An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data

Kimberly L. Fine, Hye Won Suk, Kevin Grimm

Research output: Contribution to journalArticle

Abstract

Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999.

Original languageEnglish (US)
JournalMultivariate Behavioral Research
DOIs
StatePublished - Jan 1 2019

Fingerprint

Mixed Effects
Mixed Effects Model
Longitudinal Data
Person
Growth
Modeling
Simulation Study
Trajectory
Growth Curve Model
Growth Curve
Curve
Panel Data
Nonlinear Effects
Parametric Model
Research Personnel
Form
Simulation

Keywords

  • Functional mixed-effects model
  • longitudinal data analysis
  • nonlinear growth curve modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Cite this

An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data. / Fine, Kimberly L.; Suk, Hye Won; Grimm, Kevin.

In: Multivariate Behavioral Research, 01.01.2019.

Research output: Contribution to journalArticle

@article{4789f12414174c7abb577f37b7423897,
title = "An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data",
abstract = "Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999.",
keywords = "Functional mixed-effects model, longitudinal data analysis, nonlinear growth curve modeling",
author = "Fine, {Kimberly L.} and Suk, {Hye Won} and Kevin Grimm",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/00273171.2018.1520626",
language = "English (US)",
journal = "Multivariate Behavioral Research",
issn = "0027-3171",
publisher = "Psychology Press Ltd",

}

TY - JOUR

T1 - An Examination of a Functional Mixed-Effects Modeling Approach to the Analysis of Longitudinal Data

AU - Fine, Kimberly L.

AU - Suk, Hye Won

AU - Grimm, Kevin

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999.

AB - Growth curve modeling is one of the main analytical approaches to study change over time. Growth curve models are commonly estimated in the linear and nonlinear mixed-effects modeling framework in which both the mean and person-specific curves are modeled parametrically with functions of time such as the linear, quadratic, and exponential. However, when more complex nonlinear trajectories need to be estimated and researchers do not have a priori knowledge of an appropriate functional form of growth, parametric models may be too restrictive. This paper reviews functional mixed-effects models, a nonparametric extension of mixed-effects models that permit both the mean and person-specific curves to be estimated without assuming a prespecified functional form of growth. Details of the model are presented along with results from a simulation study and an empirical example. The simulation study showed functional mixed-effects models performed reasonably well under various conditions commonly associated with longitudinal panel data, such as few time points per person, irregularly spaced time points across persons, missingness, and nonlinear trajectories. The usefulness of functional mixed-effects models is illustrated by analyzing empirical data from the Early Childhood Longitudinal Study–Kindergarten Class of 1998–1999.

KW - Functional mixed-effects model

KW - longitudinal data analysis

KW - nonlinear growth curve modeling

UR - http://www.scopus.com/inward/record.url?scp=85063143220&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063143220&partnerID=8YFLogxK

U2 - 10.1080/00273171.2018.1520626

DO - 10.1080/00273171.2018.1520626

M3 - Article

JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

SN - 0027-3171

ER -