A Finite Mixture of Nonlinear Random Coefficient Models for Continuous Repeated Measures Data

Nidhi Kohli, Jeffrey R. Harring, Cengiz Zopluoglu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Nonlinear random coefficient models (NRCMs) for continuous longitudinal data are often used for examining individual behaviors that display nonlinear patterns of development (or growth) over time in measured variables. As an extension of this model, this study considers the finite mixture of NRCMs that combine features of NRCMs with the idea of finite mixture (or latent class) models. The efficacy of this model is that it allows the integration of intrinsically nonlinear functions where the data come from a mixture of two or more unobserved subpopulations, thus allowing the simultaneous investigation of intra-individual (within-person) variability, inter-individual (between-person) variability, and subpopulation heterogeneity. Effectiveness of this model to work under real data analytic conditions was examined by executing a Monte Carlo simulation study. The simulation study was carried out using an R routine specifically developed for the purpose of this study. The R routine used maximum likelihood with the expectation–maximization algorithm. The design of the study mimicked the output obtained from running a two-class mixture model on task completion data.

Original languageEnglish (US)
Pages (from-to)851-880
Number of pages30
Issue number3
StatePublished - Sep 1 2016

Bibliographical note

Publisher Copyright:
© 2015, The Psychometric Society.


  • estimation
  • finite mixture models
  • nonlinear
  • random coefficient models


Dive into the research topics of 'A Finite Mixture of Nonlinear Random Coefficient Models for Continuous Repeated Measures Data'. Together they form a unique fingerprint.

Cite this