Linear Mixed-Effects and Latent Curve Models for Longitudinal Life Course Analyses

Abstract

The core of life course or lifespan research consists in studying how individual trajectories are shaped and unfold over time, from conception to death (Baltes 1987). Two concepts are fundamental in this endeavor: stability and change. Indeed, while certain individual characteristics remain constant across one’s lifespan (e.g., sex, ethnicity), others undergo profound change, to the point that they might mutate into other characteristics (e.g., health, cognitive capacities). Such changes need not be independent of each other. They may simply co-occur, in the sense that while they happen simultaneously, it is hard to infer causality mechanisms between them. They may also be intrinsically related, where one change process is a necessary antecedent of the other, which becomes the consequence. The empirical study of lifespan development necessitates hence statistical models capable, at the very least, of (a) estimating constancy and change in informa- tion from a collection of observed measurements and (b) assessing degrees of interrelationships among constant and changing characteristics. Moreover, in a contextual paradigm typical of life course research, it is also highly desirable to ascertain which characteristics are intrinsic to the individual and which are influenced by, or stem from, external factors. Thus, the statistical models adopted in life course research face a number of challenging questions, which in turn can only be met with an appropriate data collection methodology

Episodic memory performance in a multi-ethnic longitudinal study of 13,037 elderly

“…Although there are several methodological approaches used in developmental cognitive neuroscience, growth curve models (GCM) represent a powerful analytical framework to model individual differences in cognitive change over time, as well as the variability of patterns of cognitive change between individuals [ 14 ]. In cognitive neuroscience GCMs have been derived using linear mixed effects model (LMEM) or latent curve models (LCM) [ 1 – 4 , 6 – 11 , 15 – 23 ].…”

“…In cognitive neuroscience GCMs have been derived using linear mixed effects model (LMEM) or latent curve models (LCM) [ 1 – 4 , 6 – 11 , 15 – 23 ]. LCM uses factor analysis and structural equation models for unobserved outcomes [ 14 , 24 ] and are best suited for complex models with straightforward large data structures [ 25 ]. The flexibility of the LCM approach in incorporating variables with high degree of inter-individual variability (i.e.…”

Perte d’autonomie fonctionnelle des personnes âgées : aperçu des trajectoires d’autonomie à partir d’une revue de littérature

“…Parmi les études examinées dans ce travail, seulement 20% ( La présente étude a établi un état des lieux des trajectoires de déclin fonctionnel de l'autonomie fonctionnelle. Les nombreux défis théoriques et méthodologiques de la recherche sur les parcours de vie exigent des outils statistiques souples (GHISLETTA et al 2015). Ainsi les familles de modèles statistiques pour l'analyse de données longitudinales dont l'analyse de trajectoire de classes latentes, la modélisation linéaire hiérarchique, le modèle de mélange de croissance généralisé à effet aléatoire et beaucoup d'autres méthodes sont des modèles statistiques de changement qui ont imprégné de nombreuses disciplines scientifiques (COLLINS 2006) (DUNCAN et al 2013.…”

Occupational Class Differences in Trajectories of Working Conditions in Women

“…However, earlier studies have not found support for an interaction between physical workload and job control [ 39 ]. Multivariate models and more stratified analyses, for example, should be employed to further investigate the relationship between physical working conditions, job control, and health [ 40 ].…”