A Finite Mixture of Nonlinear Random Coefficient Models for Continuous Repeated Measures Data.
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.
Quantitative Methods in Education Program, Department of Educational Psychology, University of Minnesota, 161 Education Sciences Bldg., 56 East River Road, Minneapolis, MN, 55455 , USA. nkohli@umn.edu.
Nidhi
N
Kohli
University of Maryland, College Park, MD, USA.
Jeffrey R
JR
Harring
University of Miami, Coral Gables, FL, USA.
Cengiz
C
Zopluoglu
D006801:Humans; D007206:Individuality; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D011594:Psychometrics; D013647:Task Performance and Analysis
estimation; finite mixture models; nonlinear; random coefficient models