Jump to ContentJump to Main Navigation
Models for Intensive Longitudinal Data$
Users without a subscription are not able to see the full content.

Theodore A. Walls and Joseph L. Schafer

Print publication date: 2006

Print ISBN-13: 9780195173444

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780195173444.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 28 October 2020

A Local Linear Estimation Procedure for Functional Multilevel Modeling

A Local Linear Estimation Procedure for Functional Multilevel Modeling

(p.63) 3 A Local Linear Estimation Procedure for Functional Multilevel Modeling
Models for Intensive Longitudinal Data

Runze Li

Tammy L. Root

Saul Shiffman

Oxford University Press

Linear mixed models, also termed hierarchical linear models (HLM), have been particularly useful for researchers analyzing longitudinal data, but they are not appropriate for all types of longitudinal data. For example, these methods are not able to estimate changes in slope between an outcome variable and potentially time-varying covariates over time. The functional multilevel modeling technique proposed in this chapter addresses this issue by elaborating the linear mixed model to permit coefficients, both random and fixed, to vary nonparametrically over time. Estimation of time-varying coefficients is achieved by adding a local linear regression estimation procedure to the traditional linear mixed model. The main motivation for the current research was methodological challenges faced by drug-use researchers on how to model intensive longitudinal data.

Keywords:   mixed models, hierarchical linear models, longitudinal data, multilevel modeling, linear regression, substance abuse researchers

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .