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

Published online by Cambridge University Press:  01 January 2025

Nidhi Kohli ,
Jeffrey R. Harring  and
Cengiz Zopluoglu
Nidhi Kohli*
Affiliation:
University of Minnesota
Jeffrey R. Harring
Affiliation:
University of Maryland
Cengiz Zopluoglu
Affiliation:
University of Miami
*
Correspondence should be made to Nidhi Kohli, Quantitative Methods in Education Program, Department of Educational Psychology, University of Minnesota, 161 Education Sciences Bldg., 56 East River Road, Minneapolis, MN 55455, USA. Email: nkohli@umn.edu
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Abstract

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.

Keywords

estimationfinite mixture modelsnonlinearrandom coefficient models

Information

Type
Original Paper
Information
Psychometrika , Volume 81 , Issue 3 , September 2016 , pp. 851 - 880
Copyright
Copyright © 2015 The Psychometric Society

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