View : 992 Download: 0
A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome
- Title
- A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome
- Authors
- Kim, Su-Young; Huh, David; Zhou, Zhengyang; Mun, Eun-Young
- Ewha Authors
- 김수영
- SCOPUS Author ID
- 김수영
- Issue Date
- 2020
- Journal Title
- INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT
- ISSN
- 0165-0254
1464-0651
- Citation
- INTERNATIONAL JOURNAL OF BEHAVIORAL DEVELOPMENT vol. 44, no. 5, pp. 447 - 457
- Keywords
- Latent growth model; Bayesian estimation; diffuse priors; maximum likelihood estimation; binary outcome
- Publisher
- SAGE PUBLICATIONS LTD
- Indexed
- SSCI; SCOPUS
- Document Type
- Article
- Abstract
- Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian estimation with three different priors, and parameter recovery across the two estimators was evaluated to determine their relative performance. The findings suggest that ML estimation is a reasonable choice for most LGMs, unless it fails to converge, which can occur with limiting data situations (i.e., just a few time points, no covariate or outcome, modest sample sizes). When models do not converge using ML, we recommend Bayesian estimation with one caveat that the influence of the priors on estimation may have to be carefully examined, per recent recommendations on Bayesian modeling for applied researchers.
- DOI
- 10.1177/0165025419894730
- Appears in Collections:
- 사회과학대학 > 심리학전공 > Journal papers
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML