Conditional median-based Bayesian growth mixture modeling for nonnormal data.
Behav Res Methods
; 54(3): 1291-1305, 2022 06.
Article
en En
| MEDLINE
| ID: mdl-34590287
ABSTRACT
Growth mixture modeling is a common tool for longitudinal data analysis. One of the key assumptions of traditional growth mixture modeling is that repeated measures within each class are normally distributed. When this normality assumption is violated, traditional growth mixture modeling may provide misleading model estimation results and suffer from nonconvergence. In this article, we propose a robust approach to growth mixture modeling based on conditional medians and use Bayesian methods for model estimation and inferences. A simulation study is conducted to evaluate the performance of this approach. It is found that the new approach has a higher convergence rate and less biased parameter estimation than the traditional growth mixture modeling approach when data are skewed or have outliers. An empirical data analysis is also provided to illustrate how the proposed method can be applied in practice.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Modelos Estadísticos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Behav Res Methods
Asunto de la revista:
CIENCIAS DO COMPORTAMENTO
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos