likelihood estimation (MLE)-based models. The impact of prior choices on principal effect estimation in the Bayesian FLPS framework was also explored. Simulation results showed that Bayesian estimation with informative priors consistently outperformed three MLEs and Bayesian estimation with diffuse priors, especially in small samples. The choice of priors played a critical role in estimation accuracy and bias. The study highlights the advantages of Bayesian FLPS with informative priors for RCT research with limited sample sizes and encourages future research to explore complex latent structures, robustness to different measurement models, and guidelines for selecting appropriate priors in the Bayesian FLPS framework. |
| Abstractor: |
As Provided |
| IES Funded: |
Yes |
| Entry Date: |
2026 |
| Accession Number: |
EJ1499739 |
| Database: |
ERIC |