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A Comprehensive Model Framework for Between-Individual Differences in Longitudinal Data
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DOI：10.1037/met0000585.png)
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摘要
En 中文
Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level, our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level, our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed. Recommendations for selecting and specifying longitudinal models are made for empirical researchers who aim to account for between-individual differences. In different types of research, scores on the same variable are repeatedly assessed among different individuals. These so-called longitudinal data can be analyzed using a multitude of longitudinal models. These models differ in three main attributes: whether and how they account for trends, such as growth and decline, whether and how they account for the dynamic interplay between variables over time, and whether and how they account for differences between individuals. Because there are many different ways to account for these attributes, we propose a unified framework in which we structure and illustrate a number of prominent longitudinal models. With the framework, we highlight how different elements of the framework can be used to describe a diverse set of longitudinal data and how these elements can be interpreted. We also show how longitudinal models can account for between-individual differences. In summary, the unified framework for between-individual differences in longitudinal models guides applications of such models, supports informed model choice, and aids the interpretation of longitudinal models.
Keyword:
interindividual differences
intraindividual differences
random effects
mixture models
multilevel mixture models
intensive longitudinal data
