Nonlinear Growth Mixture Models in Research on Cognitive Aging

Fumiaki Hamagami, Kevin J. Grimm, John J. McArdle

Research output: Chapter in Book/Report/Conference proceedingChapter


This chapter provides growth mixture models into a nonlinear framework and examines patterns of cognitive development across the lifespan. It presents the work on nonlinear growth mixture models to include models based on structured curves, growth models based on latent difference scores and related multivariate models. The chapter outlines the latent growth modeling with consideration for nonlinear, multiple group, and multivariate growth models, a short introduction to growth mixture modeling, and an application of nonlinear growth mixture models to lifespan cognitive development data. In the data file the measurements are grouped into common units of time/age. The latent difference score growth curves can model a series of nonlinear shapes and allow for the evaluation of time-dependent influences on the change in the variable of interest. Some of the possibilities in cognitive aging are subpopulations of participants with a differential decline in late adulthood or subpopulations with a stunt in growth during adolescence.

Original languageEnglish (US)
Title of host publicationLongitudinal Models in the Behavioral and Related Sciences
PublisherTaylor and Francis
Number of pages28
ISBN (Electronic)9781351559751
ISBN (Print)9781315091655
StatePublished - Jan 1 2017
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences(all)
  • Medicine(all)
  • Psychology(all)


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