Mixture multilevel vector-autoregressive modeling.
Psychol Methods
; 29(1): 137-154, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37561488
ABSTRACT
With the rising popularity of intensive longitudinal research, the modeling techniques for such data are increasingly focused on individual differences. Here we present mixture multilevel vector-autoregressive modeling, which extends multilevel vector-autoregressive modeling by including a mixture, to identify individuals with similar traits and dynamic processes. This exploratory model identifies mixture components, where each component refers to individuals with similarities in means (expressing traits), autoregressions, and cross-regressions (expressing dynamics), while allowing for some interindividual differences in these attributes. Key issues in modeling are discussed, where the issue of centering predictors is examined in a small simulation study. The proposed model is validated in a simulation study and used to analyze the affective data from the COGITO study. These data consist of samples for two different age groups of over 100 individuals each who were measured for about 100 days. We demonstrate the advantage of exploratory identifying mixture components by analyzing these heterogeneous samples jointly. The model identifies three distinct components, and we provide an interpretation for each component motivated by developmental psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Individualidade
Tipo de estudo:
Prognostic_studies
Limite:
Humans
/
Infant
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article